datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
joserochabh/jr_dataset_voice | ---
license: creativeml-openrail-m
---
|
liuyanchen1015/MULTI_VALUE_rte_negative_inversion | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
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- name: test
num_bytes: 5440
num_examples: 9
- name: train
num_bytes: 3661
num_examples: 7
download_size: 18249
dataset_size: 9101
---
# Dataset Card for "MULTI_VALUE_rte_negative_inversion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
micklerj/comp-com | ---
license: other
license_name: license
license_link: LICENSE
---
|
hassansh/boolq_n_shot | ---
dataset_info:
features:
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configs:
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data_files:
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path: data/3_shot-*
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path: data/4_shot-*
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path: data/5_shot-*
---
|
stable-bias/prof_images_blip__SD_v1.4_random_seeds | ---
dataset_info:
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download_size: 1284633786
dataset_size: 1231689582.0
---
# Dataset Card for "prof_images_blip__SD_v1.4_random_seeds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
benayas/banking_chatgpt_5pct_v0 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1091301
num_examples: 10003
download_size: 357795
dataset_size: 1091301
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mounikaiiith/Telugu_Clickbait | ---
license: cc-by-4.0
---
Do cite the below reference for using the dataset:
@inproceedings{marreddy2021clickbait,
title={Clickbait Detection in Telugu: Overcoming NLP Challenges in Resource-Poor Languages using Benchmarked Techniques},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
pages={1--8},
year={2021},
organization={IEEE}
} |
vwxyzjn/openhermes-dev__kaist-ai_prometheus-13b-v1.0__1707406405 | ---
dataset_info:
features:
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dtype: 'null'
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dtype: string
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num_examples: 167
download_size: 1723422
dataset_size: 3157775
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
---
|
CyberHarem/shimabara_elena_theidolmstermillionlive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shimabara_elena/島原エレナ (THE iDOLM@STER: Million Live!)
This is the dataset of shimabara_elena/島原エレナ (THE iDOLM@STER: Million Live!), containing 284 images and their tags.
The core tags of this character are `green_hair, long_hair, ahoge, blue_eyes, hairband, bangs, 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 | 284 | 295.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 284 | 197.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 587 | 383.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 284 | 271.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 587 | 508.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shimabara_elena_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/shimabara_elena_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 | 5 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, navel, solo, blunt_bangs, blush, collarbone, cowboy_shot, large_breasts, o-ring_bikini, o-ring_bottom, open_mouth, day, outdoors, white_bikini, :d, arm_up, bare_shoulders, earrings, halterneck, heart, medium_breasts, necklace, o-ring_top, signature, skindentation, standing, stomach, thigh_gap, upper_teeth_only, wading, water, wet |
| 1 | 20 |  |  |  |  |  | open_mouth, 1girl, solo, looking_at_viewer, :d, aqua_eyes, jewelry, blush, navel |
| 2 | 9 |  |  |  |  |  | 1girl, blush, 1boy, hetero, nipples, penis, sex, solo_focus, vaginal, open_mouth, pussy, sweat, completely_nude, medium_breasts, mosaic_censoring, spread_legs, navel, cum, female_pubic_hair, girl_on_top, looking_at_viewer, smile, straddling |
| 3 | 20 |  |  |  |  |  | blush, open_mouth, serafuku, white_shirt, 1girl, long_sleeves, solo, hair_bow, pleated_skirt, blue_skirt, :d, neckerchief, cloud, looking_at_viewer, sky, blue_hairband, very_long_hair, day, outdoors, standing, white_sailor_collar |
| 4 | 5 |  |  |  |  |  | earrings, bare_shoulders, blunt_bangs, looking_at_viewer, medium_breasts, sleeveless_dress, yellow_dress, 2girls, blush, corset, frills, open_mouth, print_dress, smile, solo_focus, standing, 1girl, ;d, aqua_eyes, arm_up, black_gloves, blurry_foreground, brown_hair, choker, cowboy_shot, cross-laced_clothes, depth_of_field, hat_flower, mini_hat, one_eye_closed, orange_dress, parted_lips, pearl_(gemstone), pearl_bracelet, pearl_necklace, petals, simple_background, sparkle, stage, wavy_hair, white_background, wrist_cuffs, yellow_headwear |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | navel | solo | blunt_bangs | blush | collarbone | cowboy_shot | large_breasts | o-ring_bikini | o-ring_bottom | open_mouth | day | outdoors | white_bikini | :d | arm_up | bare_shoulders | earrings | halterneck | heart | medium_breasts | necklace | o-ring_top | signature | skindentation | standing | stomach | thigh_gap | upper_teeth_only | wading | water | wet | aqua_eyes | jewelry | 1boy | hetero | nipples | penis | sex | solo_focus | vaginal | pussy | sweat | completely_nude | mosaic_censoring | spread_legs | cum | female_pubic_hair | girl_on_top | smile | straddling | serafuku | white_shirt | long_sleeves | hair_bow | pleated_skirt | blue_skirt | neckerchief | cloud | sky | blue_hairband | very_long_hair | white_sailor_collar | sleeveless_dress | yellow_dress | 2girls | corset | frills | print_dress | ;d | black_gloves | blurry_foreground | brown_hair | choker | cross-laced_clothes | depth_of_field | hat_flower | mini_hat | one_eye_closed | orange_dress | parted_lips | pearl_(gemstone) | pearl_bracelet | pearl_necklace | petals | simple_background | sparkle | stage | wavy_hair | white_background | wrist_cuffs | yellow_headwear |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:--------|:-------|:--------------|:--------|:-------------|:--------------|:----------------|:----------------|:----------------|:-------------|:------|:-----------|:---------------|:-----|:---------|:-----------------|:-----------|:-------------|:--------|:-----------------|:-----------|:-------------|:------------|:----------------|:-----------|:----------|:------------|:-------------------|:---------|:--------|:------|:------------|:----------|:-------|:---------|:----------|:--------|:------|:-------------|:----------|:--------|:--------|:------------------|:-------------------|:--------------|:------|:--------------------|:--------------|:--------|:-------------|:-----------|:--------------|:---------------|:-----------|:----------------|:-------------|:--------------|:--------|:------|:----------------|:-----------------|:----------------------|:-------------------|:---------------|:---------|:---------|:---------|:--------------|:-----|:---------------|:--------------------|:-------------|:---------|:----------------------|:-----------------|:-------------|:-----------|:-----------------|:---------------|:--------------|:-------------------|:-----------------|:-----------------|:---------|:--------------------|:----------|:--------|:------------|:-------------------|:--------------|:------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 20 |  |  |  |  |  | X | | X | X | X | | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | X | | | X | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 20 |  |  |  |  |  | X | | X | | X | | X | | | | | | X | X | X | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | | | X | X | | X | | | | X | | | | | X | X | X | | | X | | | | | X | | | | | | | X | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Indic-LLM-Labs/Laion-Coco-Kn | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: eng_caption
dtype: string
- name: score
dtype: float64
- name: kn_caption
dtype: string
splits:
- name: test
num_bytes: 5223531
num_examples: 14906
- name: train
num_bytes: 258046154
num_examples: 733604
download_size: 156666204
dataset_size: 263269685
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
license: mit
task_categories:
- visual-question-answering
language:
- kn
- en
size_categories:
- 100K<n<1M
---
[laion-coco](https://huggingface.co/datasets/laion/laion-coco) dataset with captions translated to Kannada language. The dataset contains 733604 training and
14906 test samples. Images can be downloaded directly from Coco page.
### Data Sample:
```python
{'id': 'dde3bdc5-36b7-4340-b2ae-d9564c0d213a',
'url': 'https://i.pinimg.com/236x/ca/84/a1/ca84a1d6f83c88c94452a94e320f024c--lens.jpg',
'eng_caption': 'Black and white photograph of woman in hat leaning against tree.',
'score': 5.8029,
'kn_caption': 'ಮರದ ವಿರುದ್ಧ ಒರಗಿರುವ ಟೋಪಿ ಹೊಂದಿರುವ ಮಹಿಳೆಯ ಕಪ್ಪು ಮತ್ತು ಬಿಳಿ ಛಾಯಾಚಿತ್ರ.'}
```
### Use with Datasets:
```python
from datasets import load_dataset
ds = load_dataset("Indic-LLM-Labs/Laion-Coco-Kn")
``` |
jonathan-roberts1/EuroSAT | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': annual crop
'1': forest
'2': herbaceous vegetation
'3': highway
'4': industrial
'5': pasture
'6': permanent crop
'7': residential
'8': river
'9': sea or lake
splits:
- name: train
num_bytes: 88391109
num_examples: 27000
download_size: 88591771
dataset_size: 88391109
license: mit
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "EuroSAT"
## Dataset Description
- **Paper** [Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf)
- **Paper** [Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf)
- **GitHub** [EuroSAT](https://github.com/phelber/EuroSAT)
- **Data** [Zenodo](https://zenodo.org/record/7711810#.ZCcA9uzMLJx)
### Licensing Information
MIT.
## Citation Information
[Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification](https://ieeexplore.ieee.org/iel7/4609443/8789745/08736785.pdf)
[Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://ieeexplore.ieee.org/iel7/8496405/8517275/08519248.pdf)
```
@article{helber2019eurosat,
title = {Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
author = {Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
year = 2019,
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
publisher = {IEEE}
}
@inproceedings{helber2018introducing,
title = {Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
author = {Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
year = 2018,
booktitle = {IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
pages = {204--207},
organization = {IEEE}
}
``` |
open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish | ---
pretty_name: Evaluation run of malhajar/Mistral-7B-v0.2-meditron-turkish
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [malhajar/Mistral-7B-v0.2-meditron-turkish](https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish)\
\ 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-05T09:37:57.221599](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish/blob/main/results_2024-01-05T09-37-57.221599.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\": 0.60159226527681,\n\
\ \"acc_stderr\": 0.033104690476384036,\n \"acc_norm\": 0.6069622523870655,\n\
\ \"acc_norm_stderr\": 0.03378038316382859,\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6619182579327776,\n\
\ \"mc2_stderr\": 0.014732292169528463\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5546075085324232,\n \"acc_stderr\": 0.01452398763834408,\n\
\ \"acc_norm\": 0.5955631399317406,\n \"acc_norm_stderr\": 0.01434203648343618\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6233817964548894,\n\
\ \"acc_stderr\": 0.004835475957610925,\n \"acc_norm\": 0.8178649671380203,\n\
\ \"acc_norm_stderr\": 0.003851669934633879\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621503,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621503\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\": 0.625,\n \"acc_stderr\": 0.039397364351956274,\n \
\ \"acc_norm\": 0.625,\n \"acc_norm_stderr\": 0.039397364351956274\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n\
\ \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \
\ \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\
\ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\
\ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \"acc_norm\": 0.44,\n\
\ \"acc_norm_stderr\": 0.0498887651569859\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n\
\ \"acc_stderr\": 0.03772446857518026,\n \"acc_norm\": 0.5722543352601156,\n\
\ \"acc_norm_stderr\": 0.03772446857518026\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5361702127659574,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.5361702127659574,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\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\"\
: 0.3862433862433862,\n \"acc_stderr\": 0.025075981767601684,\n \"\
acc_norm\": 0.3862433862433862,\n \"acc_norm_stderr\": 0.025075981767601684\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.04343525428949098,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.04343525428949098\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6935483870967742,\n\
\ \"acc_stderr\": 0.026226485652553883,\n \"acc_norm\": 0.6935483870967742,\n\
\ \"acc_norm_stderr\": 0.026226485652553883\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001974,\n \"acc_norm\"\
: 0.61,\n \"acc_norm_stderr\": 0.04902071300001974\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.03095405547036589,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.03095405547036589\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5487179487179488,\n \"acc_stderr\": 0.025230381238934837,\n\
\ \"acc_norm\": 0.5487179487179488,\n \"acc_norm_stderr\": 0.025230381238934837\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606648,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606648\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787575,\n \"\
acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787575\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"\
acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7549019607843137,\n \"acc_stderr\": 0.03019028245350195,\n \"\
acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.03019028245350195\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159253,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159253\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\
\ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\
\ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.038808483010823944,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.038808483010823944\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8181818181818182,\n \"acc_stderr\": 0.03520893951097653,\n \"\
acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.03520893951097653\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7423312883435583,\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\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.043546310772605935,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.043546310772605935\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.02336505149175371,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.02336505149175371\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7803320561941252,\n\
\ \"acc_stderr\": 0.014805384478371151,\n \"acc_norm\": 0.7803320561941252,\n\
\ \"acc_norm_stderr\": 0.014805384478371151\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.02524826477424284,\n\
\ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.02524826477424284\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2748603351955307,\n\
\ \"acc_stderr\": 0.01493131670322051,\n \"acc_norm\": 0.2748603351955307,\n\
\ \"acc_norm_stderr\": 0.01493131670322051\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.02656892101545715,\n\
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.02656892101545715\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6881028938906752,\n\
\ \"acc_stderr\": 0.026311858071854155,\n \"acc_norm\": 0.6881028938906752,\n\
\ \"acc_norm_stderr\": 0.026311858071854155\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6820987654320988,\n \"acc_stderr\": 0.025910063528240875,\n\
\ \"acc_norm\": 0.6820987654320988,\n \"acc_norm_stderr\": 0.025910063528240875\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \
\ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42959582790091266,\n\
\ \"acc_stderr\": 0.012643004623790205,\n \"acc_norm\": 0.42959582790091266,\n\
\ \"acc_norm_stderr\": 0.012643004623790205\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.02952009569768776,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.02952009569768776\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6225490196078431,\n \"acc_stderr\": 0.01961085147488029,\n \
