datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
larrygzlb1994/HolidayRePaint000 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
splits:
- name: train
num_bytes: 1057140898.176
num_examples: 8266
download_size: 1060940822
dataset_size: 1057140898.176
---
# Dataset Card for "HolidayRePaint000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maknee/ggml-vicuna-v0-quantized | ---
license: apache-2.0
tags:
- vicuna
- vicuna-v0
- ggml
pipeline_tag: conversational
language:
- en
- bg
- ca
- cs
- da
- de
- es
- fr
- hr
- hu
- it
- nl
- pl
- pt
- ro
- ru
- sl
- sr
- sv
- uk
library_name: adapter-transformers
---
These are quantized ggml binary files for vicuna 7B and 13B models. The version of vicuna for these models are v0.
These files can be used in conjunction with minigpt4 ggml models [7B](https://huggingface.co/datasets/maknee/minigpt4-7b-ggml) and [13B](https://huggingface.co/datasets/maknee/minigpt4-13b-ggml) in [minigpt4.cpp](https://github.com/Maknee/minigpt4.cpp)
Recommended are the Q5_K and Q6_K implementations. If there are any issues, use Q4_1 or Q4_0.
---
# Vicuna Model Card
## Model details
**Model type:**
Vicuna is an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
Vicuna was trained between March 2023 and April 2023.
**Organizations developing the model:**
The Vicuna team with members from UC Berkeley, CMU, Stanford, and UC San Diego.
**Paper or resources for more information:**
https://vicuna.lmsys.org/
**License:**
Apache License 2.0
**Where to send questions or comments about the model:**
https://github.com/lm-sys/FastChat/issues
## Intended use
**Primary intended uses:**
The primary use of Vicuna is research on large language models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## Training dataset
70K conversations collected from ShareGPT.com.
(48k for the uncensored variant. 22k worth of garbage removed – see https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered)
## Evaluation dataset
A preliminary evaluation of the model quality is conducted by creating a set of 80 diverse questions and utilizing GPT-4 to judge the model outputs. See https://vicuna.lmsys.org/ for more details.
|
anan-2024/twitter_dataset_1713020593 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 106531
num_examples: 274
download_size: 61071
dataset_size: 106531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jimregan/nst_swedish_tts | ---
license: cc0-1.0
task_categories:
- text-to-speech
language:
- sv
---
# Dataset Card for NST Swedish Speech Synthesis (44 kHz)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [NST Swedish Speech Synthesis (44 kHz)](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-18/)
### Dataset Summary
The corpus consists of a single speaker, with 5277 segments.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Swedish.
## Dataset Structure
[Needs More Information]
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
(The below is a partially corrected machine translation from [here](https://www.nb.no/sbfil/dok/nst_taledat_se.pdf) )
The data was developed by Nordisk språkteknologi holding AS (NST),
which went bankrupt in 2003.
In 2006, a jointly owned group of the University of
Oslo, the University of Bergen, the Norwegian University of Science and Technology, the Language Council and IBM
bought the assets of NST, to ensure that the linguistic resources that NST had developed were
take care of. The National Library was commissioned by the Ministry of Culture to build a Norwegian
language bank in 2009, and started this work in 2010.
The resources after NST were transferred to the National Library in May 2011, and they are now done
available in the Language Bank, initially without further processing.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[The Norwegian Language Bank](https://www.nb.no/sprakbanken/en/sprakbanken/)
### Licensing Information
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
[Needs More Information]
### Contributions
[Needs More Information] |
ylacombe/example_process_dataset | ---
dataset_info:
config_name: midlands_male
features:
- name: line_id
dtype: string
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: utterance_pitch_mean
dtype: float32
- name: utterance_pitch_std
dtype: float32
- name: snr
dtype: float64
- name: c50
dtype: float64
- name: speaking_rate
dtype: float64
- name: phonemes
dtype: string
splits:
- name: train
num_bytes: 107980
num_examples: 450
download_size: 63769
dataset_size: 107980
configs:
- config_name: midlands_male
data_files:
- split: train
path: midlands_male/train-*
---
|
jayasuryajsk/Yuin | ---
license: apache-2.0
---
|
CyberHarem/fumizuki_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of fumizuki/文月/文月 (Kantai Collection)
This is the dataset of fumizuki/文月/文月 (Kantai Collection), containing 373 images and their tags.
