datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Willinton/Code_Llms_HiddenStates | ---
license: mit
---
|
AchrefLearning/big_five_classification | ---
license: mit
---
|
Sakshamrzt/IndicNLP-Punjabi | ---
dataset_info:
- config_name: default
features:
- name: headline
dtype: string
- name: news
dtype: string
- name: class
dtype: float64
splits:
- name: train
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- config_name: test
data_files:
- split: test
path: test.jsonl
--- |
CyberHarem/kurosaki_chitose_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls)
This is the dataset of kurosaki_chitose/黒埼ちとせ (THE iDOLM@STER: Cinderella Girls), containing 328 images and their tags.
The core tags of this character are `blonde_hair, long_hair, bangs, red_eyes, breasts, hair_between_eyes, hairband, very_long_hair, large_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 328 | 532.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 328 | 293.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 830 | 626.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 328 | 465.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 830 | 913.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurosaki_chitose_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kurosaki_chitose_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, solo, open_mouth, white_shirt, black_hairband, :d, white_background, brooch, frills, simple_background, skirt, upper_body, blush, sleeves_past_wrists, thighhighs |
| 1 | 18 |  |  |  |  |  | serafuku, 1girl, long_sleeves, red_neckerchief, shirt, looking_at_viewer, solo, white_sailor_collar, blush, pleated_skirt, black_skirt, black_hairband, open_cardigan, open_mouth, white_background, :d, simple_background |
| 2 | 8 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, white_dress, smile, solo, strapless_dress, wedding_dress, blush, bridal_veil, bride, cleavage, upper_body, collarbone, closed_mouth, holding_bouquet, necklace, red_rose, white_background |
| 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, hair_flower, looking_at_viewer, medium_breasts, red_rose, solo, red_dress, smile, blush, cleavage, hair_intakes, wrist_cuffs, nail_polish, petals, red_nails, simple_background, strapless, upper_body, white_background |
| 4 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, solo, cleavage, collarbone, midriff, pink_shirt, groin, long_sleeves, open_mouth, pink_shorts, simple_background, sweat, white_background, :d, crop_top, heart, off_shoulder, pink_eyes, sleeves_past_wrists, stomach |
| 5 | 17 |  |  |  |  |  | looking_at_viewer, smile, 1girl, blush, collarbone, navel, solo, cleavage, bikini, bare_shoulders, sitting, black_hairband, closed_mouth, thighs |
| 6 | 7 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, sweat, looking_at_viewer, nipples, penis, pov, smile, breast_grab, grabbing, mosaic_censoring, nude, open_mouth, paizuri, black_hairband, collarbone, male_pubic_hair, navel |
| 7 | 8 |  |  |  |  |  | 1girl, fake_animal_ears, playboy_bunny, rabbit_ears, detached_collar, looking_at_viewer, solo, strapless_leotard, black_leotard, cleavage, smile, wrist_cuffs, bare_shoulders, blush, simple_background, thighhighs, white_background, black_hairband, bowtie, covered_navel, medium_breasts, nail_polish, pantyhose, rabbit_tail, red_nails |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | long_sleeves | looking_at_viewer | solo | open_mouth | white_shirt | black_hairband | :d | white_background | brooch | frills | simple_background | skirt | upper_body | blush | sleeves_past_wrists | thighhighs | serafuku | red_neckerchief | shirt | white_sailor_collar | pleated_skirt | black_skirt | open_cardigan | bare_shoulders | white_dress | smile | strapless_dress | wedding_dress | bridal_veil | bride | cleavage | collarbone | closed_mouth | holding_bouquet | necklace | red_rose | hair_flower | medium_breasts | red_dress | hair_intakes | wrist_cuffs | nail_polish | petals | red_nails | strapless | navel | midriff | pink_shirt | groin | pink_shorts | sweat | crop_top | heart | off_shoulder | pink_eyes | stomach | bikini | sitting | thighs | 1boy | hetero | solo_focus | nipples | penis | pov | breast_grab | grabbing | mosaic_censoring | nude | paizuri | male_pubic_hair | fake_animal_ears | playboy_bunny | rabbit_ears | detached_collar | strapless_leotard | black_leotard | bowtie | covered_navel | pantyhose | rabbit_tail |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:-------------|:--------------|:-----------------|:-----|:-------------------|:---------|:---------|:--------------------|:--------|:-------------|:--------|:----------------------|:-------------|:-----------|:------------------|:--------|:----------------------|:----------------|:--------------|:----------------|:-----------------|:--------------|:--------|:------------------|:----------------|:--------------|:--------|:-----------|:-------------|:---------------|:------------------|:-----------|:-----------|:--------------|:-----------------|:------------|:---------------|:--------------|:--------------|:---------|:------------|:------------|:--------|:----------|:-------------|:--------|:--------------|:--------|:-----------|:--------|:---------------|:------------|:----------|:---------|:----------|:---------|:-------|:---------|:-------------|:----------|:--------|:------|:--------------|:-----------|:-------------------|:-------|:----------|:------------------|:-------------------|:----------------|:--------------|:------------------|:--------------------|:----------------|:---------|:----------------|:------------|:--------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | | X | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | X | | | | | X | | | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | X | | | | | X | | | X | | X | X | | | | | | | | | | X | | X | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | X | X | | | X | X | | | X | | | X | X | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 17 |  |  |  |  |  | X | | X | X | | | X | | | | | | | | X | | | | | | | | | | X | | X | | | | | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | | X | | X | | X | | | | | | | | X | | | | | | | | | | | | X | | | | | | X | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | |
| 7 | 8 |  |  |  |  |  | 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 |
|
katxtong/tokenized_coqa_size356 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: start_positions
dtype: int64
- name: end_positions
dtype: int64
splits:
- name: train
num_bytes: 195999188
num_examples: 108647
- name: validation
num_bytes: 14401332
num_examples: 7983
download_size: 51708569
dataset_size: 210400520
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
Tsinggu/PolyU-COMP-Information | ---
task_categories:
- question-answering
language:
- en
size_categories:
- n<1K
---
The PolyU-COMP-Information is a dataset about the department of computing in PolyU, which contains 370 rows question and answering data. |
TheGreatP/minhavozcerto | ---
license: openrail
---
|
fazni/roles-based-on-skills | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Role
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2272289
num_examples: 3660
- name: test
num_bytes: 577048
num_examples: 916
download_size: 1174905
dataset_size: 2849337
---
|
japanese-asr/whisper_transcriptions.reazonspeech.all_59 | ---
dataset_info:
config_name: all
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 30349117557.0
num_examples: 266948
download_size: 30113495680
dataset_size: 30349117557.0
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
---
|
KolaGang/process_instruct | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 2020141143
num_examples: 274459
download_size: 626897321
dataset_size: 2020141143
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jxie/dtd | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': banded
'1': blotchy
'2': braided
'3': bubbly
'4': bumpy
'5': chequered
'6': cobwebbed
'7': cracked
'8': crosshatched
'9': crystalline
'10': dotted
'11': fibrous
'12': flecked
'13': freckled
'14': frilly
'15': gauzy
'16': grid
'17': grooved
'18': honeycombed
'19': interlaced
'20': knitted
'21': lacelike
'22': lined
'23': marbled
'24': matted
'25': meshed
'26': paisley
'27': perforated
'28': pitted
'29': pleated
'30': polka-dotted
'31': porous
'32': potholed
'33': scaly
'34': smeared
'35': spiralled
'36': sprinkled
'37': stained
'38': stratified
'39': striped
'40': studded
'41': swirly
'42': veined
'43': waffled
'44': woven
'45': wrinkled
'46': zigzagged
splits:
- name: train
num_bytes: 226313270.04
num_examples: 1880
- name: test
num_bytes: 172035822.0
num_examples: 1880
- name: validation
num_bytes: 222278767.48
num_examples: 1880
download_size: 629310459
dataset_size: 620627859.52
---
# Dataset Card for "dtd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_BFauber__opt125m_10e5_20ep | ---
pretty_name: Evaluation run of BFauber/opt125m_10e5_20ep
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BFauber/opt125m_10e5_20ep](https://huggingface.co/BFauber/opt125m_10e5_20ep)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BFauber__opt125m_10e5_20ep\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-02T19:31:32.659309](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__opt125m_10e5_20ep/blob/main/results_2024-02-02T19-31-32.659309.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\": 0.23530235034095837,\n\
\ \"acc_stderr\": 0.029883235955403636,\n \"acc_norm\": 0.23550247007959293,\n\
\ \"acc_norm_stderr\": 0.030668377840836165,\n \"mc1\": 0.24112607099143207,\n\
\ \"mc1_stderr\": 0.014974827279752332,\n \"mc2\": 0.4648926603532375,\n\
\ \"mc2_stderr\": 0.01559555646787533\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.22610921501706485,\n \"acc_stderr\": 0.012224202097063269,\n\
\ \"acc_norm\": 0.25426621160409557,\n \"acc_norm_stderr\": 0.012724999945157734\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2847042421828321,\n\
\ \"acc_stderr\": 0.0045035118550500325,\n \"acc_norm\": 0.3084047002589126,\n\
\ \"acc_norm_stderr\": 0.004608907872957696\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.23703703703703705,\n\
\ \"acc_stderr\": 0.03673731683969506,\n \"acc_norm\": 0.23703703703703705,\n\
\ \"acc_norm_stderr\": 0.03673731683969506\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.29,\n\
\ \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.21132075471698114,\n \"acc_stderr\": 0.025125766484827845,\n\
\ \"acc_norm\": 0.21132075471698114,\n \"acc_norm_stderr\": 0.025125766484827845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.16,\n\
\ \"acc_stderr\": 0.0368452949177471,\n \"acc_norm\": 0.16,\n \
\ \"acc_norm_stderr\": 0.0368452949177471\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n\
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.02865917937429232,\n\
\ \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.02865917937429232\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.0404933929774814,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.0404933929774814\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.25132275132275134,\n \"acc_stderr\": 0.022340482339643895,\n \"\
acc_norm\": 0.25132275132275134,\n \"acc_norm_stderr\": 0.022340482339643895\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15079365079365079,\n\
\ \"acc_stderr\": 0.03200686497287392,\n \"acc_norm\": 0.15079365079365079,\n\
\ \"acc_norm_stderr\": 0.03200686497287392\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.27419354838709675,\n \"acc_stderr\": 0.025378139970885193,\n \"\
acc_norm\": 0.27419354838709675,\n \"acc_norm_stderr\": 0.025378139970885193\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\
acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.16,\n \"acc_stderr\": 0.0368452949177471,\n \"acc_norm\"\
: 0.16,\n \"acc_norm_stderr\": 0.0368452949177471\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860657,\n\
\ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860657\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2205128205128205,\n \"acc_stderr\": 0.021020672680827912,\n\
\ \"acc_norm\": 0.2205128205128205,\n \"acc_norm_stderr\": 0.021020672680827912\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.21481481481481482,\n \"acc_stderr\": 0.02504044387700068,\n \
\ \"acc_norm\": 0.21481481481481482,\n \"acc_norm_stderr\": 0.02504044387700068\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.18907563025210083,\n \"acc_stderr\": 0.02543511943810535,\n\
\ \"acc_norm\": 0.18907563025210083,\n \"acc_norm_stderr\": 0.02543511943810535\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.18543046357615894,\n \"acc_stderr\": 0.03173284384294285,\n \"\
acc_norm\": 0.18543046357615894,\n \"acc_norm_stderr\": 0.03173284384294285\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.2036697247706422,\n \"acc_stderr\": 0.017266742087630793,\n \"\
acc_norm\": 0.2036697247706422,\n \"acc_norm_stderr\": 0.017266742087630793\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2037037037037037,\n \"acc_stderr\": 0.027467401804057993,\n \"\
acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.027467401804057993\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.24509803921568626,\n \"acc_stderr\": 0.030190282453501947,\n \"\
acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.030190282453501947\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.25738396624472576,\n \"acc_stderr\": 0.028458820991460305,\n \
\ \"acc_norm\": 0.25738396624472576,\n \"acc_norm_stderr\": 0.028458820991460305\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3094170403587444,\n\
\ \"acc_stderr\": 0.031024411740572206,\n \"acc_norm\": 0.3094170403587444,\n\