\ \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.01961085147488029\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.0282638899437846,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.0282638899437846\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7313432835820896,\n\
\ \"acc_stderr\": 0.03134328358208955,\n \"acc_norm\": 0.7313432835820896,\n\
\ \"acc_norm_stderr\": 0.03134328358208955\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.02954774168764004,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.02954774168764004\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168103,\n \"mc2\": 0.6619182579327776,\n\
\ \"mc2_stderr\": 0.014732292169528463\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.01196129890580315\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3593631539044731,\n \
\ \"acc_stderr\": 0.01321645630985154\n }\n}\n```"
repo_url: https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish
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_05T09_36_36.907397
path:
- '**/details_harness|arc:challenge|25_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|arc:challenge|25_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|gsm8k|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|gsm8k|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hellaswag|10_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hellaswag|10_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-36-36.907397.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T09-37-57.221599.parquet'
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- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-37-57.221599.parquet'
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- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-37-57.221599.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-37-57.221599.parquet'
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- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-37-57.221599.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
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- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-36-36.907397.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-36-36.907397.parquet'
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-36-36.907397.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T09-36-36.907397.parquet'
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T09-36-36.907397.parquet'
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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path:
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- split: latest
path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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- split: latest
path:
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data_files:
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path:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T09-37-57.221599.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- '**/details_harness|winogrande|5_2024-01-05T09-36-36.907397.parquet'
- split: 2024_01_05T09_37_57.221599
path:
- '**/details_harness|winogrande|5_2024-01-05T09-37-57.221599.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-05T09-37-57.221599.parquet'
- config_name: results
data_files:
- split: 2024_01_05T09_36_36.907397
path:
- results_2024-01-05T09-36-36.907397.parquet
- split: 2024_01_05T09_37_57.221599
path:
- results_2024-01-05T09-37-57.221599.parquet
- split: latest
path:
- results_2024-01-05T09-37-57.221599.parquet
---
# Dataset Card for Evaluation run of malhajar/Mistral-7B-v0.2-meditron-turkish
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [malhajar/Mistral-7B-v0.2-meditron-turkish](https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-05T09:37:57.221599](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish/blob/main/results_2024-01-05T09-37-57.221599.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": 0.60159226527681,
"acc_stderr": 0.033104690476384036,
"acc_norm": 0.6069622523870655,
"acc_norm_stderr": 0.03378038316382859,
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168103,
"mc2": 0.6619182579327776,
"mc2_stderr": 0.014732292169528463
},
"harness|arc:challenge|25": {
"acc": 0.5546075085324232,
"acc_stderr": 0.01452398763834408,
"acc_norm": 0.5955631399317406,
"acc_norm_stderr": 0.01434203648343618
},
"harness|hellaswag|10": {
"acc": 0.6233817964548894,
"acc_stderr": 0.004835475957610925,
"acc_norm": 0.8178649671380203,
"acc_norm_stderr": 0.003851669934633879
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621503,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621503
},
"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": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.55,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7013888888888888,
"acc_stderr": 0.03827052357950756,
"acc_norm": 0.7013888888888888,
"acc_norm_stderr": 0.03827052357950756
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.0498887651569859,
"acc_norm": 0.44,
"acc_norm_stderr": 0.0498887651569859
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5722543352601156,
"acc_stderr": 0.03772446857518026,
"acc_norm": 0.5722543352601156,
"acc_norm_stderr": 0.03772446857518026
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5361702127659574,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.5361702127659574,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.38596491228070173,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.38596491228070173,
"acc_norm_stderr": 0.04579639422070434
},
"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": 0.3862433862433862,
"acc_stderr": 0.025075981767601684,
"acc_norm": 0.3862433862433862,
"acc_norm_stderr": 0.025075981767601684
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.04343525428949098,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.04343525428949098
},
"harness|hendrycksTest-global_facts|5": {
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
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PJMixers/example-sharegpt | ---
language:
- en
size_categories:
- n<1K
---
CoT items from airoboros 3.2 |
BByrneLab/multi_task_multi_modal_knowledge_retrieval_benchmark_M2KR | ---
language:
- en
license: mit
size_categories:
- 10M<n<100M
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- knowledge-based-visual-question-answering
- Knowledge-retrieval
- passage-retrieval
pretty_name: M2KR
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- name: img_ocr
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sequence: int64
- name: pos_item_ids
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- name: test
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download_size: 107083191
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- config_name: OKVQA_passages
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- name: passage_id
dtype: string
- name: passage_content
dtype: string
- name: title
dtype: string
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num_examples: 114809
- name: test_passages
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num_examples: 114809
download_size: 136470207
dataset_size: 236787348
- config_name: OVEN_data
features:
- name: pos_item_ids
sequence: string
- name: pos_item_contents
sequence: string
- name: img_id
dtype: string
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- name: valid
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- name: test
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- config_name: OVEN_passages
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- name: passage_id
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- name: passage_content
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- name: test_passages
num_bytes: 2647627
num_examples: 3192
download_size: 7283816
dataset_size: 12020425
- config_name: WIT_data
features:
- name: original_data_id
sequence: string
- name: pos_item_ids
sequence: string
- name: pos_item_contents
sequence: string
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- name: test
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- config_name: WIT_passages
features:
- name: language
dtype: string
- name: page_url
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- name: image_url
dtype: string
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dtype: string
- name: image_id
dtype: string
- name: original_data_id
dtype: string
- name: img_id
dtype: string
- name: img_path
dtype: string
- name: image_downloaded
dtype: bool
- name: passage_id
dtype: string
- name: passage_content
dtype: string
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num_examples: 4120010
- name: test_passages
num_bytes: 132381872
num_examples: 39478
download_size: 8424698596
dataset_size: 13683965378
configs:
- config_name: CC_data
data_files:
- split: train
path: CC_data/train-*
- config_name: CC_passages
data_files:
- split: train_passages
path: CC_passages/train_passages-*
- config_name: EVQA_data
data_files:
- split: train
path: EVQA_data/train-*
- split: valid
path: EVQA_data/valid-*
- split: test
path: EVQA_data/test-*
- config_name: EVQA_passages
data_files:
- split: train_passages
path: EVQA_passages/train_passages-*
- split: valid_passages
path: EVQA_passages/valid_passages-*
- split: test_passages
path: EVQA_passages/test_passages-*
- config_name: IGLUE_data
data_files:
- split: test
path: IGLUE_data/test-*
- config_name: IGLUE_passages
data_files:
- split: test_passages
path: IGLUE_passages/test_passages-*
- config_name: Infoseek_data
data_files:
- split: train
path: Infoseek_data/train-*
- split: test
path: Infoseek_data/test-*
- config_name: Infoseek_passages
data_files:
- split: train_passages
path: Infoseek_passages/train_passages-*
- split: test_passages
path: Infoseek_passages/test_passages-*
- config_name: KVQA_data
data_files:
- split: train
path: KVQA_data/train-*
- split: valid
path: KVQA_data/valid-*
- split: test
path: KVQA_data/test-*
- config_name: KVQA_passages
data_files:
- split: valid_passages
path: KVQA_passages/valid_passages-*
- split: train_passages
path: KVQA_passages/train_passages-*
- split: test_passages
path: KVQA_passages/test_passages-*
- config_name: LLaVA_data
data_files:
- split: train
path: LLaVA_data/train-*
- split: test
path: LLaVA_data/test-*
- config_name: LLaVA_passages
data_files:
- split: train_passages
path: LLaVA_passages/train_passages-*
- split: test_passages
path: LLaVA_passages/test_passages-*
- config_name: MSMARCO_data
data_files:
- split: train
path: MSMARCO_data/train-*
- split: valid
path: MSMARCO_data/valid-*
- split: test
path: MSMARCO_data/test-*
- config_name: MSMARCO_passages
data_files:
- split: valid_passages
path: MSMARCO_passages/valid_passages-*
- split: train_passages
path: MSMARCO_passages/train_passages-*
- split: test_passages
path: MSMARCO_passages/test_passages-*
- config_name: OKVQA_data
data_files:
- split: train
path: OKVQA_data/train-*
- split: valid
path: OKVQA_data/valid-*
- split: test
path: OKVQA_data/test-*
- config_name: OKVQA_passages
data_files:
- split: valid_passages
path: OKVQA_passages/valid_passages-*
- split: train_passages
path: OKVQA_passages/train_passages-*
- split: test_passages
path: OKVQA_passages/test_passages-*
- config_name: OVEN_data
data_files:
- split: train
path: OVEN_data/train-*
- split: valid
path: OVEN_data/valid-*
- split: test
path: OVEN_data/test-*
- config_name: OVEN_passages
data_files:
- split: valid_passages
path: OVEN_passages/valid_passages-*
- split: train_passages
path: OVEN_passages/train_passages-*
- split: test_passages
path: OVEN_passages/test_passages-*
- config_name: WIT_data
data_files:
- split: train
path: WIT_data/train-*
- split: valid
path: WIT_data/valid-*
- split: test
path: WIT_data/test-*
- config_name: WIT_passages
data_files:
- split: valid_passages
path: WIT_passages/valid_passages-*
- split: train_passages
path: WIT_passages/train_passages-*
- split: test_passages
path: WIT_passages/test_passages-*
---
# PreFLMR M2KR Dataset Card
## Dataset details
**Dataset type:**
M2KR is a benchmark dataset for multimodal knowledge retrieval. It contains a collection of tasks and datasets for training and evaluating multimodal knowledge retrieval models.
We pre-process the datasets into a uniform format and write several task-specific prompting instructions for each dataset. The details of the instruction can be found in the paper. The M2KR benchmark contains three types of tasks:
#### Image to Text (I2T) retrieval
These tasks evaluate the ability of a retriever to find relevant documents associated with an input image.
Component tasks are WIT, IGLUE-en, KVQA, and CC3M.
#### Question to Text (Q2T) retrieval
This task is based on MSMARCO and is included to assess whether multi-modal retrievers retain their ability in text-only retrieval after any retraining for images.
#### Image & Question to Text (IQ2T) retrieval
This is the most challenging task which requires joint understanding of questions and images for accurate retrieval. It consists of these subtasks:
OVEN, LLaVA, OKVQA, Infoseek and E-VQA.
**Paper or resources for more information:**
- **Paper:** https://arxiv.org/abs/2402.08327
- **Project Page:** https://preflmr.github.io/
- **Huggingface Implementation:** https://github.com/LinWeizheDragon/FLMR
For details on the example usage of the dataset, please see the [M2KR Benchmark Datasets](https://github.com/LinWeizheDragon/FLMR/blob/main/docs/Datasets.md)
**License:**
MIT License
**Where to send questions or comments about the model:**
https://github.com/LinWeizheDragon/FLMR/issues
## Intended use
**Primary intended uses:**
The primary use of M2KR is for pretraining general-purpose multimodal knowledge retrieval models and benchmarking their performance.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
Felipefloke/sasa | ---
license: openrail
---
|
waue0920/testdata | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: deviceId
dtype: int64
- name: PM2.5
dtype: float64
- name: time
dtype: string
- name: lon
dtype: float64
- name: lat
dtype: float64
splits:
- name: train
num_bytes: 600742065
num_examples: 10922583
download_size: 362181683
dataset_size: 600742065
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MaralGPT/persian-wikipedia | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1899154938
num_examples: 979869
download_size: 758970775
dataset_size: 1899154938
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
varun-v-rao/adversarial_hotpotqa | ---
task_categories:
- question-answering
dataset_info:
features:
- name: question
dtype: string
- name: context
dtype: string
- name: id
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 89560671.51114564
num_examples: 33358
- name: validation
num_bytes: 7454710.584712826
num_examples: 2828
download_size: 17859339
dataset_size: 97015382.09585845
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
## Dataset Card for "squad"
This truncated dataset is derived from the Stanford Question Answering Dataset (SQuAD) for reading comprehension. Its primary aim is to extract instances from the original SQuAD dataset that align with the context length of BERT, RoBERTa, OPT, and T5 models.
### Preprocessing and Filtering
Preprocessing involves tokenization using the BertTokenizer (WordPiece), RoBertaTokenizer (Byte-level BPE), OPTTokenizer (Byte-Pair Encoding), and T5Tokenizer (Sentence Piece). Each sample is then checked to ensure that the length of the tokenized input is within the specified model_max_length for each tokenizer.
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-90000 | ---
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: 998552
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Seanxh/twitter_dataset_1713212858 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 176536
num_examples: 413
download_size: 63086
dataset_size: 176536
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mammut/mammut-corpus-venezuela | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es
language_bcp47:
- es-VE
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: mammut-corpus-venezuela
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# mammut-corpus-venezuela
HuggingFace Dataset
## 1. How to use
How to load this dataset directly with the datasets library:
`>>> from datasets import load_dataset`
`>>> dataset = load_dataset("mammut-corpus-venezuela")`
## 2. Dataset Summary
**mammut-corpus-venezuela** is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.
Each record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.
The dataset counts with a train split and a test split.
## 3. Supported Tasks and Leaderboards
This dataset can be used for language modeling.
## 4. Languages
The dataset contains Venezuelan and Latin-American Spanish.
## 5. Dataset Structure
Dataset structure features.
