datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
aisuko/quora_duplicate_questions | ---
license: mit
language:
- en
---
Adapter by: Aisuko
Only for researching.
|
CyberHarem/illya_coral_tenseioujototensaireijounomahoukakumei | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Illya Coral
This is the dataset of Illya Coral, containing 117 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 117 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 262 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 117 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 117 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 117 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 117 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 117 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 262 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 262 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 262 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
andersonbcdefg/llm-tones | ---
dataset_info:
features:
- name: assistant_message
dtype: string
- name: tone
dtype: string
- name: moralizing
dtype: bool
splits:
- name: train
num_bytes: 79808474
num_examples: 99557
download_size: 45735512
dataset_size: 79808474
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
philikai/Spider-SQL-LLAMA2_train | ---
dataset_info:
features:
- name: db_id
dtype: string
- name: query
dtype: string
- name: question
dtype: string
- name: schema
dtype: string
- name: primary_keys
dtype: string
- name: foreign_keys
dtype: string
splits:
- name: train
num_bytes: 12713675
num_examples: 8659
- name: validation
num_bytes: 1169610
num_examples: 1034
download_size: 619836
dataset_size: 13883285
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
license: cc-by-sa-4.0
size_categories:
- 1K<n<10K
---
# Dataset Card for "Spider-SQL-LLAMA2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sachin7/HomeTeamPredictiondataset | ---
dataset_info:
features:
- name: final
dtype: string
splits:
- name: train
num_bytes: 3033286.2
num_examples: 29162
- name: test
num_bytes: 1299979.8
num_examples: 12498
download_size: 1133978
dataset_size: 4333266.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
juanArevalo/autotrain-data-classificacion | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: classificacion
## Dataset Description
This dataset has been automatically processed by AutoTrain for project classificacion.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<511x511 RGBA PIL image>",
"target": 4
},
{
"image": "<511x511 RGBA PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Ak', 'Ala_Idris', 'Buzgulu', 'Dimnit', 'Nazli'], 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 | 400 |
| valid | 100 |
|
CasperLD/Pizza_Dataset_Detailed | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3789541.0
num_examples: 80
download_size: 3781956
dataset_size: 3789541.0
---
# Dataset Card for "Pizza_Dataset_Detailed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gn03249822/insulin_pen_dataset | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 諾胰保
'1': 諾胰得
splits:
- name: train
num_bytes: 287496503.26086956
num_examples: 117
- name: test
num_bytes: 54706739.739130445
num_examples: 21
download_size: 342220863
dataset_size: 342203243.0
---
# Dataset Card for "insulin_pen_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danjacobellis/audio_har_descript_44kHz_frames_1240_50p | ---
dataset_info:
features:
- name: codes
dtype:
array2_d:
shape:
- 9
- 1240
dtype: float32
- name: label
dtype:
class_label:
names:
'0': No Activity
'1': Writing
'2': Drawing
'3': Cutting paper
'4': Typing on keyboard
'5': Typing on phone
'6': Browsing on phone
'7': Clapping
'8': Shuffling cards
'9': Scratching
'10': Wiping table
'11': Brushing hair
'12': Washing hands
'13': Drinking
'14': Eating snacks
'15': Brushing teeth
'16': Chopping
'17': Grating
'18': Frying
'19': Sweeping
'20': Vacuuming
'21': Washing dishes
'22': Filling water
'23': Using microwave
- name: label_str
dtype: string
- name: participant
dtype: int32
splits:
- name: train
num_bytes: 30311620
num_examples: 678
download_size: 9448705
dataset_size: 30311620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/howe_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of howe/ハウ/豪 (Azur Lane)
This is the dataset of howe/ハウ/豪 (Azur Lane), containing 74 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, large_breasts, blue_eyes, bangs, hair_ornament, braid, earrings, multicolored_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 | 74 | 155.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/howe_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 74 | 73.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/howe_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 185 | 157.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/howe_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 74 | 129.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/howe_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 185 | 250.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/howe_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/howe_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 | 17 |  |  |  |  |  | 1girl, solo, looking_at_viewer, bare_shoulders, hairclip, red_dress, hat, official_alternate_costume, black_pantyhose, blush, plaid_dress, necklace, smile, off_shoulder, black_headwear, handbag, sleeveless, gradient_hair, long_sleeves, nail_polish, open_mouth, red_hair |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, looking_at_viewer, solo, feather_boa, official_alternate_costume, black_dress, closed_mouth, red_dress, sitting, blush, bracelet, crossed_legs, ferret, high_heels, ring, smile, thigh_strap |
| 2 | 14 |  |  |  |  |  | 1girl, maid_headdress, solo, frills, looking_at_viewer, official_alternate_costume, cleavage, very_long_hair, bare_shoulders, black_gloves, detached_sleeves, simple_background, white_apron, white_background, garter_straps, black_thighhighs, holding |
| 3 | 6 |  |  |  |  |  | 1girl, blush, hetero, 1boy, censored, penis, sex, vaginal, breast_grab, faceless_male, grabbing, nipples, nude, open_mouth, solo_focus, straddling, sweat |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | bare_shoulders | hairclip | red_dress | hat | official_alternate_costume | black_pantyhose | blush | plaid_dress | necklace | smile | off_shoulder | black_headwear | handbag | sleeveless | gradient_hair | long_sleeves | nail_polish | open_mouth | red_hair | cleavage | feather_boa | black_dress | closed_mouth | sitting | bracelet | crossed_legs | ferret | high_heels | ring | thigh_strap | maid_headdress | frills | very_long_hair | black_gloves | detached_sleeves | simple_background | white_apron | white_background | garter_straps | black_thighhighs | holding | hetero | 1boy | censored | penis | sex | vaginal | breast_grab | faceless_male | grabbing | nipples | nude | solo_focus | straddling | sweat |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:-----------------|:-----------|:------------|:------|:-----------------------------|:------------------|:--------|:--------------|:-----------|:--------|:---------------|:-----------------|:----------|:-------------|:----------------|:---------------|:--------------|:-------------|:-----------|:-----------|:--------------|:--------------|:---------------|:----------|:-----------|:---------------|:---------|:-------------|:-------|:--------------|:-----------------|:---------|:-----------------|:---------------|:-------------------|:--------------------|:--------------|:-------------------|:----------------|:-------------------|:----------|:---------|:-------|:-----------|:--------|:------|:----------|:--------------|:----------------|:-----------|:----------|:-------|:-------------|:-------------|:--------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | | X | | X | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | X | X | X | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | | | | | | | | X | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
universalner/uner_llm_instructions | ---
license: cc-by-sa-4.0
language:
- ceb
- da
- de
- en
- hr
- pt
- ru
- sk
- sr
- sv
- tl
- zh
task_categories:
- token-classification
dataset_info:
- config_name: en_pud
splits:
- name: test
num_examples: 999
- config_name: pt_pud
splits:
- name: test
num_examples: 999
- config_name: sv_pud
splits:
- name: test
num_examples: 999
- config_name: de_pud
splits:
- name: test
num_examples: 999
- config_name: ru_pud
splits:
- name: test
num_examples: 999
- config_name: zh_pud
splits:
- name: test
num_examples: 999
- config_name: en_ewt
splits:
- name: test
num_examples: 2076
- name: dev
num_examples: 2000
- name: train
num_examples: 12542
- config_name: da_ddt
splits:
- name: test
num_examples: 564
- name: dev
num_examples: 563
- name: train
num_examples: 4382
- config_name: hr_set
splits:
- name: test
num_examples: 1135
- name: dev
num_examples: 959
- name: train
num_examples: 6917
- config_name: sr_set
splits:
- name: test
num_examples: 519
- name: dev
num_examples: 535
- name: train
num_examples: 3327
- config_name: pt_bosque
splits:
- name: test
num_examples: 1166
- name: dev
num_examples: 1171
- name: train
num_examples: 4302
- config_name: sk_snk
splits:
- name: test
num_examples: 1060
- name: dev
num_examples: 1059
- name: train
num_examples: 8482
- config_name: sv_talbanken
splits:
- name: test
num_examples: 1218
- name: dev
num_examples: 503
- name: train
num_examples: 4302
- config_name: tl_trg
splits:
- name: test
num_examples: 127
- config_name: tl_ugnayan
splits:
- name: test
num_examples: 93
- config_name: zh_gsd
splits:
- name: test
num_examples: 499
- name: dev
num_examples: 499
- name: train
num_examples: 3996
- config_name: zh_gsdsimp
splits:
- name: test
num_examples: 499
- name: dev
num_examples: 499
- name: train
num_examples: 3996
---
# Dataset Card for Universal NER v1 in the Aya format
This dataset is a format conversion from its original v1 format into the Aya instruction format and it's released here under the same CC-BY-SA 4.0 license and conditions.
It contains data in multiple languages and this version is intended for multi-lingual LLM construction/tuning.
The dataset contains different subsets and their dev/test/train splits, depending on language.
## Citation
If you utilize this dataset version, feel free to cite/footnote this huggingface dataset repo, but please also cite the original dataset publication.
**BibTeX:**
```
@preprint{mayhew2023universal,
title={{Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark}},
author={Stephen Mayhew and Terra Blevins and Shuheng Liu and Marek Šuppa and Hila Gonen and Joseph Marvin Imperial and Börje F. Karlsson and Peiqin Lin and Nikola Ljubešić and LJ Miranda and Barbara Plank and Arij Riabi and Yuval Pinter},
year={2023},
eprint={2311.09122},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Dataset Details
For the original Universal NER dataset v1 and more details, please check https://huggingface.co/datasets/universalner/universal_ner.