\ \"acc_norm_stderr\": 0.031024411740572206\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615623,\n\
\ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615623\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2264957264957265,\n\
\ \"acc_stderr\": 0.027421007295392926,\n \"acc_norm\": 0.2264957264957265,\n\
\ \"acc_norm_stderr\": 0.027421007295392926\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2413793103448276,\n\
\ \"acc_stderr\": 0.015302380123542094,\n \"acc_norm\": 0.2413793103448276,\n\
\ \"acc_norm_stderr\": 0.015302380123542094\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.02380518652488815,\n\
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.02380518652488815\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.022122439772480768,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.022122439772480768\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432414,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432414\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2503259452411995,\n\
\ \"acc_stderr\": 0.011064151027165434,\n \"acc_norm\": 0.2503259452411995,\n\
\ \"acc_norm_stderr\": 0.011064151027165434\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.40441176470588236,\n \"acc_stderr\": 0.029812630701569743,\n\
\ \"acc_norm\": 0.40441176470588236,\n \"acc_norm_stderr\": 0.029812630701569743\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.24081632653061225,\n\
\ \"acc_stderr\": 0.027372942201788163,\n \"acc_norm\": 0.24081632653061225,\n\
\ \"acc_norm_stderr\": 0.027372942201788163\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.2736318407960199,\n \"acc_stderr\": 0.03152439186555404,\n\
\ \"acc_norm\": 0.2736318407960199,\n \"acc_norm_stderr\": 0.03152439186555404\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n\
\ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.29239766081871343,\n\
\ \"acc_stderr\": 0.03488647713457921,\n \"acc_norm\": 0.29239766081871343,\n\
\ \"acc_norm_stderr\": 0.03488647713457921\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.24112607099143207,\n \"mc1_stderr\": 0.014974827279752332,\n\
\ \"mc2\": 0.4648926603532375,\n \"mc2_stderr\": 0.01559555646787533\n\
\ },\n \"harness|winogrande|5\": {\n \"acc\": 0.510655090765588,\n\
\ \"acc_stderr\": 0.014049294536290396\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/BFauber/opt125m_10e5_20ep
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|arc:challenge|25_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|gsm8k|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hellaswag|10_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T19-31-32.659309.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-02T19-31-32.659309.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- '**/details_harness|winogrande|5_2024-02-02T19-31-32.659309.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-02T19-31-32.659309.parquet'
- config_name: results
data_files:
- split: 2024_02_02T19_31_32.659309
path:
- results_2024-02-02T19-31-32.659309.parquet
- split: latest
path:
- results_2024-02-02T19-31-32.659309.parquet
---
# Dataset Card for Evaluation run of BFauber/opt125m_10e5_20ep
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BFauber/opt125m_10e5_20ep](https://huggingface.co/BFauber/opt125m_10e5_20ep) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BFauber__opt125m_10e5_20ep",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-02T19:31:32.659309](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__opt125m_10e5_20ep/blob/main/results_2024-02-02T19-31-32.659309.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": 0.23530235034095837,
"acc_stderr": 0.029883235955403636,
"acc_norm": 0.23550247007959293,
"acc_norm_stderr": 0.030668377840836165,
"mc1": 0.24112607099143207,
"mc1_stderr": 0.014974827279752332,
"mc2": 0.4648926603532375,
"mc2_stderr": 0.01559555646787533
},
"harness|arc:challenge|25": {
"acc": 0.22610921501706485,
"acc_stderr": 0.012224202097063269,
"acc_norm": 0.25426621160409557,
"acc_norm_stderr": 0.012724999945157734
},
"harness|hellaswag|10": {
"acc": 0.2847042421828321,
"acc_stderr": 0.0045035118550500325,
"acc_norm": 0.3084047002589126,
"acc_norm_stderr": 0.004608907872957696
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.03673731683969506,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.03673731683969506
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21132075471698114,
"acc_stderr": 0.025125766484827845,
"acc_norm": 0.21132075471698114,
"acc_norm_stderr": 0.025125766484827845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2361111111111111,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.2361111111111111,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.16,
"acc_stderr": 0.0368452949177471,
"acc_norm": 0.16,
"acc_norm_stderr": 0.0368452949177471
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.25957446808510637,
"acc_stderr": 0.02865917937429232,
"acc_norm": 0.25957446808510637,
"acc_norm_stderr": 0.02865917937429232
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.0404933929774814,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.0404933929774814
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2620689655172414,
"acc_stderr": 0.036646663372252565,
"acc_norm": 0.2620689655172414,
"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.25132275132275134,
"acc_stderr": 0.022340482339643895,
"acc_norm": 0.25132275132275134,
"acc_norm_stderr": 0.022340482339643895
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15079365079365079,
"acc_stderr": 0.03200686497287392,
"acc_norm": 0.15079365079365079,
"acc_norm_stderr": 0.03200686497287392
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.27419354838709675,
"acc_stderr": 0.025378139970885193,
"acc_norm": 0.27419354838709675,
"acc_norm_stderr": 0.025378139970885193
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.15270935960591134,
"acc_stderr": 0.02530890453938063,
"acc_norm": 0.15270935960591134,
"acc_norm_stderr": 0.02530890453938063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.16,
"acc_stderr": 0.0368452949177471,
"acc_norm": 0.16,
"acc_norm_stderr": 0.0368452949177471
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.17676767676767677,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.17676767676767677,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.19689119170984457,
"acc_stderr": 0.028697873971860657,
"acc_norm": 0.19689119170984457,
"acc_norm_stderr": 0.028697873971860657
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2205128205128205,
"acc_stderr": 0.021020672680827912,
"acc_norm": 0.2205128205128205,
"acc_norm_stderr": 0.021020672680827912
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.21481481481481482,
"acc_stderr": 0.02504044387700068,
"acc_norm": 0.21481481481481482,
"acc_norm_stderr": 0.02504044387700068
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.18907563025210083,
"acc_stderr": 0.02543511943810535,
"acc_norm": 0.18907563025210083,
"acc_norm_stderr": 0.02543511943810535
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.18543046357615894,
"acc_stderr": 0.03173284384294285,
"acc_norm": 0.18543046357615894,
"acc_norm_stderr": 0.03173284384294285
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.2036697247706422,
"acc_stderr": 0.017266742087630793,
"acc_norm": 0.2036697247706422,
"acc_norm_stderr": 0.017266742087630793
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.2037037037037037,
"acc_stderr": 0.027467401804057993,
"acc_norm": 0.2037037037037037,
"acc_norm_stderr": 0.027467401804057993
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.030190282453501947,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.030190282453501947
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.25738396624472576,
"acc_stderr": 0.028458820991460305,
"acc_norm": 0.25738396624472576,
"acc_norm_stderr": 0.028458820991460305
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.3094170403587444,
"acc_stderr": 0.031024411740572206,
"acc_norm": 0.3094170403587444,
"acc_norm_stderr": 0.031024411740572206
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2595419847328244,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.2595419847328244,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.042365112580946336,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.042365112580946336
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.26380368098159507,
"acc_stderr": 0.03462419931615623,
"acc_norm": 0.26380368098159507,
"acc_norm_stderr": 0.03462419931615623
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04287858751340456,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04287858751340456
},
"harness|hendrycksTest-management|5": {
"acc": 0.17475728155339806,
"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2264957264957265,
"acc_stderr": 0.027421007295392926,
"acc_norm": 0.2264957264957265,
"acc_norm_stderr": 0.027421007295392926
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.015302380123542094,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.015302380123542094
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.023267528432100174,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.023267528432100174
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23798882681564246,
"acc_stderr": 0.014242630070574915,
"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.02380518652488815,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.02380518652488815
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.1864951768488746,
"acc_stderr": 0.022122439772480768,
"acc_norm": 0.1864951768488746,
"acc_norm_stderr": 0.022122439772480768
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.21604938271604937,
"acc_stderr": 0.022899162918445806,
"acc_norm": 0.21604938271604937,
"acc_norm_stderr": 0.022899162918445806
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23404255319148937,
"acc_stderr": 0.025257861359432414,
"acc_norm": 0.23404255319148937,
"acc_norm_stderr": 0.025257861359432414
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2503259452411995,
"acc_stderr": 0.011064151027165434,
"acc_norm": 0.2503259452411995,
"acc_norm_stderr": 0.011064151027165434
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.40441176470588236,
"acc_stderr": 0.029812630701569743,
"acc_norm": 0.40441176470588236,
"acc_norm_stderr": 0.029812630701569743
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25,
"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03955932861795833,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.24081632653061225,
"acc_stderr": 0.027372942201788163,
"acc_norm": 0.24081632653061225,
"acc_norm_stderr": 0.027372942201788163
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.2736318407960199,
"acc_stderr": 0.03152439186555404,
"acc_norm": 0.2736318407960199,
"acc_norm_stderr": 0.03152439186555404
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-virology|5": {
"acc": 0.28313253012048195,
"acc_stderr": 0.03507295431370518,
"acc_norm": 0.28313253012048195,
"acc_norm_stderr": 0.03507295431370518
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.29239766081871343,
"acc_stderr": 0.03488647713457921,
"acc_norm": 0.29239766081871343,
"acc_norm_stderr": 0.03488647713457921
},
"harness|truthfulqa:mc|0": {
"mc1": 0.24112607099143207,
"mc1_stderr": 0.014974827279752332,
"mc2": 0.4648926603532375,
"mc2_stderr": 0.01559555646787533
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
dmrau/trec_dl20-qrels | ---
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: string
splits:
- name: test
num_bytes: 298319
num_examples: 11386
download_size: 0
dataset_size: 298319
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "trec_dl20-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maxolotl/must-c-en-fr-wait07_22.23 | ---
dataset_info:
features:
- name: current_source
dtype: string
- name: current_target
dtype: string
- name: target_token
dtype: string
splits:
- name: train
num_bytes: 1157797754
num_examples: 5530635
- name: test
num_bytes: 12864307
num_examples: 64317
- name: validation
num_bytes: 6034981
num_examples: 29172
download_size: 182901094
dataset_size: 1176697042
---
# Dataset Card for "must-c-en-fr-wait07_22.23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Bruna1221/RVC_Models_by_Bruna1221 | ---
license: cc-by-2.0
pretty_name: RVC_Models
size_categories:
- n<1K
--- |
polinaeterna/test-windows | ---
builder_config:
data_files:
- split: train
pattern: data/train-*
- split: random
pattern: data/random-*
dataset_info:
features:
- name: x
dtype: int64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 16000
num_examples: 1000
- name: random
num_bytes: 1600
num_examples: 100
download_size: 0
dataset_size: 17600
---
# Dataset Card for "test-windows"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-squad_v2-squad_v2-8571ec-1652758614 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: Palak/microsoft_deberta-base_squad
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: Palak/microsoft_deberta-base_squad
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
DavidVivancos/MindBigData2022_Imagenet_IN_Spct | ---
license: odbl
---
|
Sagar0934/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 0
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DeliberatorArchiver/discography_v2_cdn | ---
license: cc-by-nc-nd-4.0
viewer: false
---
# discography_v2_cdn
Archive of rebuilt music database
Using UUID version 7 ([uuidv7](https://github.com/LiosK/uuidv7))
|
amanteur/CHAD_hummings | ---
license: cc-by-nc-4.0
task_categories:
- feature-extraction
tags:
- music
size_categories:
- 1K<n<10K
viewer: false
---
# CHAD-Hummings Subset
This repository contains the hummings subset of the dataset from ["A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-by-Humming Task"]() (ISMIR 2023).