### 5.1 Data Instances
An example from the dataset:
"AUTHOR":"author in title",
"TITLE":"Luis Alberto Buttó: Hecho en socialismo",
"SENTENCE":"Históricamente, siempre fue así.",
"DATE":"2021-07-04 07:18:46.918253",
"SOURCE":"la patilla",
"TOKENS":"4",
"TYPE":"opinion/news",
The average word token count are provided below:
### 5.2 Total of tokens (no spelling marks)
Train: 92,431,194.
Test: 4,876,739 (in another file).
### 5.3 Data Fields
The data have several fields:
AUTHOR: author of the text. It is anonymized for conversation authors.
DATE: date on which the text was entered in the corpus.
SENTENCE: text. It was automatically tokenized for sources other than conversations.
SOURCE: source of the texts.
TITLE: title of the text from which SENTENCE originates.
TOKENS: number of tokens (excluding punctuation marks) of SENTENCE.
TYPE: linguistic register of the text.
### 5.4 Data Splits
The mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:
Number of Instances in Split.
Train: 2,983,302.
Test: 157,011.
## 6. Dataset Creation
### 6.1 Curation Rationale
The purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.
### 6.2 Source Data
**6.2.1 Initial Data Collection and Normalization**
The data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.
The text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.
An arrow parquet file was created.
Text sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), "El atajo más largo" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.
**6.2.2 Who are the source language producers?**
The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.
## 6.3 Annotations
**6.3.1 Annotation process**
At the moment the dataset does not contain any additional annotations.
**6.3.2 Who are the annotators?**
Not applicable.
### 6.4 Personal and Sensitive Information
The data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.
## 7. Considerations for Using the Data
### 7.1 Social Impact of Dataset
The purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.
### 7.2 Discussion of Biases
Most of the content comes from political, economical and sociological opinion articles. Social biases may be present.
### 7.3 Other Known Limitations
(If applicable, description of the other limitations in the data.)
Not applicable.
## 8. Additional Information
### 8.1 Dataset Curators
The data was originally collected by Lino Urdaneta and Miguel Riveros from Mammut.io.
### 8.2 Licensing Information
Not applicable.
### 8.3 Citation Information
Not applicable.
### 8.4 Contributions
Not applicable.
|
tayamaken/myDtataset | ---
license: other
---
|
Estwld/atomic2020-comet-origin | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 64203342
num_examples: 1008254
- name: test
num_bytes: 9404615
num_examples: 143736
- name: validation
num_bytes: 6314227
num_examples: 94614
download_size: 21711502
dataset_size: 79922184
---
# Dataset Card for "atomic2020-comet-origin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aleph-null/thesis | ---
license: unknown
---
|
CyberHarem/ak_15_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ak_15/AK-15/AK-15 (Girls' Frontline)
This is the dataset of ak_15/AK-15/AK-15 (Girls' Frontline), containing 381 images and their tags.
The core tags of this character are `bangs, long_hair, grey_hair, purple_eyes, breasts, braid, hair_over_one_eye`, 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 | 381 | 505.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_15_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 381 | 270.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_15_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 817 | 523.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_15_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 381 | 436.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_15_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 817 | 767.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ak_15_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/ak_15_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, solo, tactical_clothes, assault_rifle, holding_gun, kalashnikov_rifle, black_gloves, looking_at_viewer, closed_mouth, elbow_gloves, navel, pants, simple_background, white_background, standing, mask |
| 1 | 11 |  |  |  |  |  | 1girl, bare_shoulders, simple_background, solo, black_gloves, crop_top, elbow_gloves, looking_at_viewer, white_background, closed_mouth, medium_breasts, black_pants, navel, standing, tactical_clothes, abs, midriff |
| 2 | 12 |  |  |  |  |  | 1girl, solo, smile, upper_body, white_background, looking_at_viewer, simple_background, tactical_clothes, open_mouth, short_hair, black_gloves, jacket, white_hair |
| 3 | 35 |  |  |  |  |  | white_shirt, formal, 1girl, closed_mouth, red_necktie, solo, looking_at_viewer, black_jacket, ponytail, black_pants, business_suit, holding, standing, belt, id_card, collared_shirt, simple_background |
| 4 | 9 |  |  |  |  |  | cleavage, formal, white_jacket, 1girl, bandaged_neck, looking_at_viewer, purple_shirt, short_hair, smile, large_breasts, medium_breasts, medium_hair, office_lady, white_skirt, business_suit, id_card, solo_focus, white_suit, blazer, collarbone, open_mouth, standing, earpiece, simple_background |
| 5 | 8 |  |  |  |  |  | completely_nude, nipples, 1girl, blush, closed_mouth, large_breasts, navel, bar_censor, collarbone, futanari, penis, pussy, solo_focus, 2girls, huge_breasts, looking_at_viewer, one_eye_covered, sex, simple_background, sweat, testicles |
| 6 | 6 |  |  |  |  |  | 1girl, cleavage, collarbone, looking_at_viewer, thighs, closed_mouth, large_breasts, navel, solo, simple_background, sitting, bare_shoulders, white_background, white_bikini |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | tactical_clothes | assault_rifle | holding_gun | kalashnikov_rifle | black_gloves | looking_at_viewer | closed_mouth | elbow_gloves | navel | pants | simple_background | white_background | standing | mask | bare_shoulders | crop_top | medium_breasts | black_pants | abs | midriff | smile | upper_body | open_mouth | short_hair | jacket | white_hair | white_shirt | formal | red_necktie | black_jacket | ponytail | business_suit | holding | belt | id_card | collared_shirt | cleavage | white_jacket | bandaged_neck | purple_shirt | large_breasts | medium_hair | office_lady | white_skirt | solo_focus | white_suit | blazer | collarbone | earpiece | completely_nude | nipples | blush | bar_censor | futanari | penis | pussy | 2girls | huge_breasts | one_eye_covered | sex | sweat | testicles | thighs | sitting | white_bikini |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------------|:----------------|:--------------|:--------------------|:---------------|:--------------------|:---------------|:---------------|:--------|:--------|:--------------------|:-------------------|:-----------|:-------|:-----------------|:-----------|:-----------------|:--------------|:------|:----------|:--------|:-------------|:-------------|:-------------|:---------|:-------------|:--------------|:---------|:--------------|:---------------|:-----------|:----------------|:----------|:-------|:----------|:-----------------|:-----------|:---------------|:----------------|:---------------|:----------------|:--------------|:--------------|:--------------|:-------------|:-------------|:---------|:-------------|:-----------|:------------------|:----------|:--------|:-------------|:-----------|:--------|:--------|:---------|:---------------|:------------------|:------|:--------|:------------|:---------|:----------|:---------------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | | | | X | X | X | X | X | | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | X | X | | | | X | X | | | | | X | X | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 35 |  |  |  |  |  | X | X | | | | | | X | X | | | | X | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 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 | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | | | | | 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 |
|
Birchlabs/openai-prm800k-phase1_test-stepwise-best | ---
license: mit
---
|
huggingartists/bryan-adams | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/bryan-adams"
## 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:** 0.542578 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/2cb27a7f3f50142f45cd18fae968738c.750x750x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/bryan-adams">
<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">Bryan Adams</div>
<a href="https://genius.com/artists/bryan-adams">
<div style="text-align: center; font-size: 14px;">@bryan-adams</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/bryan-adams).
### 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/bryan-adams")
```
## 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|
|------:|---------:|---:|
|456| -| -|
'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/bryan-adams")
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)
|
HOXSEC/csgo-maps | ---
license: mit
task_categories:
- image-classification
pretty_name: Counter Strike Maps
size_categories:
- 1K<n<10K
---
# Counter Strike Map Dataset
This dataset consists of Counter Strike map images along with their corresponding labels and x-y coordinates. The dataset is suitable for image classification tasks and includes the necessary information for each image.
## Dataset Details
- Total Images: [1424]
- Classes: [5]
- Image Size: [1920x1080]
- Format: [png]
## Files
The dataset includes the following files:
- **maps/train/**: This folder contains the Counter Strike map images. The images are named in a consistent format, typically with a prefix or unique identifier followed by the file extension.
- **metadata.csv**: This CSV file contains the annotations for each image in the dataset. It has the following columns:
- `file_name`: The relative or absolute path to the image file.
- `label`: The label or class of the image.
- `x`: The x-coordinate of a specific point of interest within the image.
- `y`: The y-coordinate of the same point of interest within the image.
|
pidakwo/disease_CoNLL | ---
license: afl-3.0
---
|
mystgg/ru-wikipedia | ---
license: mit
---
|
Kaue123456/FoFaoOrivalPessini | ---
license: openrail
---
|
Multimodal-Fatima/Caltech101_not_background_test | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': accordion
'1': airplanes
'2': anchor
'3': ant
'4': background google
'5': barrel
'6': bass
'7': beaver
'8': binocular
'9': bonsai
'10': brain
'11': brontosaurus
'12': buddha
'13': butterfly
'14': camera
'15': cannon
'16': car side
'17': ceiling fan
'18': cellphone
'19': chair
'20': chandelier
'21': cougar body
'22': cougar face
'23': crab
'24': crayfish
'25': crocodile
'26': crocodile head
'27': cup
'28': dalmatian
'29': dollar bill
'30': dolphin
'31': dragonfly
'32': electric guitar
'33': elephant
'34': emu
'35': euphonium
'36': ewer
'37': faces
'38': faces easy
'39': ferry
'40': flamingo
'41': flamingo head
'42': garfield
'43': gerenuk
'44': gramophone
'45': grand piano
'46': hawksbill
'47': headphone
'48': hedgehog
'49': helicopter
'50': ibis
'51': inline skate
'52': joshua tree
'53': kangaroo
'54': ketch
'55': lamp
'56': laptop
'57': leopards
'58': llama
'59': lobster
'60': lotus
'61': mandolin
'62': mayfly
'63': menorah
'64': metronome
'65': minaret
'66': motorbikes
'67': nautilus
'68': octopus
'69': okapi
'70': pagoda
'71': panda
'72': pigeon
'73': pizza
'74': platypus
'75': pyramid
'76': revolver
'77': rhino
'78': rooster
'79': saxophone
'80': schooner
'81': scissors
'82': scorpion
'83': sea horse
'84': snoopy
'85': soccer ball
'86': stapler
'87': starfish
'88': stegosaurus
'89': stop sign
'90': strawberry
'91': sunflower
'92': tick
'93': trilobite
'94': umbrella
'95': watch
'96': water lilly
'97': wheelchair
'98': wild cat
'99': windsor chair
'100': wrench
'101': yin yang
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_opt175b_downstream_tasks_ViT_L_14
sequence: string
splits:
- name: test
num_bytes: 81047146.0
num_examples: 5647
download_size: 78304363
dataset_size: 81047146.0
---
# Dataset Card for "Caltech101_not_background_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
red1xe/code_instructions_7K | ---
license: openrail
---
|
sogeeking/quantized_burgers_vq | ---
dataset_info:
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path: Burgers_Sols_Nu4.0/dev-*
---
|
neenax/explanation_feedback | ---
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
AdapterOcean/med_alpaca_standardized_cluster_0_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 18996843
num_examples: 30744
download_size: 10109698
dataset_size: 18996843
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_0_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atmallen/mmlu_chat_binary | ---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int32
- name: statement
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: validation
num_bytes: 877546
num_examples: 1218
- name: test
num_bytes: 8026608
num_examples: 11526
download_size: 3732071
dataset_size: 8904154
---
# Dataset Card for "mmlu_chat_binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
astha/RNNDecompositionArtifact | ---
license: mit
---
|
glnmario/ECHR | ---
task_categories:
- text-classification
language:
- en
tags:
- legal
size_categories:
- 10K<n<100K
---
This is the **ECHR dataset**, a collection of 11.5K court cases extracted from the public database
of the European Court of Human Rights and further annotated by human experts. The dataset was
published along with [this paper](https://www.aclweb.org/anthology/P19-1424/) (pleae cite it
accordingly!) and can be donwloaded in its original form from [this website](https://archive.org/details/ECHR-ACL2019).