The core tags of this character are `brown_hair, long_hair, ponytail, brown_eyes`, 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 | 373 | 261.69 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fumizuki_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 373 | 186.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fumizuki_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 779 | 378.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fumizuki_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 373 | 246.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fumizuki_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 779 | 476.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/fumizuki_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/fumizuki_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, black_serafuku, black_skirt, blush, crescent_pin, open_mouth, simple_background, solo, twitter_username, white_necktie, hair_between_eyes, long_sleeves, white_background, black_sailor_collar, black_shirt, looking_at_viewer, neckerchief, one-hour_drawing_challenge, smile, black_socks, kneehighs, pleated_skirt |
| 1 | 6 |  |  |  |  |  | 1girl, black_sailor_collar, black_serafuku, black_shirt, black_skirt, crescent_pin, long_sleeves, pleated_skirt, simple_background, solo, white_necktie, cowboy_shot, looking_at_viewer, white_background, white_neckerchief, hair_between_eyes, open_mouth, smile, blush |
| 2 | 23 |  |  |  |  |  | 1girl, black_sailor_collar, long_sleeves, serafuku, solo, yellow_neckerchief, white_shirt, blue_jacket, crescent_pin, simple_background, smile, blush, open_mouth, white_background, looking_at_viewer, black_skirt, hair_between_eyes, yellow_necktie, upper_body |
| 3 | 6 |  |  |  |  |  | 1girl, solo, looking_at_viewer, navel, open_mouth, blush, white_bikini, armpits, crescent, flat_chest, one-hour_drawing_challenge, simple_background, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_serafuku | black_skirt | blush | crescent_pin | open_mouth | simple_background | solo | twitter_username | white_necktie | hair_between_eyes | long_sleeves | white_background | black_sailor_collar | black_shirt | looking_at_viewer | neckerchief | one-hour_drawing_challenge | smile | black_socks | kneehighs | pleated_skirt | cowboy_shot | white_neckerchief | serafuku | yellow_neckerchief | white_shirt | blue_jacket | yellow_necktie | upper_body | navel | white_bikini | armpits | crescent | flat_chest |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:--------------|:--------|:---------------|:-------------|:--------------------|:-------|:-------------------|:----------------|:--------------------|:---------------|:-------------------|:----------------------|:--------------|:--------------------|:--------------|:-----------------------------|:--------|:--------------|:------------|:----------------|:--------------|:--------------------|:-----------|:---------------------|:--------------|:--------------|:-----------------|:-------------|:--------|:---------------|:----------|:-----------|:-------------|
| 0 | 8 |  |  |  |  |  | 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 | 23 |  |  |  |  |  | X | | X | 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 |
|
distilled-from-one-sec-cv12/chunk_66 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1169675176
num_examples: 227918
download_size: 1192107166
dataset_size: 1169675176
---
# Dataset Card for "chunk_66"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EleutherAI/pythia-memorized-evals | ---
dataset_info:
features:
- name: index
dtype: int64
- name: tokens
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
- name: duped.1.4b
num_bytes: 730820104
num_examples: 1373722
- name: deduped.1.4b
num_bytes: 557587604
num_examples: 1048097
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num_bytes: 366906036
num_examples: 689673
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num_bytes: 309195740
num_examples: 581195
- name: duped.12b
num_bytes: 1267397432
num_examples: 2382326
- name: deduped.12b
num_bytes: 995486380
num_examples: 1871215
- name: duped.70m
num_bytes: 246822996
num_examples: 463953
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num_bytes: 218890336
num_examples: 411448
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num_bytes: 891140964
num_examples: 1675077
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num_bytes: 431472748
num_examples: 811039
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num_bytes: 893916408
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num_examples: 1256141
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- name: duped.12b.23000
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num_bytes: 832077260
num_examples: 1564055
- name: deduped.1b.new
num_bytes: 549484180
num_examples: 1032865
download_size: 4735823411
dataset_size: 16713076324
configs:
- config_name: default