For the complete dataset and further details, please visit the main [GitHub repository](https://github.com/amanteur/CHAD#hummings).
---
# Overview
The `chad_hummings_subset.tar.gz` archive provided in this repository contains a collection of 5,314 humming audio files.
These audio files are sorted into groups of 693 distinct humming fragments originating from 311 unique songs (groups).
Audio format - `.wav`.
---
# Dataset Structure
Upon extracting the dataset from `chad_hummings_subset.tar.gz`, you will find the following structured hierarchy:
```
├── {GROUP_ID}
│ ├── {FRAGMENT_ID}
│ ├── {ID}.wav
│ └── ...
│ └── ...
└── ...
```
where
- `GROUP_ID` - the unique identifier for each song,
- `FRAGMENT_ID` - the identifier for individual fragments within a song,
- `ID` - the version identifier for a specific fragment of the song.
This structured hierarchy organizes the audio files and fragments, making it easier to navigate and work with the dataset.
---
# Citation
Please cite the following paper if you use the code or dataset provided in this repository.
```bibtex
@inproceedings{Amatov2023,
title={A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-by-Humming Task},
author={Amatov, Amantur and Lamanov, Dmitry and Titov, Maksim and Vovk, Ivan and Makarov, Ilya and Kudinov, Mikhail},
year={2023},
}
``` |
mahdibaghbanzadeh/GUE_splice_reconstructed | ---
dataset_info:
features:
- name: sequence
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
splits:
- name: train
num_bytes: 15036352
num_examples: 36496
- name: val
num_bytes: 1879544
num_examples: 4562
- name: test
num_bytes: 1879544
num_examples: 4562
download_size: 8806003
dataset_size: 18795440
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
Atipico1/mrqa_squad-tqa-sqa | ---
dataset_info:
features:
- name: subset
dtype: string
- name: context
dtype: string
- name: qid
dtype: string
- name: question
dtype: string
- name: detected_answers
struct:
- name: char_spans
list:
- name: end
sequence: int64
- name: start
sequence: int64
- name: text
sequence: string
- name: token_spans
list:
- name: end
sequence: int64
- name: start
sequence: int64
- name: answers
sequence: string
splits:
- name: train
num_bytes: 873312545
num_examples: 265660
download_size: 470656859
dataset_size: 873312545
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
srinathmkce/CarAssistant | ---
license: apache-2.0
---
|
jouyang/clevr_1000 | ---
license: mit
---
|
Atul790/dress-lora3 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 5007966.0
num_examples: 19
download_size: 5009725
dataset_size: 5007966.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bigheiniuJ/EvalMetaICL | ---
dataset_info:
features:
- name: task
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: options
sequence: string
- name: seed
dtype: string
- name: split
dtype: string
splits:
- name: meta_eval
num_bytes: 568291690
num_examples: 984390
- name: meta_eval_100shot
num_bytes: 587900520
num_examples: 1035630
- name: meta_train
num_bytes: 162025836
num_examples: 384022
download_size: 253960910
dataset_size: 1318218046
---
# Dataset Card for "EvalMetaICL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pharaouk/dharma_test2 | ---
configs:
- config_name: default
data_files:
- split: 'dharma_test2_shuffled'
path: final/dharma_test2_eval_shuffled*
- split: 'dharma_test2_unshuffled'
path: final/dharma_test2_eval_unshuffled*
---
# "dharma_test2 Dataset"
A dharma evaluation dataset with the following configuration:
||| Subject: MMLU, Size: 12 |||
||| Subject: ARC-Challenge, Size: 12 |||
||| Subject: ARC-Easy, Size: 12 |||
||| Subject: BoolQ, Size: 12 |||
||| Subject: winogrande, Size: 12 |||
||| Subject: openbookqa, Size: 12 |||
||| Subject: truthful_qa, Size: 12 |||
||| Subject: agieval, Size: 12 |||
Made with https://github.com/pharaouk/dharma 🚀
|
unrealMJ/douyin | ---
license: apache-2.0
---
|
jstet/laouenan-notable-people | ---
license: cc-by-sa-4.0
---
Laouenan, M., Bhargava, P., Eymeoud, J.-B., Gergaud, O., Plique, G., & Wasmer, E. (2023). A Brief History of Human Time - Cross-verified Dataset. data.sciencespo. doi: 10.21410/7E4/RDAG3O |
Eitanli/wine_type | ---
dataset_info:
features:
- name: id
dtype: int64
- name: recipe
dtype: string
- name: wine_type
dtype: string
splits:
- name: train
num_bytes: 110426494
num_examples: 74465
download_size: 54694496
dataset_size: 110426494
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wine_type"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.2 | ---
pretty_name: Evaluation run of INSAIT-Institute/BgGPT-7B-Instruct-v0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [INSAIT-Institute/BgGPT-7B-Instruct-v0.2](https://huggingface.co/INSAIT-Institute/BgGPT-7B-Instruct-v0.2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-03T23:20:48.301798](https://huggingface.co/datasets/open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.2/blob/main/results_2024-03-03T23-20-48.301798.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\": 0.6046331936407989,\n\
\ \"acc_stderr\": 0.032980055225792684,\n \"acc_norm\": 0.608685542837201,\n\
\ \"acc_norm_stderr\": 0.0336435169198773,\n \"mc1\": 0.37576499388004897,\n\
\ \"mc1_stderr\": 0.016954584060214297,\n \"mc2\": 0.5462834271948728,\n\
\ \"mc2_stderr\": 0.015488298895953717\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5614334470989761,\n \"acc_stderr\": 0.014500682618212865,\n\
\ \"acc_norm\": 0.60580204778157,\n \"acc_norm_stderr\": 0.01428052266746732\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6303525194184425,\n\
\ \"acc_stderr\": 0.00481722729224028,\n \"acc_norm\": 0.8218482374029078,\n\
\ \"acc_norm_stderr\": 0.0038185843846355286\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n\
\ \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.5481481481481482,\n\
\ \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\
\ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.038760854559127644\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\"\
: 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n\
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\
\ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\
\ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.30392156862745096,\n \"acc_stderr\": 0.045766654032077615,\n\
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.045766654032077615\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\
\ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7419354838709677,\n\
\ \"acc_stderr\": 0.02489246917246283,\n \"acc_norm\": 0.7419354838709677,\n\
\ \"acc_norm_stderr\": 0.02489246917246283\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015178,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6230769230769231,\n \"acc_stderr\": 0.024570975364225995,\n\
\ \"acc_norm\": 0.6230769230769231,\n \"acc_norm_stderr\": 0.024570975364225995\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \
\ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6260504201680672,\n \"acc_stderr\": 0.03142946637883708,\n \
\ \"acc_norm\": 0.6260504201680672,\n \"acc_norm_stderr\": 0.03142946637883708\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8165137614678899,\n \"acc_stderr\": 0.01659525971039932,\n \"\
acc_norm\": 0.8165137614678899,\n \"acc_norm_stderr\": 0.01659525971039932\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.48148148148148145,\n \"acc_stderr\": 0.03407632093854052,\n \"\
acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.03407632093854052\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640773,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640773\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601443,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601443\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229146,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229146\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677697,\n\
\ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677697\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7520661157024794,\n \"acc_stderr\": 0.03941897526516301,\n \"\
acc_norm\": 0.7520661157024794,\n \"acc_norm_stderr\": 0.03941897526516301\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7879948914431673,\n\
\ \"acc_stderr\": 0.014616099385833671,\n \"acc_norm\": 0.7879948914431673,\n\
\ \"acc_norm_stderr\": 0.014616099385833671\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6502890173410405,\n \"acc_stderr\": 0.025674281456531018,\n\
\ \"acc_norm\": 0.6502890173410405,\n \"acc_norm_stderr\": 0.025674281456531018\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37206703910614525,\n\
\ \"acc_stderr\": 0.0161658475835633,\n \"acc_norm\": 0.37206703910614525,\n\
\ \"acc_norm_stderr\": 0.0161658475835633\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281416,\n\
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281416\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6559485530546624,\n\
\ \"acc_stderr\": 0.02698147804364805,\n \"acc_norm\": 0.6559485530546624,\n\
\ \"acc_norm_stderr\": 0.02698147804364805\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44002607561929596,\n\
\ \"acc_stderr\": 0.012678037478574513,\n \"acc_norm\": 0.44002607561929596,\n\
\ \"acc_norm_stderr\": 0.012678037478574513\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6286764705882353,\n \"acc_stderr\": 0.02934980313976587,\n\
\ \"acc_norm\": 0.6286764705882353,\n \"acc_norm_stderr\": 0.02934980313976587\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6094771241830066,\n \"acc_stderr\": 0.019737008998094597,\n \
\ \"acc_norm\": 0.6094771241830066,\n \"acc_norm_stderr\": 0.019737008998094597\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6816326530612244,\n \"acc_stderr\": 0.029822533793982073,\n\
\ \"acc_norm\": 0.6816326530612244,\n \"acc_norm_stderr\": 0.029822533793982073\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n\
\ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.7711442786069652,\n\
\ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036623,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036623\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \
\ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\
acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.031267817146631786,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.031267817146631786\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37576499388004897,\n\
\ \"mc1_stderr\": 0.016954584060214297,\n \"mc2\": 0.5462834271948728,\n\
\ \"mc2_stderr\": 0.015488298895953717\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.01192000816365088\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44124336618650495,\n \
\ \"acc_stderr\": 0.013677059478592636\n }\n}\n```"
repo_url: https://huggingface.co/INSAIT-Institute/BgGPT-7B-Instruct-v0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|arc:challenge|25_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|gsm8k|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hellaswag|10_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T23-20-48.301798.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-03T23-20-48.301798.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- '**/details_harness|winogrande|5_2024-03-03T23-20-48.301798.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-03T23-20-48.301798.parquet'
- config_name: results
data_files:
- split: 2024_03_03T23_20_48.301798
path:
- results_2024-03-03T23-20-48.301798.parquet
- split: latest
path:
- results_2024-03-03T23-20-48.301798.parquet
---