## Format Conversion Details
The templates used to reformat the dataset are in the ./templates-uner directory. |
result-kand2-sdxl-wuerst-karlo/b0d16951 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1367
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b0d16951"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
najju/sign-psl-7b | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Gloss
dtype: string
splits:
- name: train
num_bytes: 218345
num_examples: 3386
download_size: 135244
dataset_size: 218345
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yoinked/lolk | ---
license: agpl-3.0
tags:
- music
dataset_info:
features:
- name: image
dtype: image
- name: audio_file
dtype: string
- name: slice
dtype: int16
splits:
- name: train
num_bytes: 617198096.0
num_examples: 13880
download_size: 616888458
dataset_size: 617198096.0
---
its a tar.gz repo with music lol |
sudarsa/narkeet | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
- name: speaker_id
dtype: int64
splits:
- name: train
num_bytes: 74051474.0
num_examples: 50
download_size: 63160244
dataset_size: 74051474.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T19:55:13.680892](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o/blob/main/results_2023-10-25T19-55-13.680892.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.36294043624161076,\n\
\ \"em_stderr\": 0.004924332274160861,\n \"f1\": 0.4005872483221479,\n\
\ \"f1_stderr\": 0.004835690948801814,\n \"acc\": 0.4514532606737902,\n\
\ \"acc_stderr\": 0.01063773248719955\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.36294043624161076,\n \"em_stderr\": 0.004924332274160861,\n\
\ \"f1\": 0.4005872483221479,\n \"f1_stderr\": 0.004835690948801814\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13495072024260804,\n \
\ \"acc_stderr\": 0.009411315282571166\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827934\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T19_55_13.680892
path:
- '**/details_harness|drop|3_2023-10-25T19-55-13.680892.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T19-55-13.680892.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T19_55_13.680892
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-55-13.680892.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-55-13.680892.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-39-43.645345.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-39-43.645345.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-01T14-39-43.645345.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T19_55_13.680892
path:
- '**/details_harness|winogrande|5_2023-10-25T19-55-13.680892.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T19-55-13.680892.parquet'
- config_name: results
data_files:
- split: 2023_10_01T14_39_43.645345
path:
- results_2023-10-01T14-39-43.645345.parquet
- split: 2023_10_25T19_55_13.680892
path:
- results_2023-10-25T19-55-13.680892.parquet
- split: latest
path:
- results_2023-10-25T19-55-13.680892.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T19:55:13.680892](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE3_3.3w-r16-q_k_v_o/blob/main/results_2023-10-25T19-55-13.680892.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.36294043624161076,
"em_stderr": 0.004924332274160861,
"f1": 0.4005872483221479,
"f1_stderr": 0.004835690948801814,
"acc": 0.4514532606737902,
"acc_stderr": 0.01063773248719955
},
"harness|drop|3": {
"em": 0.36294043624161076,
"em_stderr": 0.004924332274160861,
"f1": 0.4005872483221479,
"f1_stderr": 0.004835690948801814
},
"harness|gsm8k|5": {
"acc": 0.13495072024260804,
"acc_stderr": 0.009411315282571166
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827934
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
k2-fsa/LibriSpeech | ---
license: apache-2.0
---
LibriSpeech is a corpus of approximately 1000 hours of 16kHz read English speech, prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read audiobooks from the LibriVox project, and has been carefully segmented and aligned.
Acoustic models, trained on this data set, are available at [icefall](https://github.com/k2-fsa/icefall/tree/master/egs/librispeech) and language models, suitable for evaluation can be found at [openslr](http://www.openslr.org/11/).
For more information, see the paper "LibriSpeech: an ASR corpus based on public domain audio books", Vassil Panayotov, Guoguo Chen, Daniel Povey and Sanjeev Khudanpur, ICASSP 2015 [pdf](https://www.danielpovey.com/files/2015_icassp_librispeech.pdf) |
Tychema/autotrain-data-ceconomysumdataset | ---
task_categories:
- summarization
---
# AutoTrain Dataset for project: ceconomysumdataset
## Dataset Description
This dataset has been automatically processed by AutoTrain for project ceconomysumdataset.
### 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": "\u9ed8\u6c99\u4e1c\u6536\u8d2d\u5148\u7075\u8446\u96c5\u540e\u5c06\u91cd\u7ec4\u4e3a5\u4e2a\u90e8\u95e8",
"text": "\u65b0\u6d6a\u8d22\u7ecf\u8baf \u5317\u4eac\u65f6\u95f4\u5468\u4e00\u665a\u95f4\u6d88\u606f\uff0c\u9ed8\u6c99\u4e1c\u516c\u53f8(MRK)\u603b\u88c1\u517c\u9996\u5e2d\u6267\u884c\u5b98\u7406\u67e5\u5fb7\u00b7\u514b\u62c9\u514b(Richard T. Clark)\u8868\u793a\uff0c\u5728\u5b8c\u6210\u5bf9\u7ade\u4e89\u5bf9\u624b\u5148\u7075\u8446\u96c5\u516c\u53f8(SGP)411\u4ebf\u7f8e\u5143\u7684\u6536\u8d2d\u540e\uff0c\u8be5\u516c\u53f8\u5c06\u91cd\u7ec4\u4e3a5\u4e2a\u90e8\u95e8\u3002\u514b\u62c9\u514b\u5c06\u7ee7\u7eed\u62c5\u4efb\u65b0\u516c\u53f8\u7684CEO\u3002\u6b64\u9879\u4ea4\u6613\u9884\u8ba1\u5c06\u4e8e\u7b2c\u56db\u5b63\u5ea6\u5b8c\u6210\u3002\u65b0\u516c\u53f8\u5c06\u62e5\u67095\u4e2a\u4e3b\u8981\u90e8\u95e8\uff0c\u5305\u62ec\u5168\u7403\u4eba\u7c7b\u5065\u5eb7(Global Human Health)\u3001\u52a8\u7269\u5065\u5eb7(Animal Health)\u3001\u6d88\u8d39\u8005\u5065\u5eb7\u62a4\u7406(Consumer Health Care)\u3001\u9ed8\u6c99\u4e1c\u7814\u7a76\u5b9e\u9a8c\u5ba4(Merck Research Laboratories)\uff0c\u4ee5\u53ca\u9ed8\u6c99\u4e1c\u5236\u9020\u90e8\u95e8(Merck Manufacturing)\u3002\u6b64\u5916\uff0c\u8fd9\u5bb6\u603b\u90e8\u4f4d\u4e8e\u65b0\u6cfd\u897f\u5ddeWhitehouse Station\u7684\u516c\u53f8\u8868\u793a\uff0c\u5148\u7075\u8446\u96c5\u73b0\u4efb\u9886\u5bfc\u5c42\u5927\u7ea640%\u7684\u6210\u5458\u5c06\u6210\u4e3a\u65b0\u516c\u53f8\u7ba1\u7406\u5c42\u7684\u4e00\u90e8\u5206\uff0c\u800c\u8be5\u516c\u53f8\u5458\u5de5\u4e2d\u7684\u7edd\u5927\u90e8\u5206\u4e5f\u5c06\u7559\u5728\u5408\u5e76\u540e\u7684\u516c\u53f8\u3002\u5168\u7403\u4eba\u7c7b\u5065\u5eb7\u90e8\u95e8\u5c06\u7531\u80af\u5c3c\u65af\u00b7\u5f17\u96f7\u6cfd(Kenneth C. Frazier)\u9886\u5bfc\uff0c\u540e\u8005\u73b0\u4efb\u9ed8\u6c99\u4e1c\u6267\u884c\u526f\u603b\u88c1\u517c\u5168\u7403\u4eba\u7c7b\u5065\u5eb7\u90e8\u95e8\u603b\u88c1\u3002\u5148\u7075\u8446\u96c5\u73b0\u4efb\u9ad8\u7ea7\u526f\u603b\u88c1\u517cIntervet Schering-Plough Animal Health\u90e8\u95e8\u603b\u88c1\u52b3\u5c14\u00b7\u53ef\u6c57(Raul E. Kohan)\u5c06\u9886\u5bfc\u65b0\u7684\u9ed8\u6c99\u4e1c\u52a8\u7269\u5065\u5eb7\u90e8\u95e8\u3002\u6d88\u8d39\u8005\u4fdd\u5065\u90e8\u95e8\u5c06\u6682\u65f6\u7531\u65af\u5766\u5229\u00b7\u5df4\u8c22(Stanley F. Barshay)\u9886\u5bfc\uff0c\u540e\u8005\u73b0\u4efb\u5148\u7075\u8446\u96c5\u6d88\u8d39\u8005\u5065\u5eb7\u90e8\u95e8\u8463\u4e8b\u957f\u3002\u5408\u5e76\u540e\u7684\u516c\u53f8\u5c06\u4e3a\u8be5\u90e8\u95e8\u5bfb\u627e\u4e00\u4f4d\u6b63\u5f0f\u9886\u5bfc\u4eba\u3002\u9ed8\u6c99\u4e1c\u7814\u7a76\u5b9e\u9a8c\u5ba4\u90e8\u95e8\u4ecd\u5c06\u7531\u73b0\u4efb\u603b\u88c1\u5f7c\u5f97\u00b7\u91d1(Peter S. Kim)\u9886\u5bfc\u3002\u9ed8\u6c99\u4e1c\u751f\u4ea7\u90e8\u95e8\u5c06\u7531\u5a01\u5229\u00b7\u8fea\u65af(Willie A. Deese)\u9886\u5bfc\uff0c\u540e\u8005\u73b0\u4efb\u9ed8\u6c99\u4e1c\u751f\u4ea7\u4e1a\u52a1\u603b\u88c1\u3002"
},
{
"target": "\u5927\u76d8\u4e94\u8fde\u9633\u5251\u63072900 \u4e0b\u5468\u8fd0\u884c\u8def\u7ebf\u56fe\u5206\u6790",
"text": "== \u4eca\u65e5\u76d8\u9762\uff1a\u5927\u76d8\u559c\u89c1\u4e94\u8fde\u9633 \u6caa\u6307\u5251\u63072900\u70b9 ==\u5468\u4e94A\u80a1\u7ee7\u7eed\u9707\u8361\u4e0a\u884c\uff0c\u6caa\u6307\u54112900\u70b9\u8fdb\u519b\u3002\u53d7\u9996\u53eaIPO\u843d\u5730\u3001\u56fd\u9645\u6cb9\u4ef7\u7ee7\u7eed\u4e0a\u626c\u3001\u7f8e\u80a1\u9053\u6307\u5fae\u5e45\u6536\u9ad8\u7b49\u56e0\u7d20\u5f71\u54cd\uff0c\u5927\u76d8\u518d\u63a5\u518d\u5389\u53c8\u521b\u53cd\u5f39\u65b0\u9ad8\u3002\u4f46\u80a1\u6307\u4e0a\u884c\u52bf\u5934\u540c\u6bd4\u6628\u65e5\u6709\u6240\u6536\u655b\uff0c\u76d8\u4e2d\u6ce2\u52a8\u52a0\u5267\uff0c\u4e2a\u80a1\u4f9d\u65e7\u662f\u4e24\u6781\u5206\u5316\uff0c\u91d1\u878d\u548c\u751f\u7269\u5236\u836f\u677f\u5757\u7ee7\u7eed\u5145\u5f53\u5e02\u573a\u7684\u9886\u5934\u7f8a\u3002\u800c\u8d44\u6e90\u677f\u5757\u6210\u4e3a\u505a\u7a7a\u7684\u4e3b\u8981\u529b\u91cf\u3002\u622a\u81f3\u6536\u76d8\uff0c\u4e0a\u8bc1\u7efc\u6307\u62a52880.49\u70b9\uff0c\u4e0a\u6da80.93%\uff0c\u76d8\u4e2d\u521b\u51fa2886.50\u70b9\u65b0\u9ad8\uff0c\u6210\u4ea41535\u4ebf\uff1b\u6df1\u8bc1\u6210\u6307\u6536\u5e02\u62a511242.3\u70b9\uff0c\u4e0a\u6da80.81%\uff0c\u6210\u4ea4792.8\u4ebf\u3002\u4e24\u5e02\u5171\u6210\u4ea42327.8\u4ebf\u3002\u540c\u6bd4\u653e\u5927\u7ee7\u7eed\u653e\u5927\u3002== \u76d8\u9762\u5206\u6790\uff1a\u5e02\u573a\u70ed\u70b9\u7ee7\u7eed\u6d3b\u8dc3 \u8d44\u6e90\u7c7b\u677f\u5757\u518d\u6b21\u5012\u6208 ==\u6743\u91cd\u80a1\u4f9d\u7136\u8f83\u4e3a\u5f3a\u52bf\u76d8\u53e3\u663e\u793a\uff0c\u4e2a\u80a1\u5206\u5316\u7684\u8d8b\u52bf\u6ca1\u6709\u6539\u53d8\uff0c\u6743\u91cd\u80a1\u4f9d\u7136\u8f83\u4e3a\u5f3a\u52bf\u3002\u4e07\u79d1\u5927\u6da83.94%\uff0c\u4e2d\u4fe1\u8bc1\u5238\u3001\u6d66\u53d1\u94f6\u884c\u3001\u4e2d\u56fd\u5e73\u5b89\u3001\u4ea4\u901a\u94f6\u884c\u6da8\u5e45\u57281%\u4ee5\u4e0a\uff0c\u77f3\u5316\u53cc\u96c4\u5206\u9053\u626c\u9573\uff0c\u4e2d\u56fd\u77f3\u6cb9\u5fae\u5e45\u6536\u6da80.57%\uff0c\u800c\u4e2d\u56fd\u77f3\u5316\u5219\u4e0b\u8dcc0.48%\u3002\u4e2d\u56fd\u5357\u8f66\u5de8\u91cf\u5c01\u6b7b\u6da8\u505c 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}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "Value(dtype='string', 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 | 151575 |
| valid | 37894 |
|
AdapterOcean/Open_Platypus_standardized_cluster_3_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 7726550
num_examples: 15678
download_size: 0
dataset_size: 7726550