data_files:
- split: duped.12b.23000
path: data/duped.12b.23000-*
- split: duped.12b.43000
path: data/duped.12b.43000-*
- split: duped.12b.63000
path: data/duped.12b.63000-*
- split: duped.12b.83000
path: data/duped.12b.83000-*
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path: data/duped.12b.103000-*
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path: data/duped.12b.123000-*
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path: data/deduped.12b.23000-*
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path: data/deduped.12b.43000-*
- split: deduped.12b.63000
path: data/deduped.12b.63000-*
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path: data/deduped.12b.83000-*
- split: deduped.12b.103000
path: data/deduped.12b.103000-*
- split: deduped.12b.123000
path: data/deduped.12b.123000-*
- split: duped.70m
path: data/duped.70m-*
- split: duped.160m
path: data/duped.160m-*
- split: duped.410m
path: data/duped.410m-*
- split: duped.1b
path: data/duped.1b-*
- split: duped.1.4b
path: data/duped.1.4b-*
- split: duped.2.8b
path: data/duped.2.8b-*
- split: duped.6.9b
path: data/duped.6.9b-*
- split: duped.12b
path: data/duped.12b-*
- split: deduped.70m
path: data/deduped.70m-*
- split: deduped.160m
path: data/deduped.160m-*
- split: deduped.410m
path: data/deduped.410m-*
- split: deduped.1b
path: data/deduped.1b-*
- split: deduped.1.4b
path: data/deduped.1.4b-*
- split: deduped.2.8b
path: data/deduped.2.8b-*
- split: deduped.6.9b
path: data/deduped.6.9b-*
- split: deduped.12b
path: data/deduped.12b-*
- split: deduped.1b.new
path: data/deduped.1b.new-*
---
# Dataset Card for "pythia-memorized-evals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Binaryy/reddit-multimodal-sample | ---
dataset_info:
features:
- name: image
dtype: image
- name: Title
dtype: string
- name: Image URL
dtype: string
- name: File Path
dtype: string
splits:
- name: train
num_bytes: 1954008168.804
num_examples: 1638
download_size: 1843573587
dataset_size: 1954008168.804
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "reddit-multimodal-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_kimou605__shadow-clown-BioMistral-7B-DARE | ---
pretty_name: Evaluation run of kimou605/shadow-clown-BioMistral-7B-DARE
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [kimou605/shadow-clown-BioMistral-7B-DARE](https://huggingface.co/kimou605/shadow-clown-BioMistral-7B-DARE)\
\ 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_kimou605__shadow-clown-BioMistral-7B-DARE\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-15T03:43:27.988905](https://huggingface.co/datasets/open-llm-leaderboard/details_kimou605__shadow-clown-BioMistral-7B-DARE/blob/main/results_2024-03-15T03-43-27.988905.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\": 0.6437006121406286,\n\
\ \"acc_stderr\": 0.03219656527666736,\n \"acc_norm\": 0.6450809586881737,\n\
\ \"acc_norm_stderr\": 0.0328491614180031,\n \"mc1\": 0.5104039167686658,\n\
\ \"mc1_stderr\": 0.017499711430249268,\n \"mc2\": 0.6767936843695579,\n\
\ \"mc2_stderr\": 0.014902102789887895\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6510238907849829,\n \"acc_stderr\": 0.013928933461382506,\n\
\ \"acc_norm\": 0.674061433447099,\n \"acc_norm_stderr\": 0.013697432466693246\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6832304321848237,\n\
\ \"acc_stderr\": 0.0046426596138574,\n \"acc_norm\": 0.867755427205736,\n\
\ \"acc_norm_stderr\": 0.0033806414709899296\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720386,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720386\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.743421052631579,\n \"acc_stderr\": 0.0355418036802569,\n\
\ \"acc_norm\": 0.743421052631579,\n \"acc_norm_stderr\": 0.0355418036802569\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7245283018867924,\n \"acc_stderr\": 0.027495663683724067,\n\
\ \"acc_norm\": 0.7245283018867924,\n \"acc_norm_stderr\": 0.027495663683724067\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\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\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062947,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062947\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.032436186361081004,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.032436186361081004\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.02546714904546955,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.02546714904546955\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7548387096774194,\n \"acc_stderr\": 0.024472243840895518,\n \"\
acc_norm\": 0.7548387096774194,\n \"acc_norm_stderr\": 0.024472243840895518\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n \"\
acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6794871794871795,\n \"acc_stderr\": 0.02366129639396428,\n \