# Dataset Card for Evaluation run of INSAIT-Institute/BgGPT-7B-Instruct-v0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [INSAIT-Institute/BgGPT-7B-Instruct-v0.2](https://huggingface.co/INSAIT-Institute/BgGPT-7B-Instruct-v0.2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-03T23:20:48.301798](https://huggingface.co/datasets/open-llm-leaderboard/details_INSAIT-Institute__BgGPT-7B-Instruct-v0.2/blob/main/results_2024-03-03T23-20-48.301798.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": 0.6046331936407989,
"acc_stderr": 0.032980055225792684,
"acc_norm": 0.608685542837201,
"acc_norm_stderr": 0.0336435169198773,
"mc1": 0.37576499388004897,
"mc1_stderr": 0.016954584060214297,
"mc2": 0.5462834271948728,
"mc2_stderr": 0.015488298895953717
},
"harness|arc:challenge|25": {
"acc": 0.5614334470989761,
"acc_stderr": 0.014500682618212865,
"acc_norm": 0.60580204778157,
"acc_norm_stderr": 0.01428052266746732
},
"harness|hellaswag|10": {
"acc": 0.6303525194184425,
"acc_stderr": 0.00481722729224028,
"acc_norm": 0.8218482374029078,
"acc_norm_stderr": 0.0038185843846355286
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
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"acc_norm": 0.35,
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},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5481481481481482,
"acc_stderr": 0.04299268905480864,
"acc_norm": 0.5481481481481482,
"acc_norm_stderr": 0.04299268905480864
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
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"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926605
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.6,
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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"acc_norm": 0.6867924528301886,
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},
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},
"harness|hendrycksTest-college_chemistry|5": {
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},
"harness|hendrycksTest-college_computer_science|5": {
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},
"harness|hendrycksTest-college_mathematics|5": {
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},
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},
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},
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},
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},
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},
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6260504201680672,
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8165137614678899,
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"acc_norm": 0.8165137614678899,
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},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.48148148148148145,
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"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.03407632093854052
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
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"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.027599174300640773
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
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"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601443
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6995515695067265,
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"acc_norm_stderr": 0.030769352008229146
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6870229007633588,
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
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},
"harness|hendrycksTest-logical_fallacies|5": {
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},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.41964285714285715,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.7281553398058253,
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"acc_norm_stderr": 0.044052680241409216
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
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},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7879948914431673,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6502890173410405,
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.37206703910614525,
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},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.673202614379085,
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"acc_norm": 0.673202614379085,
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6559485530546624,
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},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7191358024691358,
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},
"harness|hendrycksTest-professional_accounting|5": {
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},
"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5,
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"acc_norm": 0.5,
"acc_norm_stderr": 0.03892494720807614
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7894736842105263,
"acc_stderr": 0.031267817146631786,
"acc_norm": 0.7894736842105263,
"acc_norm_stderr": 0.031267817146631786
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37576499388004897,
"mc1_stderr": 0.016954584060214297,
"mc2": 0.5462834271948728,
"mc2_stderr": 0.015488298895953717
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.01192000816365088
},
"harness|gsm8k|5": {
"acc": 0.44124336618650495,
"acc_stderr": 0.013677059478592636
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
Multimodal-Fatima/Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 102753879.5
num_examples: 6084
- name: fewshot_3_bs_16
num_bytes: 105999857.5
num_examples: 6084
download_size: 193316942
dataset_size: 208753737.0
---
# Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_cola_will_would | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 3702
num_examples: 41
- name: test
num_bytes: 3560
num_examples: 39
- name: train
num_bytes: 30271
num_examples: 370
download_size: 21681
dataset_size: 37533
---
# Dataset Card for "MULTI_VALUE_cola_will_would"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/bubble_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of bubble/バブル/泡泡 (Arknights)
This is the dataset of bubble/バブル/泡泡 (Arknights), containing 17 images and their tags.
The core tags of this character are `brown_hair, long_hair, horns, single_horn, ponytail, animal_ears, bow, hair_ornament, hair_bow, horse_ears, hairclip, green_eyes, horse_girl`, 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 | 17 | 18.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bubble_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 17 | 16.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bubble_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 39 | 32.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/bubble_arknights/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/bubble_arknights',
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 | 17 |  |  |  |  |  | 1girl, solo, smile, open_mouth, armor, looking_at_viewer, gloves, white_background, blush, full_body, holding |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | open_mouth | armor | looking_at_viewer | gloves | white_background | blush | full_body | holding |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:-------------|:--------|:--------------------|:---------|:-------------------|:--------|:------------|:----------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X |
|
autoevaluate/autoeval-eval-acronym_identification-default-d87697-95015146250 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- acronym_identification
eval_info:
task: entity_extraction
model: lewtun/autotrain-acronym-identification-7324788
metrics: ['code_eval', 'lvwerra/ai4code']
dataset_name: acronym_identification
dataset_config: default
dataset_split: train
col_mapping:
tokens: tokens
tags: labels
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ebinum](https://huggingface.co/ebinum) for evaluating this model. |
japanese-asr/whisper_transcriptions.reazonspeech.all_51 | ---
dataset_info:
config_name: all
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 30423889983.0
num_examples: 267358
download_size: 30186859287
dataset_size: 30423889983.0
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
---
|
MohamedSaeed-dev/PyCode | ---
license: llama2
---
|
ocolegro/ts_train | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 23751416
num_examples: 11414
download_size: 8011655
dataset_size: 23751416
---
# Dataset Card for "ts_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/sugar-ray | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sugar-ray"
## 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:** 0.164888 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/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sugar-ray">
<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">Sugar Ray</div>
<a href="https://genius.com/artists/sugar-ray">
<div style="text-align: center; font-size: 14px;">@sugar-ray</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/sugar-ray).
### 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/sugar-ray")
```
## 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|
|------:|---------:|---:|
|117| -| -|
'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/sugar-ray")
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)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
liuyanchen1015/MULTI_VALUE_mnli_regularized_reflexives | ---
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: 19290
num_examples: 94
- name: dev_mismatched
num_bytes: 23196
num_examples: 87
- name: test_matched
num_bytes: 21638
num_examples: 90
- name: test_mismatched
num_bytes: 20725
num_examples: 82
- name: train
num_bytes: 938071
num_examples: 3883
download_size: 579285
dataset_size: 1022920
---
# Dataset Card for "MULTI_VALUE_mnli_regularized_reflexives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xenon3134-mc/empty-eyes-dataset | ---
license: mit
size_categories:
- n<1K
---
A dataset of AI-generated images or images modified from them.
Products using this dataset
- [empty-eyes-LoRAs](https://huggingface.co/xenon3134-mc/empty-eyes-LoRAs) |
atmallen/quirky_popqa_pythia-410m_alice_easy | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: popularity
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 956505.0212765958
num_examples: 6132
- name: validation
num_bytes: 72149.154
num_examples: 462
- name: test
num_bytes: 76436.57
num_examples: 490
download_size: 402157
dataset_size: 1105090.7452765957
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
neoALI/layout-detector-flagged-samples | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Marcos7fytyg/Dataset.Pain | ---
license: apache-2.0
---
|
k-seungri/k_whisper_dataset_prepocessing | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 99896792
num_examples: 104
- name: test
num_bytes: 13447160
num_examples: 14
- name: valid
num_bytes: 12487928
num_examples: 13
download_size: 18434628
dataset_size: 125831880
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
CATIE-AQ/orange_sum_fr_prompt_text_generation_from_title_of_an_article | ---
language:
- fr
license: cc-by-sa-4.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
tags:
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- orange_sum
---
# orange_sum_fr_prompt_text_generation_from_title_of_an_article
## Summary
**orange_sum_fr_prompt_text_generation_from_title_of_an_article** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **908,793** rows that can be used for a part-of-speech task.