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_3_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Soyoung/HistRED | ---
license: cc-by-nc-nd-4.0
task_categories:
- token-classification
language:
- ko
tags:
- art
size_categories:
- 1K<n<10K
---
This is the official code for **HistRED: A Historical Document-Level Relation Extraction Dataset** (ACL 2023).
All materials related to this paper can be found here.
- [ACL Anthology](https://aclanthology.org/2023.acl-long.180/): Official proceeding publication
- [Virtual-ACL 2023](https://virtual2023.aclweb.org/paper_P536.html#slides): You can view papers, posters, and presentation slides.
- [arXiv](https://arxiv.org/abs/2307.04285): This is the camera-ready version, which is a key part of this paper.
Note that this dataset is open under [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) license.
The same code (except the dataset) can be seen in [Github](https://github.com/dudrrm/HistRED/tree/main)
```python
from datasets import load_dataset
dataset = load_dataset("Soyoung/HistRED")
```
# Dataset Example
Due to the complexity of the dataset, we replace the dataset preview with an example figure.
The text is translated into English for comprehension (*), however, unlike the figure, the dataset does not include English-translated text, only containing Korean and Hanja.
Also, only one relation is shown for readability.
Relation information includes
1. subject and object entities for Korean and Hanja *(sbj_kor, sbj_han, obj_kor, obj_han)*,
2. a relation type *(label)*,
3. and evidence sentence index(es) for each language *(evidence_kor, evidence_han)*.
Metadata contains additional information, such as which book the text is extracted from.
# Corpus of HistRED: \<\< Yeonhaengnok \>\>
In this dataset, we choose *Yeonhaengnok*, a collection of records originally written in Hanja, classical Chinese writing, which has later been translated into Korean.
[Joseon](https://en.wikipedia.org/wiki/Joseon), the last dynastic kingdom of Korea, lasted just over five centuries, from 1392 to 1897, and many aspects of Korean traditions and customs trace their roots back to this era.
Numerous historical documents exist from the Joseon dynasty, including *Annals of Joseon Dynasty* ([AJD](https://en.wikipedia.org/wiki/Veritable_Records_of_the_Joseon_Dynasty)) and *Diaries of the Royal Secretariats* ([DRS](https://en.wikipedia.org/wiki/Seungjeongwon_ilgi)).
Note that the majority of Joseon's records were written in Hanja, the archaic Chinese writing that differs from modern Chinese because the Korean language had not been standardized until much later.
In short, Yeonhaengnok is a travel diary from the Joseon period. In the past, traveling to other places, particularly to foreign countries, was rare.
Therefore, intellectuals who traveled to Chung (also referred to as the [Qing dynasty](https://en.wikipedia.org/wiki/Qing_dynasty)) meticulously documented their journeys, and Yeonhaengnok is a compilation of these accounts.
Diverse individuals from different generations recorded their business trips following similar routes from Joseon to Chung, focusing on people, products, and events they encountered.
The Institute for the Translation of Korean Classics (ITKC) has open-sourced the original and their translated texts for many historical documents, promoting active historical research.
The entire documents were collected from an open-source database at https://db.itkc.or.kr/.
# Properties
- Our dataset contains (i) named entities, (ii) relations between the entities, and (iii) parallel relationships between Korean and Hanja texts.
- <code style="color : red"> dataset.py </code> return processed dataset that can be easily applied to general NLP models.
- For monolingual setting: *KoreanDataset*, *HanjaDataset*
- For Bilingual setting: *JointDataset*
- <code style="color : red"> ner_map.json </code> and <code style="color : red"> label_map.json </code> are the mapping dictionaries from label classes to indexes.
- Sequence level (SL) is a unit of sequence length for extracting self-contained sub-texts without losing context information for each relation in the text. Each folder SL-k indicates that SL is k.
# Dataset usages
- Testbed for evaluating the model performance when varying the sequence length.
- Relation extraction task especially on Non-English or historical corpus.
# Citation
```
@inproceedings{yang-etal-2023-histred,
title = "{H}ist{RED}: A Historical Document-Level Relation Extraction Dataset",
author = "Yang, Soyoung and
Choi, Minseok and
Cho, Youngwoo and
Choo, Jaegul",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.180",
pages = "3207--3224",
}
```
|
AdapterOcean/med_alpaca_standardized_cluster_56_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 31609180
num_examples: 17137
download_size: 16047428
dataset_size: 31609180
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_56_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
euisuh15/sven | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: func_name
dtype: string
- name: code
dtype: string
- name: vul_type
dtype: string
- name: line_changes
dtype: string
- name: char_changes
dtype: string
- name: is_vulnerable
dtype: bool
- name: vul_type_name
dtype: string
- name: vul_type_description
dtype: string
splits:
- name: train
num_bytes: 1960462
num_examples: 688
- name: test
num_bytes: 183840
num_examples: 76
download_size: 383586
dataset_size: 2144302
---
# Dataset Card for "sven"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-xsum-7db0303b-10095338 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: philschmid/distilbart-cnn-12-6-samsum
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: philschmid/distilbart-cnn-12-6-samsum
* Dataset: xsum
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ArjunPrSarkhel](https://huggingface.co/ArjunPrSarkhel) for evaluating this model. |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-74000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 658390
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
xreborn/ds2 | ---
license: apache-2.0
---
|
Woleek/Img2Spec | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: spec
dtype: image
- name: sample_name
dtype: string
splits:
- name: train
num_bytes: 3537373012.5
num_examples: 10738
download_size: 2171045369
dataset_size: 3537373012.5
---
# Dataset Card for "Img2Spec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JackBAI/chatgpt-woi-finetune | ---
license: mit
---
|
Nkumar5/Single | ---
dataset_info:
features:
- name: image
dtype: image
- name: audio_file
dtype: string
- name: slice
dtype: int16
splits:
- name: train
num_bytes: 209273.0
num_examples: 5
download_size: 211200
dataset_size: 209273.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alvations/c4p0-v1-en-ja | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: target_backto_source
dtype: string
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list:
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dtype: string
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dtype: int64
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dtype: int64
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dtype: string
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- name: dataset
dtype: string
- name: source_lang
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- name: target_lang
dtype: string
splits:
- name: train
num_bytes: 22109670
num_examples: 17956
download_size: 8614674
dataset_size: 22109670
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jxm/llama-7b__model__one_million_instructions__reconstructions_sample | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: length
dtype: int64
- name: embedder_input_ids
sequence: int64
- name: embedder_attention_mask
sequence: int64
- name: frozen_embeddings
sequence: float32
- name: idx
dtype: int64
- name: str_original
dtype: string
- name: str_reconstruction
dtype: string
splits:
- name: train
num_bytes: 13289065
num_examples: 100
download_size: 0
dataset_size: 13289065
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama-7b__model__one_million_instructions__reconstructions_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kurianbenoy/malayalam_common_voice_benchmarking | ---
license: mit
---
|
ppxscal/arxiv-metadata-oai-snapshot | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
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dtype: string
- name: submitter
dtype: string
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dtype: string
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dtype: string
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- name: report-no
dtype: string
- name: categories
dtype: string
- name: license
dtype: string
- name: abstract
dtype: string
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dtype: string
- name: authors_parsed
sequence:
sequence: string
splits:
- name: train
num_bytes: 3485474636
num_examples: 2318918
download_size: 1946711718
dataset_size: 3485474636
---
# Dataset Card for "arxiv-metadata-oai-snapshot"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rjaiswal/watches_all_brands | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 17727489.0
num_examples: 846
download_size: 17437046
dataset_size: 17727489.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
IIYOkoiyo/bilireadcv | ---
license: unknown
---
|
jorge-henao/ask2democracy-cfqa-salud-pension | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: topics
sequence: string
splits:
- name: train
num_bytes: 7711587
num_examples: 3805
download_size: 880079
dataset_size: 7711587
---
# Dataset Card for "ask2democracy-cfqa-salud-pension"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Minglii/r_wiz2_qa | ---
dataset_info:
features:
- name: data
struct:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 225921379
num_examples: 73000
download_size: 114043986
dataset_size: 225921379
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "r_wiz2_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
westphal-jan/mnli_matched | ---
source_datasets:
- multi_nli
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
---
## Dataset Description
This dataset provides easier accessibility to the original [MNLI dataset](https://huggingface.co/datasets/multi_nli).