\ \"acc_norm\": 0.6794871794871795,\n \"acc_norm_stderr\": 0.02366129639396428\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3111111111111111,\n \"acc_stderr\": 0.028226446749683515,\n \
\ \"acc_norm\": 0.3111111111111111,\n \"acc_norm_stderr\": 0.028226446749683515\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\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.01584825580650156,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.01584825580650156\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5462962962962963,\n \"acc_stderr\": 0.033953227263757976,\n \"\
acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.033953227263757976\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.025955020841621112,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.025955020841621112\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699813,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699813\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\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\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690877,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690877\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092368,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092368\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8314176245210728,\n\
\ \"acc_stderr\": 0.013387895731543604,\n \"acc_norm\": 0.8314176245210728,\n\
\ \"acc_norm_stderr\": 0.013387895731543604\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7398843930635838,\n \"acc_stderr\": 0.023618678310069363,\n\
\ \"acc_norm\": 0.7398843930635838,\n \"acc_norm_stderr\": 0.023618678310069363\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4134078212290503,\n\
\ \"acc_stderr\": 0.01646981492840617,\n \"acc_norm\": 0.4134078212290503,\n\
\ \"acc_norm_stderr\": 0.01646981492840617\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279046,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279046\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.02616058445014045,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.02616058445014045\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45436766623207303,\n\
\ \"acc_stderr\": 0.012716941720734804,\n \"acc_norm\": 0.45436766623207303,\n\
\ \"acc_norm_stderr\": 0.012716941720734804\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7272727272727273,\n\
\ \"acc_stderr\": 0.04265792110940589,\n \"acc_norm\": 0.7272727272727273,\n\
\ \"acc_norm_stderr\": 0.04265792110940589\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128438,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128438\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454132,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454132\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5104039167686658,\n\
\ \"mc1_stderr\": 0.017499711430249268,\n \"mc2\": 0.6767936843695579,\n\
\ \"mc2_stderr\": 0.014902102789887895\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8161010260457774,\n \"acc_stderr\": 0.010887916013305892\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5974222896133434,\n \
\ \"acc_stderr\": 0.01350852306366343\n }\n}\n```"
repo_url: https://huggingface.co/kimou605/shadow-clown-BioMistral-7B-DARE
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|arc:challenge|25_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|gsm8k|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hellaswag|10_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-15T03-43-27.988905.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-15T03-43-27.988905.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- '**/details_harness|winogrande|5_2024-03-15T03-43-27.988905.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-15T03-43-27.988905.parquet'
- config_name: results
data_files:
- split: 2024_03_15T03_43_27.988905
path:
- results_2024-03-15T03-43-27.988905.parquet
- split: latest
path:
- results_2024-03-15T03-43-27.988905.parquet
---
# Dataset Card for Evaluation run of kimou605/shadow-clown-BioMistral-7B-DARE
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kimou605/shadow-clown-BioMistral-7B-DARE](https://huggingface.co/kimou605/shadow-clown-BioMistral-7B-DARE) 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_kimou605__shadow-clown-BioMistral-7B-DARE",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-15T03:43:27.988905](https://huggingface.co/datasets/open-llm-leaderboard/details_kimou605__shadow-clown-BioMistral-7B-DARE/blob/main/results_2024-03-15T03-43-27.988905.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": 0.6437006121406286,
"acc_stderr": 0.03219656527666736,
"acc_norm": 0.6450809586881737,
"acc_norm_stderr": 0.0328491614180031,
"mc1": 0.5104039167686658,
"mc1_stderr": 0.017499711430249268,
"mc2": 0.6767936843695579,
"mc2_stderr": 0.014902102789887895
},
"harness|arc:challenge|25": {
"acc": 0.6510238907849829,
"acc_stderr": 0.013928933461382506,
"acc_norm": 0.674061433447099,
"acc_norm_stderr": 0.013697432466693246