Each instance in this dataset is a court case. Each court case is annotated with the following properties (the columns of the dataframe):
* `partition`: a label indicating dataset partition this court case belongs to ("train", "dev", or "test")
* `itemid`: a code which uniquely identifies this court case
* `languageisocode`: an [ISO code](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) describing the language in which the case is reported
* `respondent`: the ISO code of the party being sued or tried (respondents are nation states)
* `branch`: the branch of the Court dealing with the case, indicating at which stage of the trial a judgement was made (it can be one out of "ADMISSIBILITY", "CHAMBER", "GRANDCHAMBER", "COMMITTEE")
* `date`: the date of the judgement
* `docname`: the title of the court case (for example, "ERIKSON v. ITALY")
* `importance`: an "importance score" from 1 (key case) to 4 (unimportant), denoting a case's contribution in the development of case-law
* `conclusion`: a short summary of the case conclusion (for example, "Inadmissible" or "Violation of Art. 6-1; No violation of Art. 10"
* `judges`: the name of the judges
* `text`: the facts brought to the attention of the Court
* `binary_judgement`: a binary label indicating whether an article or protocol was (1) or wasn't (0) violated
|
open-llm-leaderboard/details_Yukang__Llama-2-7b-longlora-16k-ft | ---
pretty_name: Evaluation run of Yukang/Llama-2-7b-longlora-16k-ft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Yukang/Llama-2-7b-longlora-16k-ft](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_Yukang__Llama-2-7b-longlora-16k-ft\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T16:21:55.250823](https://huggingface.co/datasets/open-llm-leaderboard/details_Yukang__Llama-2-7b-longlora-16k-ft/blob/main/results_2023-12-03T16-21-55.250823.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|arc:challenge|25_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_27T07_10_03.989833
path:
- '**/details_harness|drop|3_2023-10-27T07-10-03.989833.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-27T07-10-03.989833.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_27T07_10_03.989833
path:
- '**/details_harness|gsm8k|5_2023-10-27T07-10-03.989833.parquet'
- split: 2023_12_03T16_21_55.250823
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-21-55.250823.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T16-21-55.250823.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hellaswag|10_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-08-49.738155.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T13-08-49.738155.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T13-08-49.738155.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_27T07_10_03.989833
path:
- '**/details_harness|winogrande|5_2023-10-27T07-10-03.989833.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-27T07-10-03.989833.parquet'
- config_name: results
data_files:
- split: 2023_10_10T13_08_49.738155
path:
- results_2023-10-10T13-08-49.738155.parquet
- split: 2023_10_27T07_10_03.989833
path:
- results_2023-10-27T07-10-03.989833.parquet
- split: 2023_12_03T16_21_55.250823
path:
- results_2023-12-03T16-21-55.250823.parquet
- split: latest
path:
- results_2023-12-03T16-21-55.250823.parquet
---
# Dataset Card for Evaluation run of Yukang/Llama-2-7b-longlora-16k-ft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft
- **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 [Yukang/Llama-2-7b-longlora-16k-ft](https://huggingface.co/Yukang/Llama-2-7b-longlora-16k-ft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_Yukang__Llama-2-7b-longlora-16k-ft",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T16:21:55.250823](https://huggingface.co/datasets/open-llm-leaderboard/details_Yukang__Llama-2-7b-longlora-16k-ft/blob/main/results_2023-12-03T16-21-55.250823.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
KenDoStudio/ITZY_Ryujin | ---
license: mit
---
|
tatsu-lab/alpaca_farm | ---
license: cc-by-nc-4.0
--- |
Sentdex/SkunkData-001 | ---
license: apache-2.0
---
|
FelipeBandeiraPoatek/evaluation | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 234024421
num_examples: 425
- name: test
num_bytes: 14512665
num_examples: 26
- name: validation
num_bytes: 27661738
num_examples: 50
download_size: 197512750
dataset_size: 276198824
license: mit
task_categories:
- feature-extraction
language:
- en
pretty_name: Sparrow Invoice Dataset
size_categories:
- n<1K
---
# Dataset Card for Invoices (Sparrow)
This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.
Annotation and data preparation task was done by [Katana ML](https://www.katanaml.io) team.
[Sparrow](https://github.com/katanaml/sparrow/tree/main) - open-source data extraction solution by Katana ML.
Original dataset [info](https://data.mendeley.com/datasets/tnj49gpmtz): Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2 |
ibranze/araproje_arc_tr_conf_mgpt_nearestscore_true_y | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: validation
num_bytes: 86423.0
num_examples: 250
download_size: 50809
dataset_size: 86423.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_arc_tr_conf_mgpt_nearestscore_true_y"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edbeeching/prj_gia_dataset_mujoco_reacher_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 mujoco_reacher environment, sample for the policy mujoco_reacher_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_173 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1018522208.0
num_examples: 200024
download_size: 1039385445
dataset_size: 1018522208.0
---
# Dataset Card for "chunk_173"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ciempiess/ciempiess_balance | ---
annotations_creators:
- expert-generated
language:
- es
language_creators:
- other
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'CIEMPIESS BALANCE CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.'
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- ciempiess
- spanish
- mexican spanish
- ciempiess project
- ciempiess-unam project
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for ciempiess_balance
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-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:** [CIEMPIESS-UNAM Project](https://ciempiess.org/)
- **Repository:** [CIEMPIESS BALANCE at LDC](https://catalog.ldc.upenn.edu/LDC2018S11)
- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org)
### Dataset Summary
The CIEMPIESS BALANCE Corpus is designed to match with the [CIEMPIESS LIGHT](https://huggingface.co/datasets/ciempiess/ciempiess_light) Corpus [(LDC2017S23)](https://catalog.ldc.upenn.edu/LDC2017S23). So, "Balance" means that if the CIEMPIESS BALANCE is combined with the CIEMPIESS LIGHT, one will get a gender balanced corpus. To appreciate this, one need to know that the CIEMPIESS LIGHT is by itself, a gender unbalanced corpus of approximately 25% of female speakers and 75% of male speakers. So, the CIEMPIESS BALANCE is a gender unbalanced corpus with approximately 25% of male speakers and 75% of female speakers.
Furthermore, the match between the two datasets is more profound than just the number of the speakers. In both corpus speakers are numbered as: F_01, M_01, F_02, M_02, etc. So, the relation between the speakers is that the speech of F_01 in CIEMPIES LIGHT has an approximate amount of time as the speech of M_01 in the CIEMPIESS BALANCE.
The consequence of this speaker-to-speaker match is that the CIEMPIESS BALANCE has a size of 18 hours and 20 minutes against the 18 hours and 25 minutes of the CIEMPIESS LIGHT. It is a very good match between them!
CIEMPIESS is the acronym for:
"Corpus de Investigación en Español de México del Posgrado de Ingeniería Eléctrica y Servicio Social".
### Example Usage
The CIEMPIESS BALANCE contains only the train split:
```python
from datasets import load_dataset
ciempiess_balance = load_dataset("ciempiess/ciempiess_balance")
```
It is also valid to do:
```python
from datasets import load_dataset
ciempiess_balance = load_dataset("ciempiess/ciempiess_balance",split="train")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The language of the corpus is Spanish with the accent of Central Mexico.
## Dataset Structure
### Data Instances
```python
{
'audio_id': 'CMPB_F_41_01CAR_00011',
'audio': {
'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/6564823bd50fe590ce15086c22ddf7efe2302a8f988f12469f61940f2b88c051/train/female/F_41/CMPB_F_41_01CAR_00011.flac',
'array': array([0.00283813, 0.00442505, 0.00720215, ..., 0.00543213, 0.00570679,
0.00952148], dtype=float32), 'sampling_rate': 16000
},
'speaker_id': 'F_41',
'gender': 'female',
'duration': 7.519000053405762,
'normalized_text': 'entonces mira oye pasa esto y tú así de ay pues déjame leer porque ni sé no así pasa porque pues'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription
### Data Splits
The corpus counts just with the train split which has a total of 8555 speech files from 53 female speakers and 34 male speakers with a total duration of 18 hours and 20 minutes.
## Dataset Creation
### Curation Rationale
The CIEMPIESS BALANCE (CB) Corpus has the following characteristics:
* The CB has a total of 8555 audio files of 53 female speakers and 34 male speakers. It has a total duration of 18 hours and 20 minutes.
* The total number of audio files that come from male speakers is 2447 with a total duration of 5 hours and 40 minutes. The total number of audio files that come from female speakers is 6108 with a total duration of 12 hours and 40 minutes.
* Every audio file in the CB has a duration between 5 and 10 seconds approximately.
* Speakers in the CB and the CIEMPIESS LIGHT (CL) are different persons. In fact, speakers in the CB are not present in any other CIEMPIESS dataset.
* The CL is slightly bigger (18 hours / 25 minutes) than the CB (18 hours / 20 minutes).
* Data in CB is classified by gender and also by speaker, so one can easily select audios from a particular set of speakers to do experiments.
* Audio files in the CL and the CB are all of the same type. In both, speakers talk about legal and lawyer issues. They also talk about things related to the [UNAM University](https://www.unam.mx/) and the ["Facultad de Derecho de la UNAM"](https://www.derecho.unam.mx/).
* As in the CL, transcriptions in the CB were made by humans.
* Audio files in the CB are distributed in a 16khz@16bit mono format.
### Source Data
#### Initial Data Collection and Normalization
The CIEMPIESS BALANCE is a Radio Corpus designed to train acoustic models of automatic speech recognition and it is made out of recordings of spontaneous conversations in Spanish between a radio moderator and his guests. Most of the speech in these conversations has the accent of Central Mexico.
All the recordings that constitute the CIEMPIESS BALANCE come from [RADIO-IUS](https://www.derecho.unam.mx/cultura-juridica/radio.php), a radio station belonging to [UNAM](https://www.unam.mx/). Recordings were donated by Lic. Cesar Gabriel Alanis Merchand and Mtro. Ricardo Rojas Arevalo from the [Facultad de Derecho de la UNAM](https://www.derecho.unam.mx/) with the condition that they have to be used for academic and research purposes only.
### Annotations
#### Annotation process
The annotation process is at follows:
* 1. A whole podcast is manually segmented keeping just the portions containing good quality speech.
* 2. A second pass os segmentation is performed; this time to separate speakers and put them in different folders.
* 3. The resulting speech files between 5 and 10 seconds are transcribed by students from different departments (computing, engineering, linguistics). Most of them are native speakers but not with a particular training as transcribers.
#### Who are the annotators?
The CIEMPIESS BALANCE Corpus was created by the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) of the ["Facultad de Ingeniería"](https://www.ingenieria.unam.mx/) (FI) in the ["Universidad Nacional Autónoma de México"](https://www.unam.mx/) (UNAM) between 2016 and 2018 by Carlos Daniel Hernández Mena, head of the program.
### Personal and Sensitive Information
The dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from publicly available podcasts, so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is valuable because it contains spontaneous speech.
### Discussion of Biases
The dataset is not gender balanced. It is comprised of 53 female speakers and 34 male speakers and the vocabulary is limited to legal issues.
### Other Known Limitations
"CIEMPIESS BALANCE CORPUS" by Carlos Daniel Hernández Mena is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
### Dataset Curators
The dataset was collected by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html). It was curated by [Carlos Daniel Hernández Mena](https://huggingface.co/carlosdanielhernandezmena) in 2018.
### Licensing Information
[CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@misc{carlosmenaciempiessbalance2018,
title={CIEMPIESS BALANCE CORPUS: Audio and Transcripts of Mexican Spanish Broadcast Conversations.},
ldc_catalog_no={LDC2018S11},
DOI={https://doi.org/10.35111/rfmw-n126},
author={Hernandez Mena, Carlos Daniel},
journal={Linguistic Data Consortium, Philadelphia},
year={2018},
url={https://catalog.ldc.upenn.edu/LDC2018S11},
}
```
### Contributions
The authors want to thank to Alejandro V. Mena, Elena Vera and Angélica Gutiérrez for their support to the social service program: "Desarrollo de Tecnologías del Habla." We also thank to the social service students for all the hard work.