We randomly choose 10% of the original `validation_matched` split and use it as the validation split.
The remaining 90% are used for the test split.
The train split remains unchanged. |
conorcl/portraits3 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 35873206.596
num_examples: 1343
download_size: 35191726
dataset_size: 35873206.596
---
# Dataset Card for "portraits3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JVictorLourenco/me | ---
license: mit
---
|
fede97/external_data_test_example_v2 | ---
dataset_info:
features:
- name: stable_unclip
dtype: image
- name: kandisky_2_2
dtype: image
- name: self_attention_guidance
dtype: image
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dtype: image
- name: deepfloyd_if
dtype: image
- name: latent_consistency_model_simianluo
dtype: image
- name: amused
dtype: image
- name: stabilityai_stable_diffusion_2_1_base
dtype: image
- name: kandisky_2_1
dtype: image
- name: sdxl_turbo
dtype: image
- name: stabilityai_stable_diffusion_xl_base_1_0
dtype: image
- name: compvis_stable_diffusion_v1_4
dtype: image
- name: pixart_alpha
dtype: image
- name: id
dtype: string
splits:
- name: train
num_bytes: 58603828411.0
num_examples: 4800
download_size: 58467592182
dataset_size: 58603828411.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
OllieStanley/oa_Vicuna_V5 | ---
license: apache-2.0
---
|
distilled-from-one-sec-cv12/chunk_189 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1308300760
num_examples: 254930
download_size: 1333497082
dataset_size: 1308300760
---
# Dataset Card for "chunk_189"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EnigmaOfTheWorld/complex-queries | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 417507
num_examples: 545
download_size: 55781
dataset_size: 417507
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "complex-queries"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-futin__guess-en_3-fcaae9-2012466611 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/guess
eval_info:
task: text_zero_shot_classification
model: facebook/opt-30b
metrics: []
dataset_name: futin/guess
dataset_config: en_3
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-30b
* Dataset: futin/guess
* Config: en_3
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
2Eden2/customsjcode1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3338910
num_examples: 6440
download_size: 1540912
dataset_size: 3338910
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Katherinetian/weather_data_NC | ---
license: apache-2.0
tags:
- climate
- weather
- predictive models
---
# Dataset Card for NC Weather Analysis Dataset
<!-- Provide a quick summary of the dataset. -->
This dataset card provides a detailed overview of a weather dataset focused on North Carolina, sourced from OpenWeatherMap.org. The dataset compiles various measurements intended for climate research, weather forecasting, and predictive model development.
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
This dataset encompasses a comprehensive collection of weather data for North Carolina, including temperature, humidity, atmospheric pressure, wind speed, and precipitation. All data comes in hourly. The data has been meticulously gathered from global and local weather models, satellites, radars, and an extensive network of weather stations.
- **Curated by:** [Katherine Tian]
- **Shared by:** [OpenWeatherMap.org]
- **Language(s) (NLP):** N/A (Non-linguistic data)
- **License:** Apache-2.0
- **Period of data collection:** March 2023 to March 2024
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://openweathermap.org/
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
This dataset is designed to support climate research, weather forecasting, and the development of predictive models for environmental studies, urban planning, and emergency preparedness.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
Usage beyond academic research, weather prediction, and climate modeling—such as for commercial forecasting without proper attribution or for misinformation purposes—is considered out of scope.
## 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. -->
This dataset provides comprehensive weather information collected from OpenWeatherMap, structured into a tabular format. Each row in the dataset represents weather data for a specific hour, including various atmospheric measurements and conditions. Below is a description of the dataset fields:
- **dt:** Unix timestamp indicating the time of the weather data.
- **main.temp:** The temperature at the specified hour, measured in Kelvin.
- **main.feels_like:** The human-perceived temperature, taking humidity and wind into account, measured in Kelvin.
- **main.pressure:** Atmospheric pressure at sea level, measured in hPa (hectopascal).
- **main.humidity:** Humidity percentage at the specified hour.
- **main.temp_min:** Minimum temperature within the last hour, measured in Kelvin.
- **main.temp_max:** Maximum temperature within the last hour, measured in Kelvin.
- **wind.speed:** Wind speed at the specified hour, measured in m/s (meters per second).
- **wind.deg:** Wind direction in degrees (meteorological).
- **wind.gust:** Wind gust speed at the specified hour, measured in m/s.
- **clouds.all:** Cloudiness percentage at the specified hour.
- **latitude and longitude:** Geographic coordinates of the location where the weather data was collected.
- **date:** Date (YYYY-MM-DD) corresponding to the weather data.
- **rain.1h (nullable):** Rain volume for the last hour, measured in mm (millimeters). This field is nullable to account for periods without rainfall.
- **weather_id:** Weather condition ID, corresponding to OpenWeatherMap's weather condition codes.
- **weather_main:** General weather condition category (e.g., Rain, Clear, Clouds).
- **weather_description:** More detailed description of the weather condition.
- **weather_icon:** Icon code representing the weather condition visually.
- **city:** The name of the city for which the weather data is provided.
- **snow.1h (nullable):** Snow volume for the last hour, measured in mm. Similar to rain.1h, this field is nullable.
- **rain.3h (nullable):** Rain volume for the last three hours, provided for datasets with a wider temporal aggregation.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was created to offer an accessible, comprehensive source of weather data for North Carolina, facilitating a wide range of scientific research and practical applications in climate studies and weather forecasting. It contains both data over a large time scale and for different locations. As many agencies, such as NOAA, do not grant access to large amounts of weather data at one time, this dataset can bring convenience to obtain hourly weather data.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
Data was collected from OpenWeatherMap.org, leveraging their access to global and local weather models, satellite data, radars, and a vast network of weather stations.
#### 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. -->
The `WeatherDataFetcher` class is designed to programmatically collect and process historical weather data for ten major NC cities over a defined date range. The data is sourced from the OpenWeatherMap API, which aggregates weather data from various global and local models, satellites, radars, and weather stations.
#### Data Collection
- **Source:** The data is fetched from OpenWeatherMap's Historical Weather Data API.
- **City Selection:** Ten major cities in NC are selected. Users can specify one or more city names to collect weather data for.
- **Date Range:** 2023/03/19 - 2024/03/16. Users can define a start and end date for the period over which they wish to collect weather data. The data can span multiple days, and the script fetches hourly weather data for each day in the specified range.
- **Latitude and Longitude:** For each city, the script first retrieves the geographic coordinates (latitude and longitude). These coordinates are then used to request historical weather data.
#### Data Processing
- **Normalization:** The fetched data is in JSON format, with nested structures for different weather parameters. The script uses `pd.json_normalize` to flatten these structures into a tabular format suitable for analysis.
- **Weather Column Expansion:** The nested 'weather' column, which contains detailed weather conditions (like weather ID, main weather condition, description, and icon), is expanded into separate columns for each attribute. This makes the dataset more accessible for analysis and visualization.
### Tools and Libraries Used
- **Requests:** For making HTTP requests to the OpenWeatherMap API.
- **Pandas:** For data manipulation and normalization.
- **Datetime:** For handling dates and timestamps.
<!-- 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. -->
### Annotations
The dataset contains columns which are not part of the initial data collection for clarity.
- **Latitude and Longitude Columns:** Each row in the resultant DataFrame is appended with latitude and longitude information, tying the weather data back to its geographic location.
- **Date Column:** A date column is added to each row, indicating the date for which the weather data was recorded. This facilitates time-series analysis and visualization.
- **City Name Column:** For datasets involving multiple cities, a 'city' column is added to differentiate the weather data by location.
#### Annotators
<!-- This section describes the people or systems who created the annotations. -->
The annotations are created by Katherine Tian, the curator of this dataset.
#### 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. -->
The data scripts contain private API keys.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- **API Rate Limits:** The OpenWeatherMap API has rate limits, which could restrict the volume of data that can be fetched in a given time frame.