},
"harness|hellaswag|10": {
"acc": 0.6832304321848237,
"acc_stderr": 0.0046426596138574,
"acc_norm": 0.867755427205736,
"acc_norm_stderr": 0.0033806414709899296
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720683,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720683
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720386,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720386
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.743421052631579,
"acc_stderr": 0.0355418036802569,
"acc_norm": 0.743421052631579,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7245283018867924,
"acc_stderr": 0.027495663683724067,
"acc_norm": 0.7245283018867924,
"acc_norm_stderr": 0.027495663683724067
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488584
},
"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": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062947,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062947
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.032436186361081004,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.032436186361081004
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42592592592592593,
"acc_stderr": 0.02546714904546955,
"acc_norm": 0.42592592592592593,
"acc_norm_stderr": 0.02546714904546955
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7548387096774194,
"acc_stderr": 0.024472243840895518,
"acc_norm": 0.7548387096774194,
"acc_norm_stderr": 0.024472243840895518
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267042,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267042
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593556,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593556
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6794871794871795,
"acc_stderr": 0.02366129639396428,
"acc_norm": 0.6794871794871795,
"acc_norm_stderr": 0.02366129639396428
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3111111111111111,
"acc_stderr": 0.028226446749683515,
"acc_norm": 0.3111111111111111,
"acc_norm_stderr": 0.028226446749683515
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.03958027231121569,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8366972477064221,
"acc_stderr": 0.01584825580650156,
"acc_norm": 0.8366972477064221,
"acc_norm_stderr": 0.01584825580650156
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5462962962962963,
"acc_stderr": 0.033953227263757976,
"acc_norm": 0.5462962962962963,
"acc_norm_stderr": 0.033953227263757976
},
"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": 0.8016877637130801,
"acc_stderr": 0.025955020841621112,
"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.025955020841621112
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7040358744394619,
"acc_stderr": 0.030636591348699813,
"acc_norm": 0.7040358744394619,
"acc_norm_stderr": 0.030636591348699813
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.037683359597287434,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.03984979653302872,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.03984979653302872
},
"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": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690877,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690877
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092368,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092368
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8314176245210728,
"acc_stderr": 0.013387895731543604,
"acc_norm": 0.8314176245210728,
"acc_norm_stderr": 0.013387895731543604
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7398843930635838,
"acc_stderr": 0.023618678310069363,
"acc_norm": 0.7398843930635838,
"acc_norm_stderr": 0.023618678310069363
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4134078212290503,
"acc_stderr": 0.01646981492840617,
"acc_norm": 0.4134078212290503,
"acc_norm_stderr": 0.01646981492840617
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7058823529411765,
"acc_stderr": 0.026090162504279046,
"acc_norm": 0.7058823529411765,
"acc_norm_stderr": 0.026090162504279046
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6945337620578779,
"acc_stderr": 0.02616058445014045,
"acc_norm": 0.6945337620578779,
"acc_norm_stderr": 0.02616058445014045
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7253086419753086,
"acc_stderr": 0.024836057868294677,
"acc_norm": 0.7253086419753086,
"acc_norm_stderr": 0.024836057868294677
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.45436766623207303,
"acc_stderr": 0.012716941720734804,
"acc_norm": 0.45436766623207303,
"acc_norm_stderr": 0.012716941720734804
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6397058823529411,
"acc_stderr": 0.029163128570670733,
"acc_norm": 0.6397058823529411,
"acc_norm_stderr": 0.029163128570670733
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6503267973856209,
"acc_stderr": 0.01929196189506638,