|
irds/wikiclir_ar | ---
pretty_name: '`wikiclir/ar`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/ar`
The `wikiclir/ar` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/ar).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=535,118
- `queries` (i.e., topics); count=324,489
- `qrels`: (relevance assessments); count=519,269
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_ar', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_ar', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_ar', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-7B-Uncensored-HF | ---
pretty_name: Evaluation run of TheBloke/Wizard-Vicuna-7B-Uncensored-HF
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Wizard-Vicuna-7B-Uncensored-HF](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF)\
\ 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_TheBloke__Wizard-Vicuna-7B-Uncensored-HF\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-22T23:25:47.452800](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-7B-Uncensored-HF/blob/main/results_2023-10-22T23-25-47.452800.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\": 0.18036912751677853,\n\
\ \"em_stderr\": 0.003937584689736024,\n \"f1\": 0.23801803691275183,\n\
\ \"f1_stderr\": 0.003988701736112215,\n \"acc\": 0.3838336904677134,\n\
\ \"acc_stderr\": 0.009164287920296908\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.18036912751677853,\n \"em_stderr\": 0.003937584689736024,\n\
\ \"f1\": 0.23801803691275183,\n \"f1_stderr\": 0.003988701736112215\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.045489006823351025,\n \
\ \"acc_stderr\": 0.005739657656722215\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871601\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_22T23_25_47.452800
path:
- '**/details_harness|drop|3_2023-10-22T23-25-47.452800.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-22T23-25-47.452800.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_22T23_25_47.452800
path:
- '**/details_harness|gsm8k|5_2023-10-22T23-25-47.452800.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-22T23-25-47.452800.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:11:01.220046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:11:01.220046.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:11:01.220046.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_22T23_25_47.452800
path:
- '**/details_harness|winogrande|5_2023-10-22T23-25-47.452800.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-22T23-25-47.452800.parquet'
- config_name: results
data_files:
- split: 2023_07_19T17_11_01.220046
path:
- results_2023-07-19T17:11:01.220046.parquet
- split: 2023_10_22T23_25_47.452800
path:
- results_2023-10-22T23-25-47.452800.parquet
- split: latest
path:
- results_2023-10-22T23-25-47.452800.parquet
---
# Dataset Card for Evaluation run of TheBloke/Wizard-Vicuna-7B-Uncensored-HF
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Wizard-Vicuna-7B-Uncensored-HF](https://huggingface.co/TheBloke/Wizard-Vicuna-7B-Uncensored-HF) 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_TheBloke__Wizard-Vicuna-7B-Uncensored-HF",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T23:25:47.452800](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Wizard-Vicuna-7B-Uncensored-HF/blob/main/results_2023-10-22T23-25-47.452800.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": 0.18036912751677853,
"em_stderr": 0.003937584689736024,
"f1": 0.23801803691275183,
"f1_stderr": 0.003988701736112215,
"acc": 0.3838336904677134,
"acc_stderr": 0.009164287920296908
},
"harness|drop|3": {
"em": 0.18036912751677853,
"em_stderr": 0.003937584689736024,
"f1": 0.23801803691275183,
"f1_stderr": 0.003988701736112215
},
"harness|gsm8k|5": {
"acc": 0.045489006823351025,
"acc_stderr": 0.005739657656722215
},
"harness|winogrande|5": {
"acc": 0.7221783741120757,
"acc_stderr": 0.012588918183871601
}
}
```
### 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] |
llm4fun/vhac-v1.0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: translated
dtype: bool
- name: output_len
dtype: int64
- name: source
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 327564182
num_examples: 100000
download_size: 157597355
dataset_size: 327564182
---
# Dataset Card for "vhac-v1.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fathyshalab/massive_weather | ---
dataset_info:
features:
- name: id
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 30514
num_examples: 573
- name: validation
num_bytes: 6972
num_examples: 126
- name: test
num_bytes: 8504
num_examples: 156
download_size: 25707
dataset_size: 45990
---
# Dataset Card for "massive_weather"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_macroeconomics | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 4117
num_examples: 5
- name: test
num_bytes: 1391944
num_examples: 390
download_size: 141522
dataset_size: 1396061
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-high_school_macroeconomics"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jxie/esol | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: float64
splits:
- name: train_0
num_bytes: 31089
num_examples: 902
- name: val_0
num_bytes: 3828
num_examples: 113
- name: test_0
num_bytes: 4016
num_examples: 113
- name: train_1
num_bytes: 31354
num_examples: 902
- name: val_1
num_bytes: 3731
num_examples: 113
- name: test_1
num_bytes: 3848
num_examples: 113
- name: train_2
num_bytes: 31095
num_examples: 902
- name: val_2
num_bytes: 3869
num_examples: 113
- name: test_2
num_bytes: 3969
num_examples: 113
download_size: 75468
dataset_size: 116799
---
# Dataset Card for "esol"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PredatorAI/DBUIUX | ---
license: gpl-3.0
---
|
datasets-examples/doc-image-4 | ---
size_categories:
- n<1K
---
# [doc] image dataset 4
This dataset contains 4 jpeg files in the `train/` subdirectory, along with a `metadata.csv` file that provides the data for other columns. |
Dhairya/trial-tweets | ---
dataset_info:
features:
- name: date
dtype: string
- name: content
dtype: string
- name: username
dtype: string
- name: media
dtype: string
- name: inferred company
dtype: string
- name: bytes
dtype: image
- name: likes
dtype: int64
splits:
- name: train
num_bytes: 166890852
num_examples: 240000
dataset_name: 'trial-tweets'
---
# Dataset Card for "trial-tweets"
sample dataset of length 240000 |
LAHASH/weatherandnews | ---
license: unknown
---
|
systemk/c4-toxic-eval | ---
dataset_info:
- config_name: balanced
features:
- name: text
dtype: string
- name: toxic
dtype: bool
- name: hate
dtype: bool
- name: harassment
dtype: bool
- name: self-harm
dtype: bool
- name: sexual
dtype: bool
- name: violence
dtype: bool
- name: sexual/minors
dtype: bool
- name: hate/threatening
dtype: bool
- name: violence/graphic
dtype: bool
- name: self-harm/intent
dtype: bool
- name: self-harm/instructions
dtype: bool
- name: harassment/threatening
dtype: bool
splits:
- name: train
num_bytes: 13545733.26234375
num_examples: 1404
- name: test
num_bytes: 1505081.47359375
num_examples: 156
download_size: 7146035
dataset_size: 15050814.735937499
- config_name: default
features:
- name: text
dtype: string
- name: toxic
dtype: bool
- name: hate
dtype: bool
- name: harassment
dtype: bool
- name: self-harm
dtype: bool
- name: sexual
dtype: bool
- name: violence
dtype: bool
- name: sexual/minors
dtype: bool
- name: hate/threatening
dtype: bool
- name: violence/graphic
dtype: bool
- name: self-harm/intent
dtype: bool
- name: self-harm/instructions
dtype: bool
- name: harassment/threatening
dtype: bool
splits:
- name: train
num_bytes: 493975458
num_examples: 51200
download_size: 258423078
dataset_size: 493975458
configs:
- config_name: balanced
data_files:
- split: train
path: balanced/train-*
- split: test
path: balanced/test-*
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/med_alpaca_standardized_cluster_92 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 113683452
num_examples: 11884
download_size: 32404470
dataset_size: 113683452
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_92"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-abstract_algebra-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 4909
num_examples: 5
- name: test
num_bytes: 196242
num_examples: 100
download_size: 11253
dataset_size: 201151
---
# Dataset Card for "mmlu-abstract_algebra-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
smart-dashcam/motorcycle-accident-driving-datasets | ---
license: other
task_categories:
- video-classification
language:
- en
tags:
- accident
- crash
- motorcycle
dataset_info:
features:
- name: filename
dtype: string
- name: case
dtype: string
- name: duration
dtype: float
---
# Dataset Summary
The dataset consisted of 2 types of cases; accident and driving while riding a motorcycle. 68 accident cases and 68 driving cases are prepared. 30 fps and 852x480 by default. It might be helpful when you train a model to infer whether a video is a motorcycle crash or not. One thing you should know about is 'driving videos' are not typically motorcycle driving. Most 'driving videos' are dashcams in the car. However, all the videos about accidents are motorcycle traffic accidents.
|
SanjanaPedada/SanjanaPedada | ---
dataset_info:
features:
- name: input
sequence: string
- name: output
sequence: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 8235326
num_examples: 67
download_size: 1769745
dataset_size: 8235326
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-11000 | ---
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: 674399
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
awinml/costco_long_practice | ---
license: mit
---
|
AabirDey/job-queries-and-customer-service | ---
license: mit
---
|
domenicrosati/QA2D | ---
annotations_creators:
- machine-generated
- crowdsourced
- found
language_creators:
- machine-generated
- crowdsourced
language: []
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended|squad
- extended|race
- extended|newsqa
- extended|qamr
- extended|movieQA
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: QA2D
---
# Dataset Card for QA2D
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://worksheets.codalab.org/worksheets/0xd4ebc52cebb84130a07cbfe81597aaf0/
- **Repository:** https://github.com/kelvinguu/qanli
- **Paper:** https://arxiv.org/abs/1809.02922
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.
This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
en
## Dataset Structure
### Data Instances
See below.
### Data Fields
- `dataset`: lowercased name of dataset (movieqa, newsqa, qamr, race, squad)
- `example_uid`: unique id of example within dataset (there are examples with the same uids from different datasets, so the combination of dataset + example_uid should be used for unique indexing)
- `question`: tokenized (space-separated) question from the source QA dataset
- `answer`: tokenized (space-separated) answer span from the source QA dataset
- `turker_answer`: tokenized (space-separated) answer sentence collected from MTurk
- `rule-based`: tokenized (space-separated) answer sentence, generated by the rule-based model
### Data Splits
| Dataset Split | Number of Instances in Split |
| ------------- |----------------------------- |
| Train | 60,710 |
| Dev | 10,344 |
## Dataset Creation
### Curation Rationale
This Question to Declarative Sentence (QA2D) Dataset contains 86k question-answer pairs and their manual transformation into declarative sentences. 95% of question answer pairs come from SQuAD (Rajkupar et al., 2016) and the remaining 5% come from four other question answering datasets.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
@article{DBLP:journals/corr/abs-1809-02922,
author = {Dorottya Demszky and
Kelvin Guu and
Percy Liang},
title = {Transforming Question Answering Datasets Into Natural Language Inference
Datasets},
journal = {CoRR},
volume = {abs/1809.02922},
year = {2018},
url = {http://arxiv.org/abs/1809.02922},
eprinttype = {arXiv},
eprint = {1809.02922},
timestamp = {Fri, 05 Oct 2018 11:34:52 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1809-02922.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} |
TFMUNIR/users-movies-ratings-28082023 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Película
dtype: string
- name: Año de la película
dtype: int64
- name: Texto 1
dtype: string
- name: Texto 2
dtype: string
- name: Texto 3
dtype: string
- name: Edad
dtype: int64
- name: Calificación
dtype: string
- name: Fecha
dtype: string
- name: Emoción texto 1
dtype: string
- name: Emoción texto 2
dtype: string
- name: Emoción texto 3
dtype: string
- name: Promedio emociones textos
dtype: string
- name: Suma promedio emociones textos
dtype: float64
- name: Emociones equilibradas
dtype: string
- name: Suma emociones equilibradas
dtype: float64
- name: Emociones películas
dtype: string
- name: Suma emociones películas
dtype: float64
- name: Score de recomendaciones
dtype: float64
- name: Emoción dominante textos
dtype: float64
- name: Emoción dominante equilibradas
dtype: float64
- name: Emoción dominante películas
dtype: float64
splits:
- name: train
num_bytes: 199996
num_examples: 188
download_size: 56295
dataset_size: 199996
---
# Dataset Card for "users-movies-qualifications-28082023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kashif/nectar_dpo_pairs | ---
license: cc-by-nc-4.0
language:
- en
size_categories:
- 100K<n<1M
datasets:
- berkeley-nest/Nectar
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 8651355540
num_examples: 3842034
download_size: 911865387
dataset_size: 8651355540
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- RLHF
- RLAIF
- reward model
---
# Dataset Card for Nectar DPO Pairs
|
Sleoruiz/discursos-completos-etiquetados | ---
dataset_info:
features:
- name: text
dtype: string
- name: name
dtype: string
- name: comision
dtype: string
- name: gaceta_numero
dtype: string
- name: fecha_gaceta
dtype: string
- name: labels
sequence: string
- name: idx
dtype: int64
splits:
- name: train
num_bytes: 184776887
num_examples: 94501
download_size: 99391198
dataset_size: 184776887
---
# Dataset Card for "discursos-completos-etiquetados"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MLRS/masri_test | ---
annotations_creators:
- expert-generated
language:
- mt
language_creators:
- other
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: >-
MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the
YouTube channel of the University of Malta.
size_categories:
- n<1K
source_datasets:
- original
tags:
- masri
- maltese
- masri-project
- malta
- test corpus
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for masri_test
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-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:** [MASRI Project](https://www.um.edu.mt/projects/masri/)
- **Repository:** [MASRI Data Repo](https://github.com/UMSpeech/)
- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org), [Andrea De Marco](mailto:andrea.demarco@um.edu.mt), [Claudia Borg](mailto:claudia.borg@um.edu.mt)
### Dataset Summary
The MASRI-TEST CORPUS was created out of YouTube videos belonging to the channel of the [University of Malta](www.youtube.com/user/universityofmalta). It has a length of 1 hour and it is gender balanced, as it has the same number of male and female speakers.
### Example Usage
The MASRI-TEST contains only the test split:
```python
from datasets import load_dataset
masri_test = load_dataset("MLRS/masri_test")
```
It is also valid to do:
```python
from datasets import load_dataset
masri_test = load_dataset("MLRS/masri_test",split="test")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to test a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The language of the corpus is Maltese.
## Dataset Structure
### Data Instances
```python
{
'audio_id': 'MSRTS_M_17_TS_00001',
'audio': {
'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/9158ecbeeb3532038f3fe3d53e0adda1f790c9363a613bac32c454a39d9c682c/test/male/M_17/MSRTS_M_17_TS_00001.flac',
'array': array([ 0.0020752 , 0.00283813, 0.00167847, ..., -0.0010376 ,
-0.00091553, -0.00100708], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': 'M_17',
'gender': 'male',
'duration': 5.920000076293945,
'normalized_text': 'ignazio saverio mifsud kien qed jippjana kien qed iħejji tliet volumi tal-biblijoteka maltese'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription
### Data Splits
The corpus counts just with the test split which has a total of 668 speech files from 17 male speakers and 17 female speakers with a total duration of 1 hour.
## Dataset Creation
### Curation Rationale
The MASRI-TEST CORPUS (MTSC) has the following characteristics:
* The MTSC has an exact duration of 1 hours and 0 minutes. It has 668 audio files.
* The MTSC has recordings from 34 different speakers: 17 men and 17 women.
* Data in MTSC is classified by speaker. Therefore, all the recordings of each individual speaker are stored in one single directory.
* Data is also classified according to the gender (male/female) of the speakers.
* Every audio file in the MTSC has a duration between 3 and 10 seconds approximately.
* Audio files in the MTSC are distributed in a 16khz@16bit mono format.
* Transcriptions in MTSC are in lowercase. No punctuation marks are permitted except for dashes (-) and apostrophes (') due to their importance in Maltese orthography.
### Source Data
#### Initial Data Collection and Normalization
The MASRI-TEST CORPUS was possible due to a collaboration of two different Universities. The data selection and audio segmentation was performed by the [CIEMPIESS-UNAM Project](http://www.ciempiess.org/) at the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/) in Mexico City. The audio transcription and corpus edition was performed by the [MASRI Team](https://www.um.edu.mt/projects/masri/) at the [University of Malta](https://www.um.edu.mt/) in the Msida Campus.
### Annotations
#### Annotation process
Proper nouns and other words pronounced in languages other than Maltese (mainly from English, Italian, French and German) were transcribed in their respective orthographic system.
#### Who are the annotators?
The audio transcription was performed by expert native speakers at the [University of Malta](https://www.um.edu.mt/) in the Msida Campus.