- **Historical Data Availability:** The availability of historical data might vary based on the geographic location and the specific date range requested.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Researchers and users are encouraged to use this dataset alongside other data sources to mitigate potential biases and limitations. OpenWeatherMap's stations do not guarantee completely accurate data. Careful consideration should be given to the dataset's scope and accuracy when drawing conclusions or making predictions. |
AdapterOcean/biology_dataset_standardized_cluster_1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 10392472
num_examples: 957
download_size: 0
dataset_size: 10392472
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "biology_dataset_standardized_cluster_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-one-sec-cv12/chunk_131 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 823325480
num_examples: 161690
download_size: 837150081
dataset_size: 823325480
---
# Dataset Card for "chunk_131"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_ABX-AI__Quantum-Citrus-9B | ---
pretty_name: Evaluation run of ABX-AI/Quantum-Citrus-9B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ABX-AI/Quantum-Citrus-9B](https://huggingface.co/ABX-AI/Quantum-Citrus-9B) 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_ABX-AI__Quantum-Citrus-9B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-10T11:54:47.470008](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Quantum-Citrus-9B/blob/main/results_2024-04-10T11-54-47.470008.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\": 0.6454939972534741,\n\
\ \"acc_stderr\": 0.03220379993543776,\n \"acc_norm\": 0.6492830222702979,\n\
\ \"acc_norm_stderr\": 0.03284431925658952,\n \"mc1\": 0.4039167686658507,\n\
\ \"mc1_stderr\": 0.017177276822584284,\n \"mc2\": 0.5595960431117389,\n\
\ \"mc2_stderr\": 0.015479532495848445\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n\
\ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179347\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6562437761402111,\n\
\ \"acc_stderr\": 0.004739902411944538,\n \"acc_norm\": 0.847540330611432,\n\
\ \"acc_norm_stderr\": 0.003587312328180705\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595852,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595852\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\": 0.63,\n\
\ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080342,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080342\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\"\
: 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\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\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.041227371113703316,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.041227371113703316\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4021164021164021,\n \"acc_stderr\": 0.02525303255499769,\n \"\
acc_norm\": 0.4021164021164021,\n \"acc_norm_stderr\": 0.02525303255499769\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\
\ \"acc_stderr\": 0.023664216671642518,\n \"acc_norm\": 0.7774193548387097,\n\
\ \"acc_norm_stderr\": 0.023664216671642518\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217483,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217483\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603346,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603346\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\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\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.016265675632010354,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.016265675632010354\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.034063153607115086,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.034063153607115086\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\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\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7791411042944786,\n \"acc_stderr\": 0.03259177392742178,\n\
\ \"acc_norm\": 0.7791411042944786,\n \"acc_norm_stderr\": 0.03259177392742178\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.013890862162876164,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.013890862162876164\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526501,\n\
\ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526501\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3452513966480447,\n\
\ \"acc_stderr\": 0.015901432608930358,\n \"acc_norm\": 0.3452513966480447,\n\
\ \"acc_norm_stderr\": 0.015901432608930358\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7418300653594772,\n \"acc_stderr\": 0.02505850331695815,\n\
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.02505850331695815\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5035460992907801,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.5035460992907801,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46284224250325945,\n\
\ \"acc_stderr\": 0.012734923579532069,\n \"acc_norm\": 0.46284224250325945,\n\
\ \"acc_norm_stderr\": 0.012734923579532069\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6691176470588235,\n \"acc_stderr\": 0.028582709753898445,\n\
\ \"acc_norm\": 0.6691176470588235,\n \"acc_norm_stderr\": 0.028582709753898445\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306053,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306053\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4039167686658507,\n\
\ \"mc1_stderr\": 0.017177276822584284,\n \"mc2\": 0.5595960431117389,\n\
\ \"mc2_stderr\": 0.015479532495848445\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7940015785319653,\n \"acc_stderr\": 0.011366474352008825\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5056861258529188,\n \
\ \"acc_stderr\": 0.013771594106283036\n }\n}\n```"
repo_url: https://huggingface.co/ABX-AI/Quantum-Citrus-9B
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_10T11_54_47.470008
path:
- '**/details_harness|arc:challenge|25_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|gsm8k|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hellaswag|10_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-54-47.470008.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-10T11-54-47.470008.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- '**/details_harness|winogrande|5_2024-04-10T11-54-47.470008.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-10T11-54-47.470008.parquet'
- config_name: results
data_files:
- split: 2024_04_10T11_54_47.470008
path:
- results_2024-04-10T11-54-47.470008.parquet
- split: latest
path:
- results_2024-04-10T11-54-47.470008.parquet
---
# Dataset Card for Evaluation run of ABX-AI/Quantum-Citrus-9B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ABX-AI/Quantum-Citrus-9B](https://huggingface.co/ABX-AI/Quantum-Citrus-9B) 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_ABX-AI__Quantum-Citrus-9B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-10T11:54:47.470008](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Quantum-Citrus-9B/blob/main/results_2024-04-10T11-54-47.470008.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": 0.6454939972534741,
"acc_stderr": 0.03220379993543776,
"acc_norm": 0.6492830222702979,
"acc_norm_stderr": 0.03284431925658952,
"mc1": 0.4039167686658507,
"mc1_stderr": 0.017177276822584284,
"mc2": 0.5595960431117389,
"mc2_stderr": 0.015479532495848445
},
"harness|arc:challenge|25": {
"acc": 0.6109215017064846,
"acc_stderr": 0.014247309976045607,
"acc_norm": 0.6518771331058021,
"acc_norm_stderr": 0.013921008595179347
},
"harness|hellaswag|10": {
"acc": 0.6562437761402111,
"acc_stderr": 0.004739902411944538,
"acc_norm": 0.847540330611432,
"acc_norm_stderr": 0.003587312328180705
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595852,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595852
},
"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": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880267,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880267
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080342,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080342
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"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": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.041227371113703316,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.041227371113703316
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4021164021164021,
"acc_stderr": 0.02525303255499769,
"acc_norm": 0.4021164021164021,
"acc_norm_stderr": 0.02525303255499769
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42063492063492064,
"acc_stderr": 0.04415438226743744,
"acc_norm": 0.42063492063492064,
"acc_norm_stderr": 0.04415438226743744
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7774193548387097,
"acc_stderr": 0.023664216671642518,
"acc_norm": 0.7774193548387097,
"acc_norm_stderr": 0.023664216671642518
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217483,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217483
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603346,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603346
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
"acc_stderr": 0.023946724741563976,
"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.028493465091028597,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.028493465091028597
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977934,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977934
},
"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": 0.8256880733944955,
"acc_stderr": 0.016265675632010354,
"acc_norm": 0.8256880733944955,
"acc_norm_stderr": 0.016265675632010354
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.034063153607115086,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.034063153607115086
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8186274509803921,
"acc_stderr": 0.027044621719474082,
"acc_norm": 0.8186274509803921,
"acc_norm_stderr": 0.027044621719474082
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7791411042944786,
"acc_stderr": 0.03259177392742178,
"acc_norm": 0.7791411042944786,
"acc_norm_stderr": 0.03259177392742178
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.013890862162876164,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.013890862162876164
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7254335260115607,
"acc_stderr": 0.02402774515526501,
"acc_norm": 0.7254335260115607,
"acc_norm_stderr": 0.02402774515526501
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3452513966480447,
"acc_stderr": 0.015901432608930358,
"acc_norm": 0.3452513966480447,
"acc_norm_stderr": 0.015901432608930358
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7418300653594772,
"acc_stderr": 0.02505850331695815,
"acc_norm": 0.7418300653594772,
"acc_norm_stderr": 0.02505850331695815
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.024288533637726095,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.024288533637726095
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5035460992907801,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.5035460992907801,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46284224250325945,
"acc_stderr": 0.012734923579532069,
"acc_norm": 0.46284224250325945,
"acc_norm_stderr": 0.012734923579532069
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6691176470588235,
"acc_stderr": 0.028582709753898445,
"acc_norm": 0.6691176470588235,
"acc_norm_stderr": 0.028582709753898445
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6781045751633987,
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"acc_norm": 0.6781045751633987,
"acc_norm_stderr": 0.018901015322093092
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.024845753212306053,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.024845753212306053
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4039167686658507,
"mc1_stderr": 0.017177276822584284,
"mc2": 0.5595960431117389,
"mc2_stderr": 0.015479532495848445
},
"harness|winogrande|5": {
"acc": 0.7940015785319653,
"acc_stderr": 0.011366474352008825
},
"harness|gsm8k|5": {
"acc": 0.5056861258529188,
"acc_stderr": 0.013771594106283036
}
}
```
## 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] |
autoevaluate/autoeval-staging-eval-project-e1907042-7494827 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: HrayrMSint/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: HrayrMSint/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
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. |
amitness/logits-maltese-128 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- name: teacher_logits
sequence:
sequence: float64
- name: teacher_indices
sequence:
sequence: int64
- name: teacher_mask_indices
sequence: int64
splits:
- name: train
num_bytes: 230752436
num_examples: 50911
download_size: 97319795
dataset_size: 230752436
---
# Dataset Card for "logits-maltese-128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Sulav/OpenHermes-2.5-1k-longest | ---
dataset_info:
features:
- name: category
dtype: string
- name: source
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: float64
- name: average_response_length
dtype: float64
splits:
- name: train
num_bytes: 6201440
num_examples: 1000
- name: test
num_bytes: 8741442
num_examples: 1000
download_size: 5950881
dataset_size: 14942882
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Manolo/autotrain-data-nl-en-esco-1 | ---
task_categories:
- translation
---
# AutoTrain Dataset for project: nl-en-esco-1
## Dataset Description
This dataset has been automatically processed by AutoTrain for project nl-en-esco-1.
### 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
[
{
"feat_Unnamed: 0": 2388,
"source": "COVID-tester",
"target": "covid tester "
},
{
"feat_Unnamed: 0": 2829,
"source": "marien bioloog",
"target": "marine biologist"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"source": "Value(dtype='string', id=None)",
"target": "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 | 2405 |
| valid | 602 |
|
Neeraj8180/BrainTumor | ---
license: apache-2.0
---
|
Vtuber-plan/sharegpt-cleaned | ---
license: other
---
|
Oburaco/juridicbase | ---
license: unknown
---
|
FINNUMBER/FINCH_TRAIN_NQA_EXT_400 | ---
dataset_info:
features:
- name: task
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1639744
num_examples: 400
download_size: 942163
dataset_size: 1639744
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
RiccardoGvn/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: 'null'
- name: closed_at
dtype: 'null'
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 11542551
num_examples: 2000
download_size: 3276181
dataset_size: 11542551
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
determined-ai/samsum_short | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 543039
num_examples: 2916
- name: validation
num_bytes: 32458
num_examples: 171
- name: test
num_bytes: 28165
num_examples: 150
download_size: 417401
dataset_size: 603662
---
# Dataset Card for "samsum_short"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/wakana_rei_bangdream | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of wakana_rei/和奏レイ (BanG Dream!)
This is the dataset of wakana_rei/和奏レイ (BanG Dream!), containing 66 images and their tags.