"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.01929196189506638
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04265792110940589,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04265792110940589
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128438,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128438
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454132,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454132
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.03487350880197769,
"acc_norm": 0.86,
"acc_norm_stderr": 0.03487350880197769
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8421052631578947,
"acc_stderr": 0.027966785859160893,
"acc_norm": 0.8421052631578947,
"acc_norm_stderr": 0.027966785859160893
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5104039167686658,
"mc1_stderr": 0.017499711430249268,
"mc2": 0.6767936843695579,
"mc2_stderr": 0.014902102789887895
},
"harness|winogrande|5": {
"acc": 0.8161010260457774,
"acc_stderr": 0.010887916013305892
},
"harness|gsm8k|5": {
"acc": 0.5974222896133434,
"acc_stderr": 0.01350852306366343
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
TriadParty/deepsword | ---
license: apache-2.0
---
|
nlpso/m1_qualitative_analysis_ref_cmbert_iob2 | ---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---
# m1_qualitative_analysis_ref_cmbert_iob2
## Introduction
This dataset was used to perform **qualitative analysis** of [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) on **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approach : M1
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IOB2
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* Level-1 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_1](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_1)
* Level 2 : [nlpso/m1_ind_layers_ref_cmbert_iob2_level_2](https://huggingface.co/nlpso/m1_ind_layers_ref_cmbert_iob2_level_2)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m1_qualitative_analysis_ref_cmbert_iob2")
|
Felladrin/ChatML-aya_dataset | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
annotations_creators:
- crowdsourced
- expert-generated
language:
- amh
- arb
- ary
- ars
- acq
- arz
- apc
- ben
- ceb
- dan
- deu
- ell
- eng
- eus
- fil
- fin
- fra
- gle
- guj
- hat
- hau
- hin
- hun
- ibo
- ind
- ita
- jav
- jpn
- kan
- kir
- kor
- kur
- lit
- mal
- mar
- mlg
- msa
- mya
- nep
- nld
- nso
- nya
- pan
- pes
- pol
- por
- pus
- rus
- sin
- sna
- snd
- som
- spa
- sqi
- srp
- sun
- swa
- swe
- tam
- tel
- tha
- tur
- ukr
- urd
- vie
- wol
- xho
- yor
- zho
- zul
language_creators:
- crowdsourced
- expert-generated
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
---
[CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) in ChatML format, ready to use in [HuggingFace TRL's SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer).
Python code used for conversion:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("CohereForAI/aya_dataset", split="train")
def format(columns):
messages = [
{
"role": "user",
"content": columns["inputs"].strip(),
},
{
"role": "assistant",
"content": columns["targets"].strip(),
},
]
return { "text": tokenizer.apply_chat_template(messages, tokenize=False) }
dataset.map(format).select_columns(['text', 'language', 'language_code', 'annotation_type', 'user_id']).to_parquet("train.parquet")
```
|
yapayzeka/meto | ---
license: openrail
---
|
open-llm-leaderboard/details_glenn2__gemma-2b-lora3 | ---
pretty_name: Evaluation run of glenn2/gemma-2b-lora3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [glenn2/gemma-2b-lora3](https://huggingface.co/glenn2/gemma-2b-lora3) 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_glenn2__gemma-2b-lora3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-29T12:17:16.722780](https://huggingface.co/datasets/open-llm-leaderboard/details_glenn2__gemma-2b-lora3/blob/main/results_2024-02-29T12-17-16.722780.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\": 0.38561103829004145,\n\
\ \"acc_stderr\": 0.034063362609756336,\n \"acc_norm\": 0.3876919839462579,\n\
\ \"acc_norm_stderr\": 0.034808766714813213,\n \"mc1\": 0.2423500611995104,\n\
\ \"mc1_stderr\": 0.015000674373570338,\n \"mc2\": 0.3642319222186389,\n\
\ \"mc2_stderr\": 0.013786837331146062\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4616040955631399,\n \"acc_stderr\": 0.01456824555029636,\n\
\ \"acc_norm\": 0.4726962457337884,\n \"acc_norm_stderr\": 0.014589589101985996\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5294761999601673,\n\
\ \"acc_stderr\": 0.004981103157940433,\n \"acc_norm\": 0.7182832105158335,\n\
\ \"acc_norm_stderr\": 0.004489166767430652\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.35526315789473684,\n \"acc_stderr\": 0.038947344870133176,\n\
\ \"acc_norm\": 0.35526315789473684,\n \"acc_norm_stderr\": 0.038947344870133176\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.35,\n\