The original data (without prompts) comes from the dataset [orange_sum](https://huggingface.co/datasets/orange_sum) by Eddine et al.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
27 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
'Rédiger un texte dont le titre est : "'+title+'".',
'Rédige un texte dont le titre est : "'+title+'".',
'Rédigez un texte dont le titre est : "'+title+'".',
'Rédiger une article dont le titre est : "'+title+'".',
'Rédige un article dont le titre est : "'+title+'".',
'Rédigez un article dont le titre est : "'+title+'".',
'Rédiger un document dont le titre est : "'+title+'".',
'Rédige un document dont le titre est : "'+title+'".',
'Rédigez un document dont le titre est : "'+title+'".',
‘Génèrer un texte dont le titre est : "'+title+'".\nTexte : ',
'Génère un texte dont le titre est : "'+title+'".\nTexte : ',
‘Génèrez un texte dont le titre est : "'+title+'".\nTexte : ',
‘Génèrer un article dont le titre est : "'+title+'".\nArticle : ',
‘Génère un article dont le titre est : "'+title+'".\nArticle : ',
‘Génèrez un article dont le titre est : "'+title+'".\nArticle : ',
‘Génèrer un document dont le titre est : "'+title+'".\nDocument : ',
'Génère un document dont le titre est : "'+title+'".\nDocument : ',
‘Génèrez un document dont le titre est : "'+title+'".\nDocument : ',
'"'+title +'"\n Ecrire un texte de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecris un texte de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecrivez un texte de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecrire un article de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecris un article de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecrivez un article de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecrire un document de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecris un document de 1 à 5 phrases sur le titre précédent : ',
'"'+title +'"\n Ecrivez un document de 1 à 5 phrases sur le titre précédent : '
```
### Features used in the prompts
In the prompt list above, `title` and `targets` have been constructed from:
```
orange_sum = load_dataset('orange_sum','title')
title = orange_sum['train'][i]['summary']
targets = orange_sum['train'][i]['text']
```
# Splits
- `train` with 827,793 samples
- `valid` with 40,500 samples
- `test` with 40,500 samples
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/orange_sum_fr_prompt_text_generation_from_title_of_an_article")
```
# Citation
## Original data
> @article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
journal={arXiv preprint arXiv:2010.12321},
year={2020}
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
CC-BY-SA-4.0 |
hammondsugar/en-tw | ---
license: mit
---
|
davidho27941/steins_gate_1k_v1.1_conversation | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 115412.4
num_examples: 855
- name: test
num_bytes: 12823.6
num_examples: 95
download_size: 77093
dataset_size: 128236.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
mrm8488/test2 | ---
license: wtfpl
---
|
irds/neumarco_ru_dev_judged | ---
pretty_name: '`neumarco/ru/dev/judged`'
viewer: false
source_datasets: ['irds/neumarco_ru', 'irds/neumarco_ru_dev']
task_categories:
- text-retrieval
---
# Dataset Card for `neumarco/ru/dev/judged`
The `neumarco/ru/dev/judged` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/neumarco#neumarco/ru/dev/judged).
# Data
This dataset provides:
- `queries` (i.e., topics); count=55,578
- For `docs`, use [`irds/neumarco_ru`](https://huggingface.co/datasets/irds/neumarco_ru)
- For `qrels`, use [`irds/neumarco_ru_dev`](https://huggingface.co/datasets/irds/neumarco_ru_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/neumarco_ru_dev_judged', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
Yuhthe/samsum | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 9479117
num_examples: 14732
- name: test
num_bytes: 534480
num_examples: 819
- name: validation
num_bytes: 516419
num_examples: 818
download_size: 6737195
dataset_size: 10530016
task_categories:
- summarization
language:
- vi
---
# Dataset Card for "samsum"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
skrishna/SeqSense_mcq_8 | ---
dataset_info:
features:
- name: input
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 24480
num_examples: 300
download_size: 8809
dataset_size: 24480
---
# Dataset Card for "SeqSense_mcq_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eedu/luangalinha | ---
license: openrail
---
|
HPalaciosMi/sentiment-banking | ---
dataset_info:
features:
- name: text
dtype: string
- name: inputs
struct:
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
dtype: 'null'
- name: annotation_agent
dtype: 'null'
- name: vectors
dtype: 'null'
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
struct:
- name: category
dtype: int64
- name: status
dtype: string
- name: event_timestamp
dtype: timestamp[us]
- name: metrics
dtype: 'null'
splits:
- name: train
num_bytes: 1445808
num_examples: 5001
download_size: 642951
dataset_size: 1445808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/haguro_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of haguro/羽黒/羽黑 (Azur Lane)
This is the dataset of haguro/羽黒/羽黑 (Azur Lane), containing 11 images and their tags.
The core tags of this character are `black_hair, hair_ornament, red_eyes, bangs, earrings, hairclip, breasts, ear_piercing, multicolored_hair, hair_between_eyes, streaked_hair, ponytail, white_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 16.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 8.31 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 28 | 19.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 13.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 28 | 28.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/haguro_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/haguro_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | midriff, 1girl, black_choker, crop_top, jewelry, navel, solo, black_shirt, looking_at_viewer, piercing, short_sleeves, pleated_skirt, belt, black_serafuku, black_skirt, stomach, black_nails, black_sailor_collar, blush, closed_mouth, collarbone, holding, nail_polish, purple_neckerchief, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | midriff | 1girl | black_choker | crop_top | jewelry | navel | solo | black_shirt | looking_at_viewer | piercing | short_sleeves | pleated_skirt | belt | black_serafuku | black_skirt | stomach | black_nails | black_sailor_collar | blush | closed_mouth | collarbone | holding | nail_polish | purple_neckerchief | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------|:--------|:---------------|:-----------|:----------|:--------|:-------|:--------------|:--------------------|:-----------|:----------------|:----------------|:-------|:-----------------|:--------------|:----------|:--------------|:----------------------|:--------|:---------------|:-------------|:----------|:--------------|:---------------------|:----------|
| 0 | 11 |  |  |  |  |  | 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 |
|
KolyaForger/mangatest | ---
license: afl-3.0
---
|
open-llm-leaderboard/details_nasiruddin15__Mistral-dolphin-2.8-grok-instract-2-7B-slerp | ---
pretty_name: Evaluation run of nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp](https://huggingface.co/nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nasiruddin15__Mistral-dolphin-2.8-grok-instract-2-7B-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-03T01:59:25.249541](https://huggingface.co/datasets/open-llm-leaderboard/details_nasiruddin15__Mistral-dolphin-2.8-grok-instract-2-7B-slerp/blob/main/results_2024-04-03T01-59-25.249541.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\": 0.629995379055331,\n\
\ \"acc_stderr\": 0.032612799341556774,\n \"acc_norm\": 0.6338419302451002,\n\
\ \"acc_norm_stderr\": 0.03326393949397101,\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.0167765996767294,\n \"mc2\": 0.5173783767474156,\n\
\ \"mc2_stderr\": 0.015444454142990593\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5964163822525598,\n \"acc_stderr\": 0.014337158914268438,\n\
\ \"acc_norm\": 0.6390784982935154,\n \"acc_norm_stderr\": 0.014034761386175452\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6509659430392352,\n\
\ \"acc_stderr\": 0.004756905819649977,\n \"acc_norm\": 0.8441545508862777,\n\
\ \"acc_norm_stderr\": 0.003619674864035018\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\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\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\
\ \"acc_stderr\": 0.037143259063020656,\n \"acc_norm\": 0.6127167630057804,\n\
\ \"acc_norm_stderr\": 0.037143259063020656\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7290322580645161,\n \"acc_stderr\": 0.025284416114900156,\n \"\
acc_norm\": 0.7290322580645161,\n \"acc_norm_stderr\": 0.025284416114900156\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.03517603540361008,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.03517603540361008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.02860620428922987,\n \"acc_norm\"\
: 0.797979797979798,\n \"acc_norm_stderr\": 0.02860620428922987\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.8601036269430051,\n \"acc_stderr\": 0.025033870583015178,\n\
\ \"acc_norm\": 0.8601036269430051,\n \"acc_norm_stderr\": 0.025033870583015178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.024537591572830506,\n\
\ \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.024537591572830506\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815642,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815642\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.031041941304059295,\n\
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.031041941304059295\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\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739152,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739152\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389094,\n \
\ \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389094\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n\
\ \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.6681614349775785,\n\
\ \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990948,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990948\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7177914110429447,\n \"acc_stderr\": 0.03536117886664742,\n\
\ \"acc_norm\": 0.7177914110429447,\n \"acc_norm_stderr\": 0.03536117886664742\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.01396439376989913,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.01396439376989913\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\
\ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43575418994413406,\n\
\ \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n\
\ \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.025829163272757482,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.025829163272757482\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.025329888171900926,\n\
\ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.025329888171900926\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4716312056737589,\n \"acc_stderr\": 0.02977945095730307,\n \
\ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.02977945095730307\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n\
\ \"acc_stderr\": 0.012704030518851488,\n \"acc_norm\": 0.4491525423728814,\n\
\ \"acc_norm_stderr\": 0.012704030518851488\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.02850145286039655,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.02850145286039655\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.01904748523936038,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.01904748523936038\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7860696517412935,\n\
\ \"acc_stderr\": 0.02899690969332891,\n \"acc_norm\": 0.7860696517412935,\n\
\ \"acc_norm_stderr\": 0.02899690969332891\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.0167765996767294,\n \"mc2\": 0.5173783767474156,\n\
\ \"mc2_stderr\": 0.015444454142990593\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7821625887924231,\n \"acc_stderr\": 0.011601066079939324\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.48673237300985595,\n \
\ \"acc_stderr\": 0.013767635127026322\n }\n}\n```"
repo_url: https://huggingface.co/nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|arc:challenge|25_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|gsm8k|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hellaswag|10_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T01-59-25.249541.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-03T01-59-25.249541.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- '**/details_harness|winogrande|5_2024-04-03T01-59-25.249541.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-03T01-59-25.249541.parquet'
- config_name: results
data_files:
- split: 2024_04_03T01_59_25.249541
path:
- results_2024-04-03T01-59-25.249541.parquet
- split: latest
path:
- results_2024-04-03T01-59-25.249541.parquet
---
# Dataset Card for Evaluation run of nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp](https://huggingface.co/nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_nasiruddin15__Mistral-dolphin-2.8-grok-instract-2-7B-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-03T01:59:25.249541](https://huggingface.co/datasets/open-llm-leaderboard/details_nasiruddin15__Mistral-dolphin-2.8-grok-instract-2-7B-slerp/blob/main/results_2024-04-03T01-59-25.249541.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": 0.629995379055331,
"acc_stderr": 0.032612799341556774,
"acc_norm": 0.6338419302451002,
"acc_norm_stderr": 0.03326393949397101,
"mc1": 0.3574051407588739,
"mc1_stderr": 0.0167765996767294,
"mc2": 0.5173783767474156,
"mc2_stderr": 0.015444454142990593
},
"harness|arc:challenge|25": {
"acc": 0.5964163822525598,
"acc_stderr": 0.014337158914268438,
"acc_norm": 0.6390784982935154,
"acc_norm_stderr": 0.014034761386175452
},
"harness|hellaswag|10": {
"acc": 0.6509659430392352,
"acc_stderr": 0.004756905819649977,
"acc_norm": 0.8441545508862777,
"acc_norm_stderr": 0.003619674864035018
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"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": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6127167630057804,
"acc_stderr": 0.037143259063020656,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.037143259063020656
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5404255319148936,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.5404255319148936,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.43859649122807015,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
huggingartists/nirvana | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/nirvana"
## 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)
## 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:** 0.336531 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/4c1373962cfc3a668a3e30da9a76a34c.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/nirvana">
<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">Nirvana</div>
<a href="https://genius.com/artists/nirvana">
<div style="text-align: center; font-size: 14px;">@nirvana</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/nirvana).