The core tags of this character are `blue_eyes, long_hair, black_hair, brown_hair, 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 | 66 | 77.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_rei_bangdream/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 66 | 51.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_rei_bangdream/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 142 | 98.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_rei_bangdream/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 66 | 70.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_rei_bangdream/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 142 | 132.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakana_rei_bangdream/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/wakana_rei_bangdream',
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 | 9 |  |  |  |  |  | 1girl, smile, solo, belt, jeans, jewelry, simple_background, white_background, white_shirt, looking_at_viewer, blue_pants, closed_mouth, holding, standing, torn_clothes |
| 1 | 5 |  |  |  |  |  | 1girl, earrings, solo, bare_shoulders, looking_at_viewer, short_hair, smile, black_gloves, flower, hair_ornament, red_dress, closed_mouth, feather_boa, upper_body |
| 2 | 17 |  |  |  |  |  | 1girl, solo, looking_at_viewer, open_mouth, guitar, microphone, earrings, choker, holding, black_gloves, skirt, smile, midriff, purple_eyes, singing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | belt | jeans | jewelry | simple_background | white_background | white_shirt | looking_at_viewer | blue_pants | closed_mouth | holding | standing | torn_clothes | earrings | bare_shoulders | short_hair | black_gloves | flower | hair_ornament | red_dress | feather_boa | upper_body | open_mouth | guitar | microphone | choker | skirt | midriff | purple_eyes | singing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:-------|:--------|:----------|:--------------------|:-------------------|:--------------|:--------------------|:-------------|:---------------|:----------|:-----------|:---------------|:-----------|:-----------------|:-------------|:---------------|:---------|:----------------|:------------|:--------------|:-------------|:-------------|:---------|:-------------|:---------|:--------|:----------|:--------------|:----------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | | | | | | | X | | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 2 | 17 |  |  |  |  |  | X | X | X | | | | | | | X | | | X | | | X | | | X | | | | | | X | X | X | X | X | X | X | X |
|
aminlouhichi/donutpreparedFinetuneDataGenreted | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 27904115.0
num_examples: 128
- name: validation
num_bytes: 13089836.0
num_examples: 60
- name: test
num_bytes: 13111083.0
num_examples: 59
download_size: 50588060
dataset_size: 54105034.0
---
# Dataset Card for "donutpreparedFinetuneDataGenreted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ichsan2895/DPO_ID-Wiki_10kTesting | ---
license: cc-by-nc-sa-4.0
---
## HOW TO WRANGLING THIS DATASET TO DPO & CHATML FORMAT
```
def return_prompt_and_responses(samples) -> dict[str, str, str]:
return {
"prompt": [
"<|im_start|>user\n" + i + "<|im_end|>\n"
for i in samples["PROMPT"]
],
"chosen": [
"<|im_start|>assistant\n" + j + "<|im_end|>"
for j in samples["CHOSEN"]
],
"rejected": [
"<|im_start|>assistant\n" + k + "<|im_end|>"
for k in samples["REJECTED"]
],
}
dataset = load_dataset(
"Ichsan2895/DPO_ID-Wiki_10kTesting",
)
original_columns = dataset.column_names
dataset.map(
return_prompt_and_responses,
batched=True,
remove_columns=original_columns
)
```
## HOW TO USE DPO
```
dpo_trainer = DPOTrainer(
model, # base model from SFT pipeline
model_ref, # typically a copy of the SFT trained base model
beta=0.1, # temperature hyperparameter of DPO
train_dataset=dataset['train'], # dataset prepared above
tokenizer=tokenizer, # tokenizer
args=training_args, # training arguments e.g. batch size, lr, etc.
)
```
## CITATION
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
@misc{vonwerra2022trl,
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang},
title = {TRL: Transformer Reinforcement Learning},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jindaznb/en_corpora_parliament_processed | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 309185247
num_examples: 2051014
download_size: 171553321
dataset_size: 309185247
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mathigatti/spanish_imdb_synopsis | ---
annotations_creators:
- no-annotation
language:
- es
license:
- apache-2.0
multilinguality:
- monolingual
task_categories:
- summarization
- text-generation
- text2text-generation
---
# Dataset Card for Spanish IMDb Synopsis
## 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 Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
## Dataset Description
4969 movie synopsis from IMDb in spanish.
### Dataset Summary
[N/A]
### Languages
All descriptions are in spanish, the other fields have some mix of spanish and english.
## Dataset Structure
[N/A]
### Data Fields
- `description`: IMDb description for the movie (string), should be spanish
- `keywords`: IMDb keywords for the movie (string), mix of spanish and english
- `genre`: The genres of the movie (string), mix of spanish and english
- `year`: The year the movie was published (float)
- `name`: The name of the movie (string), mix of spanish and english
- `director`: The name of the main director in the movie, can be empty (string)
## Dataset Creation
[This kaggle dataset](https://www.kaggle.com/datasets/komalkhetlani/imdb-dataset) was used as a starting point. Then IMDb was scraped downloading the synopsis of the movies that have more than 5000 votes/reviews and those that did not have a synopsis available in Spanish were discarded. |
dongyoung4091/shp-generated_flan_t5_large_external_rm1_large | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: external_rm1
dtype: float64
splits:
- name: train
num_bytes: 27036265
num_examples: 25600
download_size: 1846172
dataset_size: 27036265
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "shp-generated_flan_t5_large_external_rm1_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kfahn/kaleidoscope | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 93843389.0
num_examples: 418
download_size: 93853106
dataset_size: 93843389.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Mel-Iza0/squad3 | ---
dataset_info:
features:
- name: id
dtype: string
- name: input_ids
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 3833665
num_examples: 5928
- name: validation
num_bytes: 982887
num_examples: 1482
download_size: 2412739
dataset_size: 4816552
---
# Dataset Card for "squad3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged | ---
pretty_name: Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged)\
\ 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-16T08:43:34.747997](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged/blob/main/results_2024-02-16T08-43-34.747997.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\": 0.6443352125855402,\n\
\ \"acc_stderr\": 0.03223578518541491,\n \"acc_norm\": 0.6464316260111327,\n\
\ \"acc_norm_stderr\": 0.03288034667596033,\n \"mc1\": 0.36964504283965727,\n\
\ \"mc1_stderr\": 0.016898180706973884,\n \"mc2\": 0.5318297182928406,\n\
\ \"mc2_stderr\": 0.015213885422385947\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6109215017064846,\n \"acc_stderr\": 0.014247309976045607,\n\
\ \"acc_norm\": 0.6578498293515358,\n \"acc_norm_stderr\": 0.013864152159177275\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6619199362676758,\n\
\ \"acc_stderr\": 0.004720891597174729,\n \"acc_norm\": 0.8526190001991635,\n\
\ \"acc_norm_stderr\": 0.0035376085010691773\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7105263157894737,\n \"acc_stderr\": 0.03690677986137283,\n\
\ \"acc_norm\": 0.7105263157894737,\n \"acc_norm_stderr\": 0.03690677986137283\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252603,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252603\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n\
\ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\
\ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.049665709039785295,\n\
\ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.049665709039785295\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n\
\ \"acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5175438596491229,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.5175438596491229,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n\
\ \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055256,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055256\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7709677419354839,\n\
\ \"acc_stderr\": 0.023904914311782648,\n \"acc_norm\": 0.7709677419354839,\n\
\ \"acc_norm_stderr\": 0.023904914311782648\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\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\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298901,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298901\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6512820512820513,\n \"acc_stderr\": 0.02416278028401772,\n \
\ \"acc_norm\": 0.6512820512820513,\n \"acc_norm_stderr\": 0.02416278028401772\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3851851851851852,\n \"acc_stderr\": 0.029670906124630882,\n \
\ \"acc_norm\": 0.3851851851851852,\n \"acc_norm_stderr\": 0.029670906124630882\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8385321100917431,\n\
\ \"acc_stderr\": 0.015776239256163248,\n \"acc_norm\": 0.8385321100917431,\n\
\ \"acc_norm_stderr\": 0.015776239256163248\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n\
\ \"acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\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\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.039153454088478354,\n\
\ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.039153454088478354\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.0398913985953177,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.0398913985953177\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\": 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.01354741565866226,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.01354741565866226\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7283236994219653,\n \"acc_stderr\": 0.023948512905468358,\n\
\ \"acc_norm\": 0.7283236994219653,\n \"acc_norm_stderr\": 0.023948512905468358\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4201117318435754,\n\
\ \"acc_stderr\": 0.016507671073256402,\n \"acc_norm\": 0.4201117318435754,\n\
\ \"acc_norm_stderr\": 0.016507671073256402\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7320261437908496,\n \"acc_stderr\": 0.02536060379624256,\n\
\ \"acc_norm\": 0.7320261437908496,\n \"acc_norm_stderr\": 0.02536060379624256\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.026003301117885142,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.026003301117885142\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.023788583551658533,\n\
\ \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.023788583551658533\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4645390070921986,\n \"acc_stderr\": 0.02975238965742705,\n \
\ \"acc_norm\": 0.4645390070921986,\n \"acc_norm_stderr\": 0.02975238965742705\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44328552803129073,\n\
\ \"acc_stderr\": 0.012687818419599923,\n \"acc_norm\": 0.44328552803129073,\n\
\ \"acc_norm_stderr\": 0.012687818419599923\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.673202614379085,\n \"acc_stderr\": 0.0189754279205072,\n \
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.0189754279205072\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\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399677,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399677\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616914,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616914\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.36964504283965727,\n\
\ \"mc1_stderr\": 0.016898180706973884,\n \"mc2\": 0.5318297182928406,\n\
\ \"mc2_stderr\": 0.015213885422385947\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7892659826361483,\n \"acc_stderr\": 0.011462046419710676\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6133434420015162,\n \
\ \"acc_stderr\": 0.013413955095965307\n }\n}\n```"
repo_url: https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged
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_16T08_43_34.747997
path:
- '**/details_harness|arc:challenge|25_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|gsm8k|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hellaswag|10_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T08-43-34.747997.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-16T08-43-34.747997.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- '**/details_harness|winogrande|5_2024-02-16T08-43-34.747997.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-16T08-43-34.747997.parquet'
- config_name: results
data_files:
- split: 2024_02_16T08_43_34.747997
path:
- results_2024-02-16T08-43-34.747997.parquet
- split: latest
path:
- results_2024-02-16T08-43-34.747997.parquet
---