### Personal and Sensitive Information
The dataset could contain names revealing the identity of some speakers; on the other side, the recordings come from a publicly repository (YouTube), so, there is not a real intent of the participants to be anonymized. Anyway, you agree to not attempt to determine the identity of speakers in this dataset.
**Notice:** Should you consider that our data contains material that is owned by you and should therefore not be reproduced here?, please:
* Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
* Send the request to [Carlos Mena](mailto:carlos.mena@ciempiess.org)
Take down: We will comply to legitimate requests by removing the affected sources from the corpus.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is challenging because it contains spontaneous speech; so, it will be helpful for the ASR community to evaluate their acoustic models in Maltese with it.
### Discussion of Biases
The dataset intents to be gender balanced. It is comprised of 17 male speakers and 17 female speakers.
### Other Known Limitations
Neither the MASRI Team or the CIEMPIESS-UNAM Project guarantee the accuracy of this corpus, nor its suitability for any specific purpose. As a matter of fact, a number of errors, omissions and inconsistencies are expected to be found within the corpus.
### Dataset Curators
The audio recordings were collected and segmented by students belonging to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html), it was curated by Carlos Daniel Hernández Mena and its transcriptions were manually performed by Ayrton-Didier Brincat during 2020.
### Licensing Information
[CC-BY-NC-SA-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). The copyright remains with the original owners of the video.
As the data is taken from YouTube, we invoke the same argument of "fair use" as in the [Voxlingua107](http://bark.phon.ioc.ee/voxlingua107/) dataset, which is:
**"While YouTube users own the copyright to their own videos, using the audio in the videos for training speech recognition models has very limited and transformative purpose and qualifies thus as "fair use" of copyrighted materials. YouTube’s terms of service forbid downloading, storing and distribution of videos. However, the aim of this rule is clearly to forbid unfair monetization of the content by third-party sites and applications. Our dataset contains the videos in segmented audio-only form that makes the monetization of the actual distributed content extremely difficult."**
### Citation Information
```
@misc{carlosmenamasritest2020,
title={MASRI-TEST CORPUS: Audio and Transcriptions in Maltese extracted from the YouTube channel of the University of Malta.},
author={Hernandez Mena, Carlos Daniel and Brincat, Ayrton-Didier and Gatt, Albert and DeMarco, Andrea and Borg, Claudia and van der Plas, Lonneke and Meza Ruiz, Iván Vladimir},
journal={MASRI Project, Malta},
year={2020},
url={https://huggingface.co/datasets/MLRS/masri_test},
}
```
### Contributions
The authors would like to thank to Alberto Templos Carbajal, Elena Vera and Angélica Gutiérrez for their support to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the ["Facultad de Ingeniería (FI)"](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). We also thank to the social service students for all the hard work during the audio segmentation. |
nyanko7/yandere-images | ---
license: openrail
---
yande.re sampled images 2019-2022
Estimated 500k, including metadata(`.json`) and tags(`.txt`) |
communityai/HuggingFaceH4___OpenHermes-2.5-preferences-v0-deduped-200k | ---
dataset_info:
features:
- name: source
dtype: string
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 393093482.7736979
num_examples: 200000
download_size: 197145960
dataset_size: 393093482.7736979
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
joey234/mmlu-conceptual_physics-dev | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: dev
num_bytes: 2298
num_examples: 5
download_size: 0
dataset_size: 2298
---
# Dataset Card for "mmlu-conceptual_physics-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
israelfama/semeval2007_task_14 | ---
license: unknown
---
|
hcho22/code_instructions_120k_alpaca_filtered | ---
license: apache-2.0
---
|
JasiekKaczmarczyk/giant-midi-sustain-masked | ---
dataset_info:
features:
- name: midi_filename
dtype: string
- name: source
dtype: string
- name: pitch
sequence: int16
length: 128
- name: start
sequence: float32
length: 128
- name: dstart
sequence: float32
length: 128
- name: duration
sequence: float32
length: 128
- name: velocity
sequence: int16
length: 128
- name: masking_spaces
struct:
- name: <Random Mask>
sequence: bool
length: 128
- name: <LH Mask>
sequence: bool
length: 128
- name: <RH Mask>
sequence: bool
length: 128
- name: <Harmonic Root Mask>
sequence: bool
length: 128
- name: <Harmonic Outliers Mask>
sequence: bool
length: 128
splits:
- name: train
num_bytes: 574785389
num_examples: 238926
- name: validation
num_bytes: 68225196
num_examples: 28367
- name: test
num_bytes: 71425664
num_examples: 29707
download_size: 305011106
dataset_size: 714436249
---
# Dataset Card for "giant-midi-sustain-masked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Cometyang/SQLData | ---
license: apache-2.0
---
|
if001/oscar_2023_filtered | ---
language:
- ja
license: cc0-1.0
task_categories:
- text-generation
dataset_info:
features:
- name: text
dtype: string
---
```
from datasets import load_dataset
ds=load_dataset("if001/oscar_2023_filtered")
ds['train']
---
Dataset({
features: ['text'],
num_rows: 312396
})
```
oscar 2023をfilterしたもの
https://huggingface.co/datasets/oscar-corpus/OSCAR-2301
詳細はコードを参照
https://github.com/if001/HojiChar_OSCAR_sample/tree/0.0.4 |
HelloKattyz/NveeBYHKattyz | ---
license: openrail
---
|
EgilKarlsen/CSIC_GPT2_Finetuned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
- name: '3'
dtype: float32
- name: '4'
dtype: float32
- name: '5'
dtype: float32
- name: '6'
dtype: float32
- name: '7'
dtype: float32
- name: '8'
dtype: float32
- name: '9'
dtype: float32
- name: '10'
dtype: float32
- name: '11'
dtype: float32
- name: '12'
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dtype: float32
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dtype: string
splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211864778
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_GPT2_Finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
saridormi/commit-message-quality | ---
license: other
language:
- code
- en
task_categories:
- text-classification
tags:
- code
- commit_message_generation
configs:
- config_name: default
data_files:
- split: test
path: data.jsonl
---
# Commit Message Quality dataset
This is the dataset for commit message quality classification, used during processing of [Commit Message Generation dataset](https://huggingface.co/datasets/JetBrains-Research/lca-commit-message-generation) from
🏟️ [Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
This is a cleaned and relabeled version of the [dataset](https://zenodo.org/records/7042943#.YxG_ROzMLdo) from 📜 ["Commit Message Matters: Investigating Impact and Evolution of Commit Message Quality", ICSE'23](https://ieeexplore.ieee.org/abstract/document/10172825). We drop "Neither Why nor What" examples, clean all the external references (URLs, issues/PR references) from messages and manually label each sample with the goal of training a binary commit message quality classifier for data filtering in mind.
## How-to
Load the data via [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.load_dataset):
```
from datasets import load_dataset
dataset = load_dataset("saridormi/commit-message-quality", split="test")
```
Note that all the data we have is considered to be in the test split.
## Dataset Structure
Each example has the following fields:
| **Field** | **Description** |
|:---------------------|:---------------------------------------------------------------------------|
| `url` | Link to commit on GitHub. |
| `original_message` | Commit message as it was in the original dataset. |
| `message` | Commit message cleaned from external references. |
| `original_label` | Commit message label as it was in the original dataset (`Why and What`/`No Why`/`No What`). |
| `is_good` | Whether the commit message serves as a good example of a *high quality* commit message (boolean). |
| `is_bad` | Whether the commit message serves as a good example of a *low quality* commit message (boolean). |
| `binary_label` | Commit message label: `1` for *high quality* messages, `0` for *low quality* messages, `null` for messages not recommended to consider for classifier training. |
Data point example:
```
{"url":"https://github.com/spring-projects/spring-boot/commit/7080500db9ecf1cf78ad23503280c713bb6e8649",
"original_message":"Upgrade to Commons Lang3 3.6 \n \n Closes gh-9661",
"message":"Upgrade to Commons Lang3 3.6",
"original_label":"Why and What",
"is_good": False,
"is_bad": True,
"binary_label":0.0,
}
```
|
AdapterOcean/data-standardized_cluster_2_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 16284739
num_examples: 7804
download_size: 6842507
dataset_size: 16284739
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_2_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
2uanDM/soict-motorbike-detection | ---
license: mit
---
|
recoilme/aesthetic_photos_xs | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1391150970.57
num_examples: 1010
download_size: 1391377501
dataset_size: 1391150970.57
tags:
- art
pretty_name: aesthetic photos xs
size_categories:
- 1K<n<10K
---
# aesthetic_photos_xs
- 1k manually selected photos from unsplash
- captioned with BLIP model large caption && SmilingWolf/wd-v1-4-convnext-tagger-v2
# repositories
- https://github.com/recoilme/unsplash_dwn
- https://github.com/kohya-ss/sd-scripts
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
louisbrulenaudet/code-communes-nouvelle-caledonie | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code des communes de la Nouvelle-Calédonie
source_datasets:
- original
pretty_name: Code des communes de la Nouvelle-Calédonie
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code des communes de la Nouvelle-Calédonie, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_500 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 934
num_examples: 32
download_size: 2046
dataset_size: 934
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_13_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kkurihara-cs/LCTG-Bench | ---
license: cc-by-nc-nd-4.0
---
|
Aim34/LATEX_Correction | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: data_source
dtype: string
splits:
- name: train
num_bytes: 120749.44578313253
num_examples: 82
- name: test
num_bytes: 2237
num_examples: 1
download_size: 82665
dataset_size: 122986.44578313253
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
open-llm-leaderboard/details_lloorree__kssht-dahj-70b | ---
pretty_name: Evaluation run of lloorree/kssht-dahj-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lloorree/kssht-dahj-70b](https://huggingface.co/lloorree/kssht-dahj-70b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lloorree__kssht-dahj-70b\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-18T23:50:58.093131](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__kssht-dahj-70b/blob/main/results_2023-09-18T23-50-58.093131.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\": 0.7033014017574061,\n\
\ \"acc_stderr\": 0.03081446175839962,\n \"acc_norm\": 0.7072547203046122,\n\
\ \"acc_norm_stderr\": 0.03078306684205309,\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5891645864509103,\n\
\ \"mc2_stderr\": 0.015115214729699759\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6612627986348123,\n \"acc_stderr\": 0.013830568927974332,\n\
\ \"acc_norm\": 0.7081911262798635,\n \"acc_norm_stderr\": 0.013284525292403515\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6867157936666003,\n\
\ \"acc_stderr\": 0.0046288092584835265,\n \"acc_norm\": 0.8730332603067118,\n\
\ \"acc_norm_stderr\": 0.003322552829608905\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.030643607071677098,\n\
\ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.030643607071677098\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7283018867924528,\n \"acc_stderr\": 0.027377706624670713,\n\
\ \"acc_norm\": 0.7283018867924528,\n \"acc_norm_stderr\": 0.027377706624670713\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.032166008088022675,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.032166008088022675\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|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-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\": 0.37254901960784315,\n \"acc_stderr\": 0.04810840148082635,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.04810840148082635\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7021276595744681,\n \"acc_stderr\": 0.029896145682095455,\n\
\ \"acc_norm\": 0.7021276595744681,\n \"acc_norm_stderr\": 0.029896145682095455\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.0470070803355104,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.0470070803355104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4470899470899471,\n \"acc_stderr\": 0.025606723995777025,\n \"\
acc_norm\": 0.4470899470899471,\n \"acc_norm_stderr\": 0.025606723995777025\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n\
\ \"acc_stderr\": 0.021886178567172534,\n \"acc_norm\": 0.8193548387096774,\n\
\ \"acc_norm_stderr\": 0.021886178567172534\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.541871921182266,\n \"acc_stderr\": 0.03505630140785741,\n\
\ \"acc_norm\": 0.541871921182266,\n \"acc_norm_stderr\": 0.03505630140785741\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\"\
: 0.81,\n \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.02845038880528437,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.02845038880528437\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\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078912,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078912\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7282051282051282,\n \"acc_stderr\": 0.02255655101013236,\n \
\ \"acc_norm\": 0.7282051282051282,\n \"acc_norm_stderr\": 0.02255655101013236\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\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295894,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295894\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.46357615894039733,\n \"acc_stderr\": 0.04071636065944215,\n \"\
acc_norm\": 0.46357615894039733,\n \"acc_norm_stderr\": 0.04071636065944215\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.908256880733945,\n \"acc_stderr\": 0.012376323409137103,\n \"\
acc_norm\": 0.908256880733945,\n \"acc_norm_stderr\": 0.012376323409137103\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.03362277436608043,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03362277436608043\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\
acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7982062780269058,\n\
\ \"acc_stderr\": 0.02693611191280227,\n \"acc_norm\": 0.7982062780269058,\n\
\ \"acc_norm_stderr\": 0.02693611191280227\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\
\ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8159509202453987,\n \"acc_stderr\": 0.03044677768797173,\n\