### 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/nirvana")
```
## 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|
|------:|---------:|---:|
|TRAIN_0.336531| -| -|
'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/nirvana")
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
}
```
|
Eric33/MyDataset_project_1 | ---
license: gpl
---
|
liuyanchen1015/MULTI_VALUE_sst2_corr_conjunction_doubling | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 12080
num_examples: 71
- name: test
num_bytes: 27503
num_examples: 161
- name: train
num_bytes: 234657
num_examples: 1414
download_size: 153416
dataset_size: 274240
---
# Dataset Card for "MULTI_VALUE_sst2_corr_conjunction_doubling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iamkaikai/CUBISM-ART | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 15223404.0
num_examples: 325
download_size: 15207055
dataset_size: 15223404.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CUBISM-ART"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liblinear/russian-paintings-t2i | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 165781456.64100003
num_examples: 1503
download_size: 165228421
dataset_size: 165781456.64100003
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BigTMiami/amazon_25M_500_000_condensed | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 559771932
num_examples: 83949
- name: validation
num_bytes: 55611120
num_examples: 8340
download_size: 196115236
dataset_size: 615383052
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
Partha117/oss_bugs | ---
dataset_info:
features:
- name: status
dtype: string
- name: repo_name
dtype: string
- name: repo_url
dtype: string
- name: issue_id
dtype: int64
- name: updated_files
dtype: string
- name: title
dtype: string
- name: body
dtype: string
- name: issue_url
dtype: string
- name: pull_url
dtype: string
- name: before_fix_sha
dtype: string
- name: after_fix_sha
dtype: string
- name: report_datetime
dtype: timestamp[ns, tz=UTC]
- name: language
dtype: string
- name: commit_datetime
dtype: timestamp[us, tz=UTC]
splits:
- name: train
num_bytes: 78218675
num_examples: 26321
download_size: 27477501
dataset_size: 78218675
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oss_bugs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_wnli_his_he | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 3017
num_examples: 12
- name: test
num_bytes: 8421
num_examples: 28
- name: train
num_bytes: 27142
num_examples: 125
download_size: 22059
dataset_size: 38580
---
# Dataset Card for "MULTI_VALUE_wnli_his_he"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/OK-VQA_test_google_flan_t5_xl_mode_CM_Q_rices_ns_5046 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
sequence: string
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_
num_bytes: 27657536
num_examples: 5046
download_size: 5163191
dataset_size: 27657536
---
# Dataset Card for "OK-VQA_test_google_flan_t5_xl_mode_CM_Q_rices_ns_5046"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abertsch/booksum-fullbooks | ---
dataset_info:
features:
- name: bid
dtype: string
- name: source
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: book
dtype: string
splits:
- name: validation
num_bytes: 23586559
num_examples: 45
- name: train
num_bytes: 165182724
num_examples: 314
- name: test
num_bytes: 31094987
num_examples: 46
download_size: 60336046
dataset_size: 219864270
---
# Dataset Card for "booksum-fullbooks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/d27cefa1 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1341
dataset_size: 188
---
# Dataset Card for "d27cefa1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Saviourscs/Article_Review | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 551346
num_examples: 460
download_size: 318366
dataset_size: 551346
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NickyNicky/aya_dataset_multilingual_inputs_targets_ext9 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: language
dtype: string
- name: language_code
dtype: string
- name: targets_es
dtype: string
- name: targets_en
dtype: string
- name: targets_fr
dtype: string
- name: targets_de
dtype: string
- name: inputs_es
dtype: string
- name: inputs_en
dtype: string
- name: inputs_fr
dtype: string
- name: inputs_de
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3147601
num_examples: 1000
download_size: 2019422
dataset_size: 3147601
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dshut002/Mermaid_LLAMA | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 503
num_examples: 1
download_size: 4922
dataset_size: 503
---
# Dataset Card for "Mermaid_LLAMA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aswin1906/countries-inflation | ---
license: apache-2.0
task_categories:
- tabular-regression
- text-classification
- text-generation
language:
- en
pretty_name: Countries by Inflation rate of 2022
size_categories:
- n<1K
---
# Dataset Summary
Inflation is a critical economic indicator that reflects the overall increase in prices of goods and services within an economy over a specific period. Understanding inflation trends on a global scale is crucial for economists, policymakers, investors, and businesses. This dataset provides comprehensive insights into the inflation rates of various countries for the year 2022. The data is sourced from reputable international organizations and government reports, making it a valuable resource for economic analysis and research.
This dataset includes four essential columns:
1. Countries: The names of countries for which inflation data is recorded. Each row represents a specific country.
1. Inflation, 2022: The inflation rate for each country in the year 2022. Inflation rates are typically expressed as a percentage and indicate the average increase in prices for that year.
1. Global Rank: The rank of each country based on its inflation rate in 2022. Countries with the highest inflation rates will have a lower rank, while those with lower inflation rates will have a higher rank.
1. Available Data: A binary indicator (Yes/No) denoting whether complete and reliable data for inflation in 2022 is available for a particular country. This column helps users identify the data quality and coverage.
## Potential Use Cases
**Economic Analysis:** Researchers and economists can use this dataset to analyze inflation trends globally, identify countries with high or low inflation rates, and make comparisons across regions.
**Investment Decisions:** Investors and financial analysts can incorporate inflation data into their risk assessments and investment strategies.
**Business Planning:** Companies operating in multiple countries can assess the impact of inflation on their costs and pricing strategies, helping them make informed decisions.
## Data Accuracy:
Efforts have been made to ensure the accuracy and reliability of the data; however, users are encouraged to cross-reference this dataset with official sources for critical decision-making processes.
## Updates:
This dataset will be periodically updated to include the latest available inflation data, making it an ongoing resource for tracking global inflation trends. |
timm/imagenet-22k-wds | ---
license: other
license_name: imagenet
license_link: https://www.image-net.org/download.php
task_categories:
- image-classification
pretty_name: ImageNet-22k
size_categories:
- 10M<n<100M
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree to ImageNet Terms of
Access:
[RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the
ImageNet database (the "Database") at Princeton University and Stanford
University. In exchange for such permission, Researcher hereby agrees to the
following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and
educational purposes.
2. Princeton University, Stanford University and Hugging Face make no
representations or warranties regarding the Database, including but not
limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database
and shall defend and indemnify the ImageNet team, Princeton University,
Stanford University and Hugging Face, including their employees, Trustees,
officers and agents, against any and all claims arising from Researcher's use
of the Database, including but not limited to Researcher's use of any copies
of copyrighted images that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to
the Database provided that they first agree to be bound by these terms and
conditions.
5. Princeton University, Stanford University and Hugging Face reserve the
right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's
employer shall also be bound by these terms and conditions, and Researcher
hereby represents that he or she is fully authorized to enter into this
agreement on behalf of such employer.
7. The law of the State of New Jersey shall apply to all disputes under this
agreement.
tags:
- webdataset
---
## Dataset Description
- **Homepage:** https://image-net.org/index.php
- **Repository:** https://github.com/rwightman/imagenet-12k
- **Paper:** https://arxiv.org/abs/1409.0575
### Dataset Summary
This is a copy of the full [ImageNet](https://www.image-net.org/) dataset consisting of all of the original 21841 clases. It also contains labels in a separate field for the '12k' subset described at at (https://github.com/rwightman/imagenet-12k, https://huggingface.co/datasets/timm/imagenet-12k-wds)
This dataset is from the original `fall11` ImageNet release which has been replaced by the `winter21` release which removes close to 3000 synsets containing people, a number of these are of an offensive or sensitive nature. There is work in progress to filter a similar dataset from `winter21`, and there is already [ImageNet-21k-P](https://github.com/Alibaba-MIIL/ImageNet21K/blob/main/dataset_preprocessing/processing_instructions.md) but with different thresholds & preprocessing steps.
### Data Splits
Unlike ImageNet-1k (ILSVRC 2012), the full ImageNet dataset has no defined splits.
This instance does include a randomly selected validation split consiting of 40 samples for the 11821 classes in ImageNet-12k. The validation split is the exact same as https://huggingface.co/datasets/timm/imagenet-12k-wds and does not fully cover all 22k classes. Beyond the 12k classes (sorted by # samples), the remaining have very few samples per-class. ImageNet-22k is not a balanced dataset.
#### Train
* `imagenet22k-train-{0000..4095}.tar`
* 13673551 samples over 4095 shards
#### Validation
* `imagenet22k-validation-{0000..0511}.tar`
* 472840 samples over 512 shards
### Processing
I performed some processing while sharding this dataset:
* All exif tags not related to color space were removed
* All images with width or height < 48 were removed.
* All images with the smallest edge > 600 were resized, maintaining aspect so that they were = 600. Improving size & decoding time uniformity for typical pretrain use cases.