# Dataset Card for Evaluation run of fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged](https://huggingface.co/fzzhang/Marcoroni-neural-chat-7B-v2_gsm8k_merged) 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_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-16T08:43:34.747997](https://huggingface.co/datasets/open-llm-leaderboard/details_fzzhang__Marcoroni-neural-chat-7B-v2_gsm8k_merged/blob/main/results_2024-02-16T08-43-34.747997.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": 0.6443352125855402,
"acc_stderr": 0.03223578518541491,
"acc_norm": 0.6464316260111327,
"acc_norm_stderr": 0.03288034667596033,
"mc1": 0.36964504283965727,
"mc1_stderr": 0.016898180706973884,
"mc2": 0.5318297182928406,
"mc2_stderr": 0.015213885422385947
},
"harness|arc:challenge|25": {
"acc": 0.6109215017064846,
"acc_stderr": 0.014247309976045607,
"acc_norm": 0.6578498293515358,
"acc_norm_stderr": 0.013864152159177275
},
"harness|hellaswag|10": {
"acc": 0.6619199362676758,
"acc_stderr": 0.004720891597174729,
"acc_norm": 0.8526190001991635,
"acc_norm_stderr": 0.0035376085010691773
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7105263157894737,
"acc_stderr": 0.03690677986137283,
"acc_norm": 0.7105263157894737,
"acc_norm_stderr": 0.03690677986137283
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252603,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252603
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.03533133389323657
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.47058823529411764,
"acc_stderr": 0.049665709039785295,
"acc_norm": 0.47058823529411764,
"acc_norm_stderr": 0.049665709039785295
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6085106382978723,
"acc_stderr": 0.03190701242326812,
"acc_norm": 0.6085106382978723,
"acc_norm_stderr": 0.03190701242326812
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5175438596491229,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.5175438596491229,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.025355741263055256,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.025355741263055256
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7709677419354839,
"acc_stderr": 0.023904914311782648,
"acc_norm": 0.7709677419354839,
"acc_norm_stderr": 0.023904914311782648
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
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"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.02805779167298901
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6512820512820513,
"acc_stderr": 0.02416278028401772,
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"acc_norm_stderr": 0.02416278028401772
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"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3851851851851852,
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"acc_norm": 0.3851851851851852,
"acc_norm_stderr": 0.029670906124630882
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
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"acc_norm_stderr": 0.03048991141767323
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.304635761589404,
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"acc_norm_stderr": 0.03757949922943343
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"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
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"acc_norm_stderr": 0.015776239256163248
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"acc_norm_stderr": 0.03398110890294636
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8186274509803921,
"acc_stderr": 0.027044621719474082,
"acc_norm": 0.8186274509803921,
"acc_norm_stderr": 0.027044621719474082
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601443,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601443
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7251908396946565,
"acc_stderr": 0.039153454088478354,
"acc_norm": 0.7251908396946565,
"acc_norm_stderr": 0.039153454088478354
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.7961165048543689,
"acc_stderr": 0.0398913985953177,
"acc_norm": 0.7961165048543689,
"acc_norm_stderr": 0.0398913985953177
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
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"acc_norm": 0.73,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
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"acc_norm_stderr": 0.01354741565866226
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7283236994219653,
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"acc_norm": 0.7283236994219653,
"acc_norm_stderr": 0.023948512905468358
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4201117318435754,
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"acc_norm": 0.4201117318435754,
"acc_norm_stderr": 0.016507671073256402
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7320261437908496,
"acc_stderr": 0.02536060379624256,
"acc_norm": 0.7320261437908496,
"acc_norm_stderr": 0.02536060379624256
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7009646302250804,
"acc_stderr": 0.026003301117885142,
"acc_norm": 0.7009646302250804,
"acc_norm_stderr": 0.026003301117885142
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.023788583551658533,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.023788583551658533
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.02975238965742705,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.02975238965742705
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.44328552803129073,
"acc_stderr": 0.012687818419599923,
"acc_norm": 0.44328552803129073,
"acc_norm_stderr": 0.012687818419599923
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6727941176470589,
"acc_stderr": 0.028501452860396556,
"acc_norm": 0.6727941176470589,
"acc_norm_stderr": 0.028501452860396556
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.673202614379085,
"acc_stderr": 0.0189754279205072,
"acc_norm": 0.673202614379085,
"acc_norm_stderr": 0.0189754279205072
},
"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": 0.746938775510204,
"acc_stderr": 0.027833023871399677,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.027833023871399677
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616914,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616914
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.0348735088019777,
"acc_norm": 0.86,
"acc_norm_stderr": 0.0348735088019777
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.36964504283965727,
"mc1_stderr": 0.016898180706973884,
"mc2": 0.5318297182928406,
"mc2_stderr": 0.015213885422385947
},
"harness|winogrande|5": {
"acc": 0.7892659826361483,
"acc_stderr": 0.011462046419710676
},
"harness|gsm8k|5": {
"acc": 0.6133434420015162,
"acc_stderr": 0.013413955095965307
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
ppak10/NIST-LPBF-Scan-Tracks | ---
license: mit
language:
- en
tags:
- NIST
---
# NIST LPBF Scan Tracks
2 single and 2 multiple lpbf scan tracks on nickel alloy 625.
## 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]:** [Thermographic measurements of single and multiple scan tracks on nickel alloy 625 substrates with and without a powder layer in a commercial laser powder bed fusion process (an additive manufacturing technology)](https://data.nist.gov/od/id/5887178FE62C46F8E0531A57068103631858)
- **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] |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/ff4ad9fc | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 178
num_examples: 10
download_size: 1345
dataset_size: 178
---
# Dataset Card for "ff4ad9fc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edwinjue/311-data-2015 | ---
license: gpl-3.0
---
|
Kazabian/voxxuxa | ---
license: openrail
---
|
jojo-ai-mst/hospital-antibiotics-usage | ---
tags:
- medical
pretty_name: Hospital Antibiotics Usage
---
# Drug Resistance and antibiotics
Drug resistance to antibiotics is increasing around the world every day. Generations of antibiotics are dynamically changing in treatment.
In 2019, a group of medical students in Myanmar, Mandalay researched antibiotics usage in hospitals. The research won first prize at the University of Medicine, Mandalay 3rd MBBS research competition, 2019.
The dataset focuses on the retrospective study of the usage of antibiotics and diseases under the title of antibiotic resistance. The dataset contains
- age and gender of the patient,
- diagnosis of the patient,
- Antibiotics used to treat patient
- Dosage of the antibiotics in grams
- Route of application of antibiotics
- Frequency of usage of antibiotics
- Duration of treatment using antibiotics in days
- Indiction of antibiotics
License
CC BY-SA 4.0
Tags
- Health
- Drugs and Medications |
LIAGM/DAEFR_results | ---
license: mit
---
|
pctemple/Cards_against_humanity | ---
license: openrail
---
|
open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1 | ---
pretty_name: Evaluation run of habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 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_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T14:49:10.140020](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-10.140020.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\": 0.01592115238817286,\n\
\ \"acc_stderr\": 0.0034478192723889976\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.01592115238817286,\n \"acc_stderr\": 0.0034478192723889976\n\
\ }\n}\n```"
repo_url: https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T14_49_10.140020
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-10.140020.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T14-49-10.140020.parquet'
- config_name: results
data_files:
- split: 2023_12_02T14_49_10.140020
path:
- results_2023-12-02T14-49-10.140020.parquet
- split: latest
path:
- results_2023-12-02T14-49-10.140020.parquet
---
# Dataset Card for Evaluation run of habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1](https://huggingface.co/habanoz/TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 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_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T14:49:10.140020](https://huggingface.co/datasets/open-llm-leaderboard/details_habanoz__TinyLlama-1.1B-step-2T-lr-5-5ep-oasst1-top1-instruct-V1/blob/main/results_2023-12-02T14-49-10.140020.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": 0.01592115238817286,
"acc_stderr": 0.0034478192723889976
},
"harness|gsm8k|5": {
"acc": 0.01592115238817286,
"acc_stderr": 0.0034478192723889976
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
zaydzuhri/the_pile_tokenized_5percent_truncated_packed | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
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dataset_size: 17271389820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
YAGOSTOSO/KIMPETRAS | ---
license: unknown
---
|
roupenminassian/vehicle-dataset-v7 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: int64
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dtype: int64
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dtype: int64
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struct:
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sequence: int64
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sequence: float64
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sequence:
sequence: float64
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sequence: int64
splits:
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download_size: 275908173
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vehicle-dataset-v7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
raprotv/Renata | ---
license: mit
---
|
h4rr9/mnist_palette_num_1_bit | ---
dataset_info:
features:
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download_size: 19420162
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---
# Dataset Card for "mnist_palette_1_bit_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Codec-SUPERB/ljspeech_synth | ---
configs:
- config_name: default
data_files:
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path: data/original-*
- split: academicodec_hifi_16k_320d
path: data/academicodec_hifi_16k_320d-*
- split: academicodec_hifi_16k_320d_large_uni
path: data/academicodec_hifi_16k_320d_large_uni-*
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path: data/audiodec_24k_320d-*
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path: data/dac_16k-*
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path: data/dac_24k-*
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path: data/dac_44k-*
- split: encodec_24k
path: data/encodec_24k-*
- split: funcodec_en_libritts_16k_gr1nq32ds320
path: data/funcodec_en_libritts_16k_gr1nq32ds320-*
- split: funcodec_en_libritts_16k_gr8nq32ds320
path: data/funcodec_en_libritts_16k_gr8nq32ds320-*
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path: data/funcodec_en_libritts_16k_nq32ds320-*
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path: data/funcodec_en_libritts_16k_nq32ds640-*
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path: data/funcodec_zh_en_16k_nq32ds320-*
- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: speech_tokenizer_16k
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num_examples: 13100
download_size: 55125538550
dataset_size: 55996892718.70001
---
# Dataset Card for "ljspeech_synth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gayanin/kaggle-native-mixed | ---
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download_size: 346018