\ \"acc_norm\": 0.8159509202453987,\n \"acc_norm_stderr\": 0.03044677768797173\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8931623931623932,\n\
\ \"acc_stderr\": 0.02023714900899093,\n \"acc_norm\": 0.8931623931623932,\n\
\ \"acc_norm_stderr\": 0.02023714900899093\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8735632183908046,\n\
\ \"acc_stderr\": 0.011884488905895538,\n \"acc_norm\": 0.8735632183908046,\n\
\ \"acc_norm_stderr\": 0.011884488905895538\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7832369942196532,\n \"acc_stderr\": 0.022183477668412856,\n\
\ \"acc_norm\": 0.7832369942196532,\n \"acc_norm_stderr\": 0.022183477668412856\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6458100558659218,\n\
\ \"acc_stderr\": 0.015995644947299225,\n \"acc_norm\": 0.6458100558659218,\n\
\ \"acc_norm_stderr\": 0.015995644947299225\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7679738562091504,\n \"acc_stderr\": 0.024170840879340873,\n\
\ \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.024170840879340873\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\
\ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\
\ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8240740740740741,\n \"acc_stderr\": 0.021185893615225184,\n\
\ \"acc_norm\": 0.8240740740740741,\n \"acc_norm_stderr\": 0.021185893615225184\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.574468085106383,\n \"acc_stderr\": 0.029494827600144366,\n \
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.029494827600144366\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5612777053455019,\n\
\ \"acc_stderr\": 0.012673969883493268,\n \"acc_norm\": 0.5612777053455019,\n\
\ \"acc_norm_stderr\": 0.012673969883493268\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.02667925227010314,\n\
\ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.02667925227010314\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7696078431372549,\n \"acc_stderr\": 0.01703522925803403,\n \
\ \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.01703522925803403\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.025000256039546195,\n\
\ \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.025000256039546195\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276915,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276915\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42962056303549573,\n\
\ \"mc1_stderr\": 0.017329234580409098,\n \"mc2\": 0.5891645864509103,\n\
\ \"mc2_stderr\": 0.015115214729699759\n }\n}\n```"
repo_url: https://huggingface.co/lloorree/kssht-dahj-70b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|arc:challenge|25_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hellaswag|10_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T23-50-58.093131.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T23-50-58.093131.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T23-50-58.093131.parquet'
- config_name: results
data_files:
- split: 2023_09_18T23_50_58.093131
path:
- results_2023-09-18T23-50-58.093131.parquet
- split: latest
path:
- results_2023-09-18T23-50-58.093131.parquet
---
# Dataset Card for Evaluation run of lloorree/kssht-dahj-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lloorree/kssht-dahj-70b
- **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 [lloorree/kssht-dahj-70b](https://huggingface.co/lloorree/kssht-dahj-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lloorree__kssht-dahj-70b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T23:50:58.093131](https://huggingface.co/datasets/open-llm-leaderboard/details_lloorree__kssht-dahj-70b/blob/main/results_2023-09-18T23-50-58.093131.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": 0.7033014017574061,
"acc_stderr": 0.03081446175839962,
"acc_norm": 0.7072547203046122,
"acc_norm_stderr": 0.03078306684205309,
"mc1": 0.42962056303549573,
"mc1_stderr": 0.017329234580409098,
"mc2": 0.5891645864509103,
"mc2_stderr": 0.015115214729699759
},
"harness|arc:challenge|25": {
"acc": 0.6612627986348123,
"acc_stderr": 0.013830568927974332,
"acc_norm": 0.7081911262798635,
"acc_norm_stderr": 0.013284525292403515
},
"harness|hellaswag|10": {
"acc": 0.6867157936666003,
"acc_stderr": 0.0046288092584835265,
"acc_norm": 0.8730332603067118,
"acc_norm_stderr": 0.003322552829608905
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8289473684210527,
"acc_stderr": 0.030643607071677098,
"acc_norm": 0.8289473684210527,
"acc_norm_stderr": 0.030643607071677098
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7283018867924528,
"acc_stderr": 0.027377706624670713,
"acc_norm": 0.7283018867924528,
"acc_norm_stderr": 0.027377706624670713
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.032166008088022675,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.032166008088022675
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"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": 0.37254901960784315,
"acc_stderr": 0.04810840148082635,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.04810840148082635
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7021276595744681,
"acc_stderr": 0.029896145682095455,
"acc_norm": 0.7021276595744681,
"acc_norm_stderr": 0.029896145682095455
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.0470070803355104,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.0470070803355104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.03996629574876719,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.03996629574876719
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4470899470899471,
"acc_stderr": 0.025606723995777025,
"acc_norm": 0.4470899470899471,
"acc_norm_stderr": 0.025606723995777025
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
"acc_stderr": 0.021886178567172534,
"acc_norm": 0.8193548387096774,
"acc_norm_stderr": 0.021886178567172534
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.541871921182266,
"acc_stderr": 0.03505630140785741,
"acc_norm": 0.541871921182266,
"acc_norm_stderr": 0.03505630140785741
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8424242424242424,
"acc_stderr": 0.02845038880528437,
"acc_norm": 0.8424242424242424,
"acc_norm_stderr": 0.02845038880528437
},
"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": 0.9326424870466321,
"acc_stderr": 0.018088393839078912,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.018088393839078912
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7282051282051282,
"acc_stderr": 0.02255655101013236,
"acc_norm": 0.7282051282051282,
"acc_norm_stderr": 0.02255655101013236
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251972,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251972
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7815126050420168,
"acc_stderr": 0.02684151432295894,
"acc_norm": 0.7815126050420168,
"acc_norm_stderr": 0.02684151432295894
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.46357615894039733,
"acc_stderr": 0.04071636065944215,
"acc_norm": 0.46357615894039733,
"acc_norm_stderr": 0.04071636065944215
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.908256880733945,
"acc_stderr": 0.012376323409137103,
"acc_norm": 0.908256880733945,
"acc_norm_stderr": 0.012376323409137103
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.03362277436608043,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.03362277436608043
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
"acc_stderr": 0.018869514646658928,
"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.018869514646658928
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8987341772151899,
"acc_stderr": 0.019637720526065498,
"acc_norm": 0.8987341772151899,
"acc_norm_stderr": 0.019637720526065498
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7982062780269058,
"acc_stderr": 0.02693611191280227,
"acc_norm": 0.7982062780269058,
"acc_norm_stderr": 0.02693611191280227
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8702290076335878,
"acc_stderr": 0.029473649496907065,
"acc_norm": 0.8702290076335878,
"acc_norm_stderr": 0.029473649496907065
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035202,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035202
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8240740740740741,
"acc_stderr": 0.036809181416738807,
"acc_norm": 0.8240740740740741,
"acc_norm_stderr": 0.036809181416738807
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8159509202453987,
"acc_stderr": 0.03044677768797173,
"acc_norm": 0.8159509202453987,
"acc_norm_stderr": 0.03044677768797173
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.5089285714285714,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8931623931623932,
"acc_stderr": 0.02023714900899093,
"acc_norm": 0.8931623931623932,
"acc_norm_stderr": 0.02023714900899093
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8735632183908046,
"acc_stderr": 0.011884488905895538,
"acc_norm": 0.8735632183908046,
"acc_norm_stderr": 0.011884488905895538
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7832369942196532,
"acc_stderr": 0.022183477668412856,
"acc_norm": 0.7832369942196532,
"acc_norm_stderr": 0.022183477668412856
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6458100558659218,
"acc_stderr": 0.015995644947299225,
"acc_norm": 0.6458100558659218,
"acc_norm_stderr": 0.015995644947299225
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7679738562091504,
"acc_stderr": 0.024170840879340873,
"acc_norm": 0.7679738562091504,
"acc_norm_stderr": 0.024170840879340873
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.77491961414791,
"acc_stderr": 0.023720088516179027,
"acc_norm": 0.77491961414791,
"acc_norm_stderr": 0.023720088516179027
},
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},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.574468085106383,
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"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.029494827600144366
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5612777053455019,
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"acc_norm": 0.5612777053455019,
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},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7389705882352942,
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"acc_norm_stderr": 0.02667925227010314
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7696078431372549,
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"acc_norm_stderr": 0.01703522925803403
},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8122448979591836,
"acc_stderr": 0.025000256039546195,
"acc_norm": 0.8122448979591836,
"acc_norm_stderr": 0.025000256039546195
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8955223880597015,
"acc_stderr": 0.021628920516700637,
"acc_norm": 0.8955223880597015,
"acc_norm_stderr": 0.021628920516700637
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5481927710843374,
"acc_stderr": 0.03874371556587953,
"acc_norm": 0.5481927710843374,
"acc_norm_stderr": 0.03874371556587953
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8713450292397661,
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"acc_norm": 0.8713450292397661,
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},
"harness|truthfulqa:mc|0": {
"mc1": 0.42962056303549573,
"mc1_stderr": 0.017329234580409098,
"mc2": 0.5891645864509103,
"mc2_stderr": 0.015115214729699759
}
}
```
### 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] |
Wanfq/Explore_Instruct_Math_64k | ---
license: cc-by-nc-4.0
language:
- en
---
<p align="center" width="100%">
</p>
<div id="top" align="center">
**Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration**
<h4> |<a href="https://arxiv.org/abs/2310.09168"> 📑 Paper </a> |
<a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> |
<a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> |
<a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> |
</h4>
<!-- **Authors:** -->
_**Fanqi Wan<sup>†</sup>, Xinting Huang<sup>‡</sup>, Tao Yang<sup>†</sup>, Xiaojun Quan<sup>†</sup>, Wei Bi<sup>‡</sup>, Shuming Shi<sup>‡</sup>**_
<!-- **Affiliations:** -->
_<sup>†</sup> Sun Yat-sen University,
<sup>‡</sup> Tencent AI Lab_
</div>
## News
- **Oct 16, 2023:** 🔥 We're excited to announce that the Explore-Instruct datasets in brainstorming, rewriting, and math domains are now available on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct)! Additionally, we've released Explore-LM models that have been initialized with LLaMA-7B and fine-tuned with the Explore-Instruct data in each domain. You can find these models on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Happy exploring and instructing!
## Contents
- [Overview](#overview)
- [Data Release](#data-release)
- [Model Release](#model-release)
- [Data Generation Process](#data-generation-process)
- [Fine-tuning](#fine-tuning)
- [Evaluation](#evaluation)
- [Limitations](#limitations)
- [License](#license)
- [Citation](#citation)
- [Acknowledgements](#acknowledgments)
## Overview
We propose Explore-Instruct, a novel approach to enhancing domain-specific instruction coverage. We posit that the domain space is inherently structured akin to a tree, reminiscent of cognitive science ontologies. Drawing from the essence of classical search algorithms and incorporating the power of LLMs, Explore-Instruct is conceived to actively traverse the domain space and generate instruction-tuning data, **not** necessitating a predefined tree structure. Specifically, Explore-Instruct employs two strategic operations: lookahead and backtracking exploration:
- **Lookahead** delves into a multitude of potential fine-grained sub-tasks, thereby mapping out a complex network of tasks
- **Backtracking** seeks alternative branches to widen the search boundary, hence extending the domain spectrum.
<p align="center">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig2.png?raw=true" width="95%"> <br>
</p>
## Data Release
We release the Explore-Instruct data in brainstorming, rewriting, and math domains on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct). Each domain includes two versions of datasets: the basic and extended version. The base version contains 10k instruction-tuning data and the extended version contains 16k, 32k, and 64k instruction-tuning data for each domain respectively. Each dataset is a structured data file in the JSON format. It consists of a list of dictionaries, with each dictionary containing the following fields:
- `instruction`: `str`, describes the task the model should perform.
- `input`: `str`, optional context or input for the task.
- `output`: `str`, ground-truth output text for the task and input text.
The results of data-centric analysis are shown as follows:
<p align="left">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig1.png?raw=true" width="50%"> <br>
</p>
| Method | Brainstorming Unique<br/>V-N pairs | Rewriting Unique<br/>V-N pairs | Math Unique<br/>V-N pairs |
|:--------------------------------|:----------------------------------:|:------------------------------:|:-------------------------:|
| _Domain-Specific Human-Curated_ | 2 | 8 | 3 |
| _Domain-Aware Self-Instruct_ | 781 | 1715 | 451 |
| Explore-Instruct | **790** | **2015** | **917** |
## Model Release
We release the Explore-LM models in brainstorming, rewriting, and math domains on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Each domain includes two versions of models: the basic and extended version trained with the corresponding version of dataset.