* Images were pre-shuffled across the shards
## Additional Information
### Dataset Curators
Authors of [[1]](https://arxiv.org/abs/1409.0575) and [[2]](https://ieeexplore.ieee.org/abstract/document/5206848):
- Olga Russakovsky
- Jia Deng
- Hao Su
- Jonathan Krause
- Sanjeev Satheesh
- Wei Dong
- Richard Socher
- Li-Jia Li
- Kai Li
- Sean Ma
- Zhiheng Huang
- Andrej Karpathy
- Aditya Khosla
- Michael Bernstein
- Alexander C Berg
- Li Fei-Fei
### Licensing Information
In exchange for permission to use the ImageNet database (the "Database") at Princeton University and Stanford University, Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
1. Princeton University and Stanford University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
1. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the ImageNet team, Princeton University, and Stanford University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted images that he or she may create from the Database.
1. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
1. Princeton University and Stanford University reserve the right to terminate Researcher's access to the Database at any time.
1. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.
1. The law of the State of New Jersey shall apply to all disputes under this agreement.
### Citation Information
```bibtex
@article{imagenet15russakovsky,
Author = {Olga Russakovsky and Jia Deng and Hao Su and Jonathan Krause and Sanjeev Satheesh and Sean Ma and Zhiheng Huang and Andrej Karpathy and Aditya Khosla and Michael Bernstein and Alexander C. Berg and Li Fei-Fei},
Title = { {ImageNet Large Scale Visual Recognition Challenge} },
Year = {2015},
journal = {International Journal of Computer Vision (IJCV)},
doi = {10.1007/s11263-015-0816-y},
volume={115},
number={3},
pages={211-252}
}
``` |
DUOMO-Lab/TransGPT-sft | ---
license: apache-2.0
---
|
namphan410/Test | ---
license: unknown
---
|
erickdp/autotrain-data-tweet-es-sent | ---
task_categories:
- text-classification
---
# AutoTrain Dataset for project: tweet-es-sent
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tweet-es-sent.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"target": 1,
"text": "1sola vuelta! arauz presidente! 1sola vuelta! todo 1 1sola la 1 es ecdor! por ti!1 por 1 los tuyos!1 por nosotros juntos1 mas de 45 d apoyo popular el 7 se vota 1por la vida por el futuro,por la esperanza guayaquil ec dor es 1"
},
{
"target": 1,
"text": "excelente decisi\u00f3n , las mujeres son importantes y por esa raz\u00f3n, a productos de primera necesidad hay que quitarles el iva "
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(num_classes=3, names=['0', '1', '2'], id=None)",
"text": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 12400 |
| valid | 3685 |
|
sharad36/beat | ---
license: afl-3.0
---
|
dgblife/detection_clp | ---
license: other
---
|
open-llm-leaderboard/details_TwT-6__open_llm_leaderboard_demo2 | ---
pretty_name: Evaluation run of TwT-6/open_llm_leaderboard_demo2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TwT-6/open_llm_leaderboard_demo2](https://huggingface.co/TwT-6/open_llm_leaderboard_demo2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TwT-6__open_llm_leaderboard_demo2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-15T16:29:36.546610](https://huggingface.co/datasets/open-llm-leaderboard/details_TwT-6__open_llm_leaderboard_demo2/blob/main/results_2024-04-15T16-29-36.546610.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\": 0.6463960806225129,\n\
\ \"acc_stderr\": 0.03156272465458696,\n \"acc_norm\": 0.6590262499023557,\n\
\ \"acc_norm_stderr\": 0.03241582471085193,\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5249573452382528,\n\
\ \"mc2_stderr\": 0.015222313755138895\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520767,\n\
\ \"acc_norm\": 0.6237201365187713,\n \"acc_norm_stderr\": 0.014157022555407156\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6414060944035053,\n\
\ \"acc_stderr\": 0.004786075107572191,\n \"acc_norm\": 0.8375821549492133,\n\
\ \"acc_norm_stderr\": 0.0036807989505319135\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361074,\n\
\ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361074\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\
\ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4656084656084656,\n \"acc_stderr\": 0.025690321762493838,\n \"\
acc_norm\": 0.4656084656084656,\n \"acc_norm_stderr\": 0.025690321762493838\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8064516129032258,\n \"acc_stderr\": 0.022475258525536057,\n \"\
acc_norm\": 0.8064516129032258,\n \"acc_norm_stderr\": 0.022475258525536057\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876105,\n \"\
acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8363636363636363,\n \"acc_stderr\": 0.02888787239548795,\n\
\ \"acc_norm\": 0.8363636363636363,\n \"acc_norm_stderr\": 0.02888787239548795\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298902,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298902\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402538,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402538\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35555555555555557,\n \"acc_stderr\": 0.029185714949857406,\n \
\ \"acc_norm\": 0.35555555555555557,\n \"acc_norm_stderr\": 0.029185714949857406\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\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461763,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461763\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8676470588235294,\n \"acc_stderr\": 0.023784297520918853,\n \"\
acc_norm\": 0.8676470588235294,\n \"acc_norm_stderr\": 0.023784297520918853\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8438818565400844,\n \"acc_stderr\": 0.023627159460318677,\n \
\ \"acc_norm\": 0.8438818565400844,\n \"acc_norm_stderr\": 0.023627159460318677\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057222,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057222\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990945,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990945\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\
\ \"acc_stderr\": 0.013468201614066302,\n \"acc_norm\": 0.8288633461047255,\n\
\ \"acc_norm_stderr\": 0.013468201614066302\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.02353292543104429,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.02353292543104429\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.38324022346368714,\n\
\ \"acc_stderr\": 0.016260159604429128,\n \"acc_norm\": 0.38324022346368714,\n\
\ \"acc_norm_stderr\": 0.016260159604429128\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729487,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729487\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188936,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188936\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135118,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135118\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.48891786179921776,\n \"acc_stderr\": 0.012767098998525834,\n\
\ \"acc_norm\": 0.48891786179921776,\n \"acc_norm_stderr\": 0.012767098998525834\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.7426470588235294,\n \"acc_stderr\": 0.026556519470041496,\n \"\
acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.026556519470041496\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6879084967320261,\n \"acc_stderr\": 0.01874501120127766,\n \
\ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.01874501120127766\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.02797982353874455,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.02797982353874455\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088376,\n \"mc2\": 0.5249573452382528,\n\
\ \"mc2_stderr\": 0.015222313755138895\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7924230465666929,\n \"acc_stderr\": 0.011398593419386795\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/TwT-6/open_llm_leaderboard_demo2
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_04_15T16_29_36.546610
path:
- '**/details_harness|arc:challenge|25_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|gsm8k|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hellaswag|10_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-29-36.546610.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-15T16-29-36.546610.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- '**/details_harness|winogrande|5_2024-04-15T16-29-36.546610.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-15T16-29-36.546610.parquet'
- config_name: results
data_files:
- split: 2024_04_15T16_29_36.546610
path:
- results_2024-04-15T16-29-36.546610.parquet
- split: latest
path:
- results_2024-04-15T16-29-36.546610.parquet
---
# Dataset Card for Evaluation run of TwT-6/open_llm_leaderboard_demo2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TwT-6/open_llm_leaderboard_demo2](https://huggingface.co/TwT-6/open_llm_leaderboard_demo2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TwT-6__open_llm_leaderboard_demo2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-15T16:29:36.546610](https://huggingface.co/datasets/open-llm-leaderboard/details_TwT-6__open_llm_leaderboard_demo2/blob/main/results_2024-04-15T16-29-36.546610.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": 0.6463960806225129,
"acc_stderr": 0.03156272465458696,
"acc_norm": 0.6590262499023557,
"acc_norm_stderr": 0.03241582471085193,
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088376,
"mc2": 0.5249573452382528,
"mc2_stderr": 0.015222313755138895
},
"harness|arc:challenge|25": {
"acc": 0.5750853242320819,
"acc_stderr": 0.014445698968520767,
"acc_norm": 0.6237201365187713,
"acc_norm_stderr": 0.014157022555407156
},
"harness|hellaswag|10": {
"acc": 0.6414060944035053,
"acc_stderr": 0.004786075107572191,
"acc_norm": 0.8375821549492133,
"acc_norm_stderr": 0.0036807989505319135
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7368421052631579,
"acc_stderr": 0.03583496176361074,
"acc_norm": 0.7368421052631579,
"acc_norm_stderr": 0.03583496176361074
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.02825420034443866,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.02825420034443866
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663434,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663434
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.593103448275862,
"acc_stderr": 0.04093793981266236,
"acc_norm": 0.593103448275862,
"acc_norm_stderr": 0.04093793981266236
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4656084656084656,
"acc_stderr": 0.025690321762493838,
"acc_norm": 0.4656084656084656,
"acc_norm_stderr": 0.025690321762493838
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8064516129032258,
"acc_stderr": 0.022475258525536057,
"acc_norm": 0.8064516129032258,
"acc_norm_stderr": 0.022475258525536057
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4729064039408867,
"acc_stderr": 0.03512819077876105,
"acc_norm": 0.4729064039408867,
"acc_norm_stderr": 0.03512819077876105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8363636363636363,
"acc_stderr": 0.02888787239548795,
"acc_norm": 0.8363636363636363,
"acc_norm_stderr": 0.02888787239548795
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
"acc_stderr": 0.02805779167298902,
"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.02805779167298902
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
"acc_stderr": 0.01989934131572178,
"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402538,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402538
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35555555555555557,
"acc_stderr": 0.029185714949857406,
"acc_norm": 0.35555555555555557,
"acc_norm_stderr": 0.029185714949857406
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8403669724770643,
"acc_stderr": 0.015703498348461763,
"acc_norm": 0.8403669724770643,
"acc_norm_stderr": 0.015703498348461763
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.033622774366080424,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.033622774366080424
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8676470588235294,
"acc_stderr": 0.023784297520918853,
"acc_norm": 0.8676470588235294,
"acc_norm_stderr": 0.023784297520918853
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8438818565400844,
"acc_stderr": 0.023627159460318677,
"acc_norm": 0.8438818565400844,
"acc_norm_stderr": 0.023627159460318677
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057222,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057222
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.037683359597287434,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990945,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990945
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.03408997886857529,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.03408997886857529
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.03760178006026621,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.03760178006026621
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092368,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092368
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8288633461047255,
"acc_stderr": 0.013468201614066302,
"acc_norm": 0.8288633461047255,
"acc_norm_stderr": 0.013468201614066302
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7427745664739884,
"acc_stderr": 0.02353292543104429,
"acc_norm": 0.7427745664739884,
"acc_norm_stderr": 0.02353292543104429
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.38324022346368714,
"acc_stderr": 0.016260159604429128,
"acc_norm": 0.38324022346368714,
"acc_norm_stderr": 0.016260159604429128
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.025261691219729487,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.025261691219729487
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188936,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188936
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.024477222856135118,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135118
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5,
"acc_stderr": 0.029827499313594685,
"acc_norm": 0.5,
"acc_norm_stderr": 0.029827499313594685
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.48891786179921776,
"acc_stderr": 0.012767098998525834,
"acc_norm": 0.48891786179921776,
"acc_norm_stderr": 0.012767098998525834
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7426470588235294,
"acc_stderr": 0.026556519470041496,
"acc_norm": 0.7426470588235294,
"acc_norm_stderr": 0.026556519470041496
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6879084967320261,
"acc_stderr": 0.01874501120127766,
"acc_norm": 0.6879084967320261,
"acc_norm_stderr": 0.01874501120127766
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7428571428571429,
"acc_stderr": 0.02797982353874455,
"acc_norm": 0.7428571428571429,
"acc_norm_stderr": 0.02797982353874455
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.025196929874827072,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827072
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352202,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352202
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685515,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088376,
"mc2": 0.5249573452382528,
"mc2_stderr": 0.015222313755138895
},
"harness|winogrande|5": {
"acc": 0.7924230465666929,
"acc_stderr": 0.011398593419386795
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
yzhuang/autotree_automl_bank-marketing_sgosdt_l256_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 174960000
num_examples: 10000
- name: validation
num_bytes: 174960000
num_examples: 10000
download_size: 72788389
dataset_size: 349920000
---
# Dataset Card for "autotree_automl_bank-marketing_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilabel-internal-testing/deita-after-conversation | ---
dataset_info:
features:
- name: evolved_instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: 'null'
- name: motivation_app
dtype: 'null'
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
- name: answer
dtype: string
- name: model_name
dtype: string
- name: evol_instruction_score
dtype: float64
- name: evolved_response
dtype: string
- name: evol_response_score
dtype: float64
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 6923587
num_examples: 1800
download_size: 1022792
dataset_size: 6923587
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dclure/laion-aesthetics-12m-umap | ---
annotations_creators: []
language:
- en
language_creators:
- found
license:
- mit
multilinguality:
- monolingual
pretty_name: laion-aesthetics-12m-umap
size_categories: []
source_datasets: []
tags:
- laion
- stable-diffuson
- text2img
task_categories: []
task_ids: []
---
# LAION-Aesthetics :: CLIP → UMAP
This dataset is a CLIP (text) → UMAP embedding of the [LAION-Aesthetics dataset](https://laion.ai/blog/laion-aesthetics/) - specifically the [`improved_aesthetics_6plus` version](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus), which filters the full dataset to images with scores of > 6 under the "aesthetic" filtering model.