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configs:
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- split: test
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- split: validation
path: prob-0.1/validation-*
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data_files:
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- split: test
path: prob-0.2/test-*
- split: validation
path: prob-0.2/validation-*
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data_files:
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path: prob-0.3/test-*
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path: prob-0.3/validation-*
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path: prob-0.5/test-*
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path: prob-0.5/validation-*
---
|
cyrilw/voiced | ---
license: mit
task_categories:
- text-classification
language:
- en
pretty_name: VOICED
size_categories:
- 1K<n<10K
--- |
DataStudio/OCR_handwritting_HAT2023 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
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dtype: image
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dtype: string
splits:
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num_examples: 103000
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num_examples: 33000
download_size: 0
dataset_size: 913651775.0
---
# Dataset Card for "OCR_handwritting_HAT2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huninEye/OdinSSON | ---
license: artistic-2.0
---
|
CodecSR/esc50_16k_synth | ---
dataset_info:
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dtype:
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download_size: 6501395022
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configs:
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path: data/academicodec_hifi_16k_320d_large_uni-*
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path: data/academicodec_hifi_24k_320d-*
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path: data/audiodec_24k_300d-*
- split: audiodec_48k_300d_uni
path: data/audiodec_48k_300d_uni-*
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path: data/dac_16k-*
- split: dac_24k
path: data/dac_24k-*
- split: dac_44k
path: data/dac_44k-*
- split: encodec_24k_12bps
path: data/encodec_24k_12bps-*
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path: data/encodec_24k_1_5bps-*
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path: data/encodec_24k_24bps-*
- split: encodec_24k_3bps
path: data/encodec_24k_3bps-*
- split: encodec_24k_6bps
path: data/encodec_24k_6bps-*
- split: facodec_16k
path: data/facodec_16k-*
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path: data/funcodec_en_libritts_16k_nq32ds320-*
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path: data/funcodec_en_libritts_16k_nq32ds640-*
- split: funcodec_zh_en_16k_nq32ds320
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- split: funcodec_zh_en_16k_nq32ds640
path: data/funcodec_zh_en_16k_nq32ds640-*
- split: language_codec_chinese_24k_nq8_12kbps
path: data/language_codec_chinese_24k_nq8_12kbps-*
- split: language_codec_paper_24k_nq8_12kbps
path: data/language_codec_paper_24k_nq8_12kbps-*
- split: speech_tokenizer_16k
path: data/speech_tokenizer_16k-*
---
|
DeepFoldProtein/Contrastive_Test | ---
dataset_info:
features:
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dtype: string
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splits:
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num_examples: 32
download_size: 2022553
dataset_size: 2137735
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_qqp_no_preverbal_negator | ---
dataset_info:
features:
- name: question1
dtype: string
- name: question2
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dtype: int64
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download_size: 3046484
dataset_size: 4916353
---
# Dataset Card for "MULTI_VALUE_qqp_no_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibivibiv/alpaca_lamini6 | ---
dataset_info:
features:
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dtype: string
- name: input
dtype: string
splits:
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dataset_size: 56350468
configs:
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data_files:
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path: data/train-*
---
|
enio/TinyStories | ---
license: mit
task_categories:
- text-generation
language:
- en
---
# Pretokenized TinyStories
[Based on roneneldan/TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)
* [**105 Tokens**](https://huggingface.co/datasets/enio/TinyStories/tree/main/tok105)   `byte_fallback=False`
* [**210 Tokens**](https://huggingface.co/datasets/enio/TinyStories/tree/main/tok210)   `byte_fallback=False`
* [**361 Tokens**](https://huggingface.co/datasets/enio/TinyStories/tree/main/tok361)
* [**4k Tokens**](https://huggingface.co/datasets/enio/TinyStories/tree/main/tok4096)
* [**32K Tokens**](https://huggingface.co/datasets/enio/TinyStories/tree/main/tok32000)
includes:
* tok*.vocab
* tok*.model
* tok*.bin
* tok*.tar.gz
* data{00..49}.bin
Pretokenized to speed up training on:
* [karpathy/llama2.c](https://github.com/karpathy/llama2.c)
* [EN10/BabyLlama](https://github.com/EN10/BabyLlama) |
MAPS-research/GEMRec-PromptBook | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: tag
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dtype: string
- name: note
dtype: string
- name: nsfw_score
dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
splits:
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num_bytes: 10373652334
num_examples: 18000
download_size: 9873105007
dataset_size: 10373652334
task_categories:
- text-to-image
language:
- en
tags:
- art
- stable diffusion
- diffusers
size_categories:
- 10K<n<100K
license: openrail
---
# GEMRec-18k -- Prompt Book
This is the official image dataset for the paper [Towards Personalized Prompt-Model Retrieval for Generative Recommendation](https://github.com/MAPS-research/GEMRec).
## Dataset Intro
`GEMRec-18K` is a prompt-model interaction dataset with 18K images generated by 200 publicly-available generative models paired with a diverse set of 90 textual prompts. We randomly sampled a subset of 197 models from the full set of models (all finetuned from Stable Diffusion) on [Civitai](https://civitai.com/) according to the popularity distribution (i.e., download counts) and added 3 original Stable Diffusion checkpoints (v1.4, v1.5, v2.1) from HuggingFace. All the model checkpoints have been converted to the [Diffusers](https://huggingface.co/docs/diffusers/index) format. The textual prompts were drawn from three sources: 60 prompts were sampled from [Parti Prompts](https://github.com/google-research/parti); 10 prompts were sampled from [Civitai](https://civitai.com/) by popularity; we also handcrafted 10 prompts following the prompting guide from [DreamStudio](https://beta.dreamstudio.ai/prompt-guide), and then extended them to 20 by creating a shortened and simplified version following the tips from [Midjourney](https://docs.midjourney.com/docs/prompts). The textual prompts were classified into 12 categories: abstract, animal, architecture, art, artifact, food, illustration, people, produce & plant, scenery, vehicle, and world knowledge.
## Links
#### Dataset
- [GEMRec-Promptbook](https://huggingface.co/datasets/MAPS-research/GEMRec-PromptBook): The full version of our GemRec-18k dataset (images & metadata).
- [GEMRec-Metadata](https://huggingface.co/datasets/MAPS-research/GEMRec-Metadata): The pruned version of our GemRec-18k dataset (metadata only).
- [GEMRec-Roster](https://huggingface.co/datasets/MAPS-research/GEMRec-Roster): The metadata for the 200 model checkpoints fetched from [Civitai](https://civitai.com/).
#### Space
- [GEMRec-Gallery](https://huggingface.co/spaces/MAPS-research/GEMRec-Gallery): Our web application for browsing and comparing the generated images.
#### Github Code
- [GEMRec](https://github.com/MAPS-research/GEMRec)
## Acknowledgement
This work was supported through the NYU High Performance Computing resources, services, and staff expertise.
## Citation
If you find our work helpful, please consider cite it as follows:
```bibtex
@article{guo2023towards,
title={Towards Personalized Prompt-Model Retrieval for Generative Recommendation},
author={Guo, Yuanhe and Liu, Haoming and Wen, Hongyi},
journal={arXiv preprint arXiv:2308.02205},
year={2023}
}
``` |
Ojimi/Hifu-Lora-Example | ---
license: creativeml-openrail-m
---
Nothing.... |
TheFinAI/flare-ma | ---
dataset_info:
features:
- name: id
dtype: string
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dtype: string
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dtype: string
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dtype: string
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sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 2295726
num_examples: 500
download_size: 1220605
dataset_size: 2295726
---
# Dataset Card for "flare-ma"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_68 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
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download_size: 24968503
dataset_size: 83015759
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_68"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/diadora_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of diadora (Fire Emblem)
This is the dataset of diadora (Fire Emblem), containing 69 images and their tags.
The core tags of this character are `long_hair, purple_eyes, purple_hair, breasts, light_purple_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 | 69 | 85.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 69 | 50.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 140 | 96.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 69 | 76.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 140 | 135.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/diadora_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/diadora_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 26 |  |  |  |  |  | 1girl, solo, smile, circlet, looking_at_viewer, cape, simple_background, upper_body, white_background, very_long_hair, white_dress |
| 1 | 14 |  |  |  |  |  | 1girl, blush, large_breasts, hetero, 1boy, nipples, penis, open_mouth, completely_nude, blue_hair, cum_in_pussy, hair_ornament, navel, solo_focus, female_pubic_hair, hair_between_eyes, heart, lying, sex, uncensored, vaginal |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | smile | circlet | looking_at_viewer | cape | simple_background | upper_body | white_background | very_long_hair | white_dress | blush | large_breasts | hetero | 1boy | nipples | penis | open_mouth | completely_nude | blue_hair | cum_in_pussy | hair_ornament | navel | solo_focus | female_pubic_hair | hair_between_eyes | heart | lying | sex | uncensored | vaginal |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:----------|:--------------------|:-------|:--------------------|:-------------|:-------------------|:-----------------|:--------------|:--------|:----------------|:---------|:-------|:----------|:--------|:-------------|:------------------|:------------|:---------------|:----------------|:--------|:-------------|:--------------------|:--------------------|:--------|:--------|:------|:-------------|:----------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Will-uob/150_Spectrogram_SD | ---
license: gpl-3.0
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 14979746.0
num_examples: 118
download_size: 14958675
dataset_size: 14979746.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
neelr11/well-pad-global-500-500 | ---
dataset_info:
features:
- name: image
dtype: image
- name: index
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 103120399.0
num_examples: 1000
download_size: 103077439
dataset_size: 103120399.0
---
# Dataset Card for "well-pad-global-500-500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
picocreator/rwkv-4-cpp-quantize-bin | ---
license: apache-2.0
---
You are probably looking for the raven models found here : https://huggingface.co/BlinkDL/rwkv-4-raven
This is a collection of converted CPP binaries, that maybe prequantized.
This is primarily used for the rwkv-cpp-node project here : https://github.com/RWKV/RWKV-cpp-node
|
reubenjohn/stackoverflow-unified-text-open-status-classification-sample | ---
dataset_info:
features:
- name: PostId
dtype: int64
- name: PostCreationDate
dtype: string
- name: OwnerUserId
dtype: int64
- name: OwnerCreationDate
dtype: string
- name: ReputationAtPostCreation
dtype: int64
- name: OwnerUndeletedAnswerCountAtPostTime
dtype: int64
- name: Title
dtype: string
- name: BodyMarkdown
dtype: string
- name: Tag1
dtype: string
- name: Tag2
dtype: string
- name: Tag3
dtype: string
- name: Tag4
dtype: string
- name: Tag5
dtype: string
- name: PostClosedDate
dtype: string
- name: OpenStatus
dtype: string
- name: unified_texts
dtype: string
- name: OpenStatus_id
dtype: int64
splits:
- name: train
num_bytes: 216256197
num_examples: 112217
- name: valid
num_bytes: 43398940
num_examples: 22443
- name: test
num_bytes: 43398940
num_examples: 22443
download_size: 163036345
dataset_size: 303054077
---
# Dataset Card for "stackoverflow-open-status-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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