The results of automatic and human evaluation in three domains are shown as follows:
- Automatic evaluation:
| Automatic Comparison in the Brainstorming Domain | Win:Tie:Lose | Beat Rate |
|:-------------------------------------------------|:------------:|:---------:|
| Explore-LM vs Domain-Curated-LM | 194:1:13 | 93.72 |
| Explore-LM-Ext vs Domain-Curated-LM | 196:1:11 | 94.69 |
| Explore-LM vs Domain-Instruct-LM | 114:56:38 | 75.00 |
| Explore-LM-Ext vs Domain-Instruct-LM | 122:55:31 | 79.74 |
| Explore-LM vs ChatGPT | 52:71:85 | 37.96 |
| Explore-LM-Ext vs ChatGPT | 83:69:56 | 59.71 |
| Automatic Comparison in the Rewriting Domain | Win:Tie:Lose | Beat Rate |
|:---------------------------------------------|:------------:|:---------:|
| Explore-LM vs Domain-Curated-LM | 50:38:6 | 89.29 |
| Explore-LM-Ext vs Domain-Curated-LM | 53:37:4 | 92.98 |
| Explore-LM vs Domain-Instruct-LM | 34:49:11 | 75.56 |
| Explore-LM-Ext vs Domain-Instruct-LM | 35:53:6 | 85.37 |
| Explore-LM vs ChatGPT | 11:59:24 | 31.43 |
| Explore-LM-Ext vs ChatGPT | 12:56:26 | 31.58 |
| Automatic Comparison in the Math Domain | Accuracy Rate |
|:----------------------------------------|:-------------:|
| Domain-Curated-LM | 3.4 |
| Domain-Instruct-LM | 4.0 |
| Explore-LM | 6.8 |
| Explore-LM-Ext | 8.4 |
| ChatGPT | 34.8 |
- Human evaluation:
<p align="left">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig5.png?raw=true" width="95%"> <br>
</p>
## Data Generation Process
To generate the domain-specific instruction-tuning data, please follow the following commands step by step:
### Domain Space Exploration
```
python3 generate_instruction.py \
--action extend \
--save_dir ./en_data/demo_domain \ # input dir include current domain tree for exploration
--out_dir ./en_data/demo_domain_exploration \ # output dir of the explored new domain tree
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--extend_nums <TASK_NUMBER_DEPTH_0>,...,<TASK_NUMBER_DEPTH_MAX_DEPTH-1> \ # exploration breadth at each depth
--max_depth <MAX_DEPTH> \ # exploration depth
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Instruction-Tuning Data Generation
```
python3 generate_instruction.py \
--action enrich \
--save_dir ./en_data/demo_domain_exploration \ # input dir include current domain tree for data generation
--out_dir ./en_data/demo_domain_generation \ # output dir of the domain tree with generated data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--enrich_nums <DATA_NUMBER_DEPTH_0>,...,<DATA_NUMBER_DEPTH_MAX_DEPTH> \ # data number for task at each depth
--enrich_batch_size <BATCH_SIZE> \ # batch size for data generation
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Task Pruning
```
python3 generate_instruction.py \
--action prune \
--save_dir ./en_data/demo_domain_generation \ # input dir include current domain tree for task pruning
--out_dir ./en_data/demo_domain_pruning \ # output dir of the domain tree with 'pruned_subtasks_name.json' file
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks
--prune_threshold <PRUNE_THRESHOLD> \ # threshold of rouge-l overlap between task names
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Data Filtering
```
python3 generate_instruction.py \
--action filter \
--save_dir ./en_data/demo_domain_pruning \ # input dir include current domain tree for data filtering
--out_dir ./en_data/demo_domain_filtering \ # output dir of the domain tree with fitered data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks
--filter_threshold <FILTER_THRESHOLD> \ # threshold of rouge-l overlap between instructions
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Data Sampling
```
python3 generate_instruction.py \
--action sample \
--save_dir ./en_data/demo_domain_filtering \ # input dir include current domain tree for data sampling
--out_dir ./en_data/demo_domain_sampling \ # output dir of the domain tree with sampled data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_filtering/pruned_subtasks_name.json \ # file of pruned tasks
--sample_example_num <SAMPLE_EXAMPLES_NUM> \ # number of sampled examples
--sample_max_depth <SAMPLE_MAX_DEPTH> \ # max depth for data sampling
--sample_use_pruned \ # do not sample from pruned tasks
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
## Fine-tuning
We fine-tune LLaMA-7B with the following hyperparameters:
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
|:----------------|-------------------:|---------------:|--------:|------------:|--------------:|
| LLaMA 7B | 128 | 2e-5 | 3 | 512| 0 |
To reproduce the training procedure, please use the following command:
```
deepspeed --num_gpus=8 ./train/train.py \
--deepspeed ./deepspeed_config/deepspeed_zero3_offload_config.json \
--model_name_or_path decapoda-research/llama-7b-hf \
--data_path ./en_data/demo_domain_sampling \
--fp16 True \
--output_dir ./training_results/explore-lm-7b-demo-domain \
--num_train_epochs 3 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--model_max_length 512 \
--save_strategy "steps" \
--save_steps 2000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--prompt_type alpaca \
2>&1 | tee ./training_logs/explore-lm-7b-demo-domain.log
python3 ./train/zero_to_fp32.py \
--checkpoint_dir ./training_results/explore-lm-7b-demo-domain \
--output_file ./training_results/explore-lm-7b-demo-domain/pytorch_model.bin
```
## Evaluation
The evaluation datasets for different domains are as follows:
- Brainstorming and Rewriting: From the corresponding categories in the translated test set of BELLE. ([en_eval_set.jsonl](./eval/question/en_eval_set.jsonl))
- Math: From randomly selected 500 questions from the test set of MATH. ([MATH_eval_set_sample.jsonl](./eval/question/MATH_eval_set_sample.jsonl))
The evaluation metrics for different domains are as follows:
- Brainstorming and Rewriting: Both automatic and human evaluations following Vicuna.
- Math: Accuracy Rate metric in solving math problems.
The automatic evaluation commands for different domains are as follows:
```
# Brainstorming and Rewriting Domain
# 1. Inference
python3 ./eval/generate.py \
--model_id <MODEL_ID> \
--model_path <MODEL_PATH> \
--question_file ./eval/question/en_eval_set.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl \
--num_gpus 8 \
--num_beams 1 \
--temperature 0.7 \
--max_new_tokens 512 \
--prompt_type alpaca \
--do_sample
# 2. Evaluation
python3 ./eval/chatgpt_score.py \
--baseline_file ./eval/answer/<MODEL_1>.jsonl \ # answer of baseline model to compare with
--answer_file ./eval/answer/<MODEL_2>.jsonl \ # answer of evaluation model
--review_file ./eval/review/<MODEL_1>_cp_<MODEL_2>_<DOMAIN>.jsonl \ # review from chatgpt
--prompt_file ./eval/prompt/en_review_prompt_compare.jsonl \ # evaluation prompt for chatgpt
--target_classes <DOMAIN> \ # evaluation domain
--batch_size <BATCH_SIZE> \
--review_model "gpt-3.5-turbo-0301"
```
```
# Math Domain
# 1. Inference
python3 ./eval/generate.py \
--model_id <MODEL_ID> \
--model_path <MODEL_PATH> \
--question_file ./eval/question/MATH_eval_set_sample.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl \
--num_gpus 8 \
--num_beams 10 \
--temperature 1.0 \
--max_new_tokens 512 \
--prompt_type alpaca
# 2. Evaluation
python3 ./eval/auto_eval.py \
--question_file ./eval/question/MATH_eval_set_sample.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl # answer of evaluation model
```
## Limitations
Explore-Instruct is still under development and needs a lot of improvements. We acknowledge that our work focuses on the enhancement of domain-specific instruction coverage and does not address other aspects of instruction-tuning, such as the generation of complex and challenging instructions or the mitigation of toxic and harmful instructions. Future work is needed to explore the potential of our approach in these areas.
## License
Explore-Instruct is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. The weights of Explore-LM models are also CC BY NC 4.0 (allowing only non-commercial use).
## Citation
If you find this work is relevant with your research or applications, please feel free to cite our work!
```
@misc{wan2023explore,
title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration},
author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi},
year={2023},
eprint={2310.09168},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Acknowledgments
This repo benefits from [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Vicuna](https://github.com/lm-sys/FastChat). Thanks for their wonderful works!
|
ateebak/sidewalk-imagery | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 3138225.0
num_examples: 10
download_size: 3139735
dataset_size: 3138225.0
---
# Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ovior/twitter_dataset_1712998781 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
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dtype: int64
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dtype: string
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dtype: string
splits:
- name: train
num_bytes: 2773442
num_examples: 8252
download_size: 1581091
dataset_size: 2773442
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-14000 | ---
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: 1074175
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
griffin/seahorse_zeroshot | ---
dataset_info:
features:
- name: gem_id
dtype: string
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 115866100
num_examples: 85114
- name: validation
num_bytes: 16905383
num_examples: 12568
- name: test
num_bytes: 34729550
num_examples: 25053
download_size: 23952107
dataset_size: 167501033
---
# Dataset Card for "seahorse_zeroshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wltjr1007/cifar100_clip | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-80-Million-Tiny-Images
task_categories:
- image-classification
task_ids: []
paperswithcode_id: cifar-100
pretty_name: Cifar100
dataset_info:
config_name: cifar100
features:
- name: img
dtype: image
- name: fine_label
dtype:
class_label:
names:
'0': apple
'1': aquarium_fish
'2': baby
'3': bear
'4': beaver
'5': bed
'6': bee
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'8': bicycle
'9': bottle
'10': bowl
'11': boy
'12': bridge
'13': bus
'14': butterfly
'15': camel
'16': can
'17': castle
'18': caterpillar
'19': cattle
'20': chair
'21': chimpanzee
'22': clock
'23': cloud
'24': cockroach
'25': couch
'26': cra
'27': crocodile
'28': cup
'29': dinosaur
'30': dolphin
'31': elephant
'32': flatfish
'33': forest
'34': fox
'35': girl
'36': hamster
'37': house
'38': kangaroo
'39': keyboard
'40': lamp
'41': lawn_mower
'42': leopard
'43': lion
'44': lizard
'45': lobster
'46': man
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'48': motorcycle
'49': mountain
'50': mouse
'51': mushroom
'52': oak_tree
'53': orange
'54': orchid
'55': otter
'56': palm_tree
'57': pear
'58': pickup_truck
'59': pine_tree
'60': plain
'61': plate
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'63': porcupine
'64': possum
'65': rabbit
'66': raccoon
'67': ray
'68': road
'69': rocket
'70': rose
'71': sea
'72': seal
'73': shark
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'75': skunk
'76': skyscraper
'77': snail
'78': snake
'79': spider
'80': squirrel
'81': streetcar
'82': sunflower
'83': sweet_pepper
'84': table
'85': tank
'86': telephone
'87': television
'88': tiger
'89': tractor
'90': train
'91': trout
'92': tulip
'93': turtle
'94': wardrobe
'95': whale
'96': willow_tree
'97': wolf
'98': woman
'99': worm
- name: coarse_label
dtype:
class_label:
names:
'0': aquatic_mammals
'1': fish
'2': flowers
'3': food_containers
'4': fruit_and_vegetables
'5': household_electrical_devices
'6': household_furniture
'7': insects
'8': large_carnivores
'9': large_man-made_outdoor_things
'10': large_natural_outdoor_scenes
'11': large_omnivores_and_herbivores
'12': medium_mammals
'13': non-insect_invertebrates
'14': people
'15': reptiles
'16': small_mammals
'17': trees
'18': vehicles_1
'19': vehicles_2
--- |
heliosprime/twitter_dataset_1713150223 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
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dtype: string
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dtype: string
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dtype: int64
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dtype: string
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dtype: string
splits:
- name: train
num_bytes: 3401
num_examples: 9
download_size: 8348
dataset_size: 3401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713150223"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_uninflect | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 569109
num_examples: 2444
- name: dev_mismatched
num_bytes: 576931
num_examples: 2309
- name: test_matched
num_bytes: 577734
num_examples: 2486
- name: test_mismatched
num_bytes: 604714
num_examples: 2479
- name: train
num_bytes: 23996394
num_examples: 101139
download_size: 17049839
dataset_size: 26324882
---
# Dataset Card for "MULTI_VALUE_mnli_uninflect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/lmind_hotpot_train1000_eval200_v1_recite_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 173266
num_examples: 1000
- name: train_recite_qa
num_bytes: 1024784
num_examples: 1000
- name: eval_qa
num_bytes: 33160
num_examples: 200
- name: eval_recite_qa
num_bytes: 208740
num_examples: 200
- name: all_docs
num_bytes: 1054269
num_examples: 2373
- name: train
num_bytes: 2079053
num_examples: 3373
- name: validation
num_bytes: 208740
num_examples: 200
download_size: 2996388
dataset_size: 4782012
---
# Dataset Card for "lmind_hotpot_train1000_eval200_v1_recite_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
enwik8 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: enwik8
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- fill-mask
- text-generation
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
- config_name: enwik8
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 104299244
num_examples: 1128024
download_size: 36445475
dataset_size: 102383126
- config_name: enwik8-raw
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 100000008
num_examples: 1
download_size: 36445475
dataset_size: 100000008
---
# Dataset Card for enwik8
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** http://mattmahoney.net/dc/textdata.html
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** https://paperswithcode.com/sota/language-modelling-on-enwiki8
- **Point of Contact:** [Needs More Information]
- **Size of downloaded dataset files:** 36.45 MB
- **Size of the generated dataset:** 102.38 MB
- **Total amount of disk used:** 138.83 MB
### Dataset Summary
The enwik8 dataset is the first 100,000,000 (100M) bytes of the English Wikipedia XML dump on Mar. 3, 2006 and is typically used to measure a model's ability to compress data.
### Supported Tasks and Leaderboards
A leaderboard for byte-level causal language modelling can be found on [paperswithcode](https://paperswithcode.com/sota/language-modelling-on-enwiki8)
### Languages
en
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 36.45 MB
- **Size of the generated dataset:** 102.38 MB
- **Total amount of disk used:** 138.83 MB
```
{
"text": "In [[Denmark]], the [[Freetown Christiania]] was created in downtown [[Copenhagen]]....",
}
```
### Data Fields
The data fields are the same among all sets.
#### enwik8
- `text`: a `string` feature.
#### enwik8-raw
- `text`: a `string` feature.
### Data Splits
| dataset | train |
| --- | --- |
| enwik8 | 1128024 |
| enwik8- raw | 1 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
The data is just English Wikipedia XML dump on Mar. 3, 2006 split by line for enwik8 and not split by line for enwik8-raw.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Dataset is not part of a publication, and can therefore not be cited.
### Contributions
Thanks to [@HallerPatrick](https://github.com/HallerPatrick) for adding this dataset and [@mtanghu](https://github.com/mtanghu) for updating it. |
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