\ \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\": 0.35,\n \
\ \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.45660377358490567,\n \"acc_stderr\": 0.030656748696739435,\n\
\ \"acc_norm\": 0.45660377358490567,\n \"acc_norm_stderr\": 0.030656748696739435\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n\
\ \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3468208092485549,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.3468208092485549,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171452,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171452\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.03202563076101735,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.03202563076101735\n },\n\
\ \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n\
\ \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.3586206896551724,\n \"acc_stderr\": 0.03996629574876719,\n\
\ \"acc_norm\": 0.3586206896551724,\n \"acc_norm_stderr\": 0.03996629574876719\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3148148148148148,\n \"acc_stderr\": 0.023919984164047732,\n \"\
acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.023919984164047732\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.18253968253968253,\n\
\ \"acc_stderr\": 0.03455071019102149,\n \"acc_norm\": 0.18253968253968253,\n\
\ \"acc_norm_stderr\": 0.03455071019102149\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.4032258064516129,\n \"acc_stderr\": 0.027906150826041146,\n \"\
acc_norm\": 0.4032258064516129,\n \"acc_norm_stderr\": 0.027906150826041146\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.35467980295566504,\n \"acc_stderr\": 0.03366124489051449,\n \"\
acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.03366124489051449\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\"\
: 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.41818181818181815,\n \"acc_stderr\": 0.03851716319398395,\n\
\ \"acc_norm\": 0.41818181818181815,\n \"acc_norm_stderr\": 0.03851716319398395\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.4393939393939394,\n \"acc_stderr\": 0.035360859475294805,\n \"\
acc_norm\": 0.4393939393939394,\n \"acc_norm_stderr\": 0.035360859475294805\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.49222797927461137,\n \"acc_stderr\": 0.03608003225569654,\n\
\ \"acc_norm\": 0.49222797927461137,\n \"acc_norm_stderr\": 0.03608003225569654\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.3076923076923077,\n \"acc_stderr\": 0.02340092891831051,\n \
\ \"acc_norm\": 0.3076923076923077,\n \"acc_norm_stderr\": 0.02340092891831051\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507383,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507383\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3319327731092437,\n \"acc_stderr\": 0.030588697013783663,\n\
\ \"acc_norm\": 0.3319327731092437,\n \"acc_norm_stderr\": 0.030588697013783663\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.25165562913907286,\n \"acc_stderr\": 0.03543304234389985,\n \"\
acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.03543304234389985\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.46422018348623856,\n \"acc_stderr\": 0.021382364775701906,\n \"\
acc_norm\": 0.46422018348623856,\n \"acc_norm_stderr\": 0.021382364775701906\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.25462962962962965,\n \"acc_stderr\": 0.029711275860005337,\n \"\
acc_norm\": 0.25462962962962965,\n \"acc_norm_stderr\": 0.029711275860005337\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.39215686274509803,\n \"acc_stderr\": 0.03426712349247272,\n \"\
acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.03426712349247272\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.4092827004219409,\n \"acc_stderr\": 0.032007041833595914,\n \
\ \"acc_norm\": 0.4092827004219409,\n \"acc_norm_stderr\": 0.032007041833595914\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.45739910313901344,\n\
\ \"acc_stderr\": 0.033435777055830646,\n \"acc_norm\": 0.45739910313901344,\n\
\ \"acc_norm_stderr\": 0.033435777055830646\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.35877862595419846,\n \"acc_stderr\": 0.04206739313864908,\n\
\ \"acc_norm\": 0.35877862595419846,\n \"acc_norm_stderr\": 0.04206739313864908\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.5537190082644629,\n \"acc_stderr\": 0.0453793517794788,\n \"acc_norm\"\
: 0.5537190082644629,\n \"acc_norm_stderr\": 0.0453793517794788\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.37962962962962965,\n\
\ \"acc_stderr\": 0.04691521224077742,\n \"acc_norm\": 0.37962962962962965,\n\
\ \"acc_norm_stderr\": 0.04691521224077742\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.36809815950920244,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.36809815950920244,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\
\ \"acc_stderr\": 0.04432804055291519,\n \"acc_norm\": 0.32142857142857145,\n\