Thanks LAION for this amazing corpus!
---
The dataset here includes coordinates for 3x separate UMAP fits using different values for the `n_neighbors` parameter - `10`, `30`, and `60` - which are broken out as separate columns with different suffixes:
- `n_neighbors=10` → (`x_nn10`, `y_nn10`)
- `n_neighbors=30` → (`x_nn30`, `y_nn30`)
- `n_neighbors=60` → (`x_nn60`, `y_nn60`)
### `nn10`
### `nn30`
### `nn60`
(The version from [Twitter](https://twitter.com/clured/status/1565399157606580224).)
## Pipeline
The script for producing this can be found here:
https://github.com/davidmcclure/loam-viz/blob/laion/laion.py
And is very simple - just using the `openai/clip-vit-base-patch32` model out-of-the-box to encode the text captions:
```python
@app.command()
def clip(
src: str,
dst: str,
text_col: str = 'TEXT',
limit: Optional[int] = typer.Option(None),
batch_size: int = typer.Option(512),
):
"""Embed with CLIP."""
df = pd.read_parquet(src)
if limit:
df = df.head(limit)
tokenizer = CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32')
model = CLIPTextModel.from_pretrained('openai/clip-vit-base-patch32')
model = model.to(device)
texts = df[text_col].tolist()
embeds = []
for batch in chunked_iter(tqdm(texts), batch_size):
enc = tokenizer(
batch,
return_tensors='pt',
padding=True,
truncation=True,
)
enc = enc.to(device)
with torch.no_grad():
res = model(**enc)
embeds.append(res.pooler_output.to('cpu'))
embeds = torch.cat(embeds).numpy()
np.save(dst, embeds)
print(embeds.shape)
```
Then using `cuml.GaussianRandomProjection` to do an initial squeeze to 64d (which gets the embedding tensor small enough to fit onto a single GPU for the UMAP) -
```python
@app.command()
def random_projection(src: str, dst: str, dim: int = 64):
"""Random projection on an embedding matrix."""
rmm.reinitialize(managed_memory=True)
embeds = np.load(src)
rp = cuml.GaussianRandomProjection(n_components=dim)
embeds = rp.fit_transform(embeds)
np.save(dst, embeds)
print(embeds.shape)
```
And then `cuml.UMAP` to get from 64d -> 2d -
```python
@app.command()
def umap(
df_src: str,
embeds_src: str,
dst: str,
n_neighbors: int = typer.Option(30),
n_epochs: int = typer.Option(1000),
negative_sample_rate: int = typer.Option(20),
):
"""UMAP to 2d."""
rmm.reinitialize(managed_memory=True)
df = pd.read_parquet(df_src)
embeds = np.load(embeds_src)
embeds = embeds.astype('float16')
print(embeds.shape)
print(embeds.dtype)
reducer = cuml.UMAP(
n_neighbors=n_neighbors,
n_epochs=n_epochs,
negative_sample_rate=negative_sample_rate,
verbose=True,
)
x = reducer.fit_transform(embeds)
df['x'] = x[:,0]
df['y'] = x[:,1]
df.to_parquet(dst)
print(df)
``` |
Ali-C137/Arabic_guanaco_oasst1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 20962143
num_examples: 9846
- name: test
num_bytes: 1102534
num_examples: 518
download_size: 10417464
dataset_size: 22064677
license: apache-2.0
language:
- ar
size_categories:
- 1K<n<10K
---
# Dataset Card for "Arabic_guanaco_oasst1"
This dataset is the openassistant-guanaco dataset a subset of the Open Assistant dataset translated to Arabic.
You can find the original dataset here: https://huggingface.co/datasets/timdettmers/openassistant-guanaco
Or the main dataset here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main
This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
For further information, please see the main dataset.
License: Apache 2.0
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_wnli_completive_done | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 2794
num_examples: 12
- name: test
num_bytes: 16167
num_examples: 57
- name: train
num_bytes: 29881
num_examples: 129
download_size: 23959
dataset_size: 48842
---
# Dataset Card for "MULTI_VALUE_wnli_completive_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_70m_thr_0.1_seed_1 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: chosen
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num_examples: 18929
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num_examples: 18929
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num_examples: 18929
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num_examples: 18929
download_size: 699430221
dataset_size: 1327519374
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
jilp00/youtoks-curious-amalgam-v2-chatml | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6377681
num_examples: 9358
download_size: 2582238
dataset_size: 6377681
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cwiz/igor-gofman-text | ---
license: apache-2.0
---
Полный корпус изречений и постов Игоря Гофмана |
peter2000/ecoicop_online_product | ---
license: cc
task_categories:
- text-classification
language:
- de
- fr
- it
size_categories:
- 10K<n<100K
--- |
dmrau/cqadupstack-tex | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: queries
num_bytes: 186934
num_examples: 2906
- name: corpus
num_bytes: 86600423
num_examples: 68184
download_size: 43424126
dataset_size: 86787357
---
# Dataset Card for "cqadupstack-tex"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Abirami/tamilwikipedia | ---
license: other
---
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_1.4b_bo2_100_kl_0.1_prm_410m_thr_0.3_seed_2 | ---
dataset_info:
config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
features:
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dtype: string
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num_bytes: 43615944
num_examples: 18929
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num_bytes: 43610500
num_examples: 18929
download_size: 231996117
dataset_size: 436687301
configs:
- config_name: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500
data_files:
- split: epoch_0
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_0-*
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path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_1-*
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path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_2-*
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path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_3-*
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path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_4-*
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path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia-1.4b_alpaca_farm_instructions_sft_constant_pa-checkpoint-7500/epoch_9-*
---
|
Ngat/NgatNang | ---
license: creativeml-openrail-m
---
|
Shoubhik8/mpt_finetune_dataset | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 331283580
num_examples: 371277
download_size: 13534489
dataset_size: 331283580
---
# Dataset Card for "mpt_finetune_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hczhu/TickerTick-stock-news | ---
license: mit
---
https://github.com/hczhu/TickerTick-API/releases |
Zombely/fiszki-ocr-train | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 354017910.0
num_examples: 85
- name: validation
num_bytes: 56459717.0
num_examples: 14
download_size: 410390428
dataset_size: 410477627.0
---
# Dataset Card for "fiszki-ocr-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
luona/datasetplayground | ---
license: apache-2.0
---
|
mbarnig/Tatoeba-en-lb | ---
license: cc-by-nc-sa-4.0
---
|
naorm/all-captions-screen2words-16bit-blip2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: hf-blip2-16bit
dtype: string
- name: hf-blip2-coco-16bit
dtype: string
splits:
- name: train
num_bytes: 448040117.41
num_examples: 4310
download_size: 362303052
dataset_size: 448040117.41
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rcds/swiss_rulings | ---
license: cc-by-sa-4.0
language:
- it
- de
- fr
pretty_name: Swiss Rulings
size_categories:
- 100K<n<1M
---
# Dataset Card for Swiss Rulings
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
SwissRulings is a multilingual, diachronic dataset of 637K Swiss Federal Supreme Court (FSCS) cases. This dataset can be used to pretrain language models on Swiss legal data.
### Supported Tasks and Leaderboards
### Languages
Switzerland has four official languages with three languages German, French and Italian being represenated. The decisions are written by the judges and clerks in the language of the proceedings.
| Language | Subset | Number of Documents Full |
|------------|------------|--------------------------|
| German | **de** | 319K |
| French | **fr** | 246K |
| Italian | **it** | 71K |
## Dataset Structure
### Data Fields
```
decision_id (string)
facts (string)
considerations (string)
origin_facts (string)
origin_considerations (string)
law_area (string)
language (string)
year (int32)
court (string)
chamber (string)
canton (string)
region (string)
```
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
#### Who are the annotators?
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
Please cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)
```
@misc{rasiah2023scale,
title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation},
author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},
year={2023},
eprint={2306.09237},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions |
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