\ \"acc_norm_stderr\": 0.04432804055291519\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.49514563106796117,\n \"acc_stderr\": 0.04950504382128921,\n\
\ \"acc_norm\": 0.49514563106796117,\n \"acc_norm_stderr\": 0.04950504382128921\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5726495726495726,\n\
\ \"acc_stderr\": 0.032408473935163266,\n \"acc_norm\": 0.5726495726495726,\n\
\ \"acc_norm_stderr\": 0.032408473935163266\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.5325670498084292,\n\
\ \"acc_stderr\": 0.017841995750520874,\n \"acc_norm\": 0.5325670498084292,\n\
\ \"acc_norm_stderr\": 0.017841995750520874\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.34971098265895956,\n \"acc_stderr\": 0.02567428145653102,\n\
\ \"acc_norm\": 0.34971098265895956,\n \"acc_norm_stderr\": 0.02567428145653102\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2446927374301676,\n\
\ \"acc_stderr\": 0.01437816988409843,\n \"acc_norm\": 0.2446927374301676,\n\
\ \"acc_norm_stderr\": 0.01437816988409843\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.3464052287581699,\n \"acc_stderr\": 0.027245613047215355,\n\
\ \"acc_norm\": 0.3464052287581699,\n \"acc_norm_stderr\": 0.027245613047215355\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4115755627009646,\n\
\ \"acc_stderr\": 0.02795048149440127,\n \"acc_norm\": 0.4115755627009646,\n\
\ \"acc_norm_stderr\": 0.02795048149440127\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.44753086419753085,\n \"acc_stderr\": 0.027667138569422697,\n\
\ \"acc_norm\": 0.44753086419753085,\n \"acc_norm_stderr\": 0.027667138569422697\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.26595744680851063,\n \"acc_stderr\": 0.0263580656988806,\n \
\ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.0263580656988806\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.31747066492829207,\n\
\ \"acc_stderr\": 0.01188889206880931,\n \"acc_norm\": 0.31747066492829207,\n\
\ \"acc_norm_stderr\": 0.01188889206880931\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.28308823529411764,\n \"acc_stderr\": 0.02736586113151381,\n\
\ \"acc_norm\": 0.28308823529411764,\n \"acc_norm_stderr\": 0.02736586113151381\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.37745098039215685,\n \"acc_stderr\": 0.019610851474880286,\n \
\ \"acc_norm\": 0.37745098039215685,\n \"acc_norm_stderr\": 0.019610851474880286\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.41818181818181815,\n\
\ \"acc_stderr\": 0.04724577405731571,\n \"acc_norm\": 0.41818181818181815,\n\
\ \"acc_norm_stderr\": 0.04724577405731571\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.46122448979591835,\n \"acc_stderr\": 0.03191282052669277,\n\
\ \"acc_norm\": 0.46122448979591835,\n \"acc_norm_stderr\": 0.03191282052669277\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.4925373134328358,\n\
\ \"acc_stderr\": 0.035351400842767194,\n \"acc_norm\": 0.4925373134328358,\n\
\ \"acc_norm_stderr\": 0.035351400842767194\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.43373493975903615,\n\
\ \"acc_stderr\": 0.03858158940685517,\n \"acc_norm\": 0.43373493975903615,\n\
\ \"acc_norm_stderr\": 0.03858158940685517\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.03811079669833531,\n\
\ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03811079669833531\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2423500611995104,\n\
\ \"mc1_stderr\": 0.015000674373570338,\n \"mc2\": 0.3642319222186389,\n\
\ \"mc2_stderr\": 0.013786837331146062\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6724546172059984,\n \"acc_stderr\": 0.013190169546797017\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17589082638362397,\n \
\ \"acc_stderr\": 0.01048712063553962\n }\n}\n```"
repo_url: https://huggingface.co/glenn2/gemma-2b-lora3
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_29T12_17_16.722780
path:
- '**/details_harness|arc:challenge|25_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|gsm8k|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hellaswag|10_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-17-16.722780.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T12-17-16.722780.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- '**/details_harness|winogrande|5_2024-02-29T12-17-16.722780.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-29T12-17-16.722780.parquet'
- config_name: results
data_files:
- split: 2024_02_29T12_17_16.722780
path:
- results_2024-02-29T12-17-16.722780.parquet
- split: latest
path:
- results_2024-02-29T12-17-16.722780.parquet
---
# Dataset Card for Evaluation run of glenn2/gemma-2b-lora3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [glenn2/gemma-2b-lora3](https://huggingface.co/glenn2/gemma-2b-lora3) 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_glenn2__gemma-2b-lora3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-29T12:17:16.722780](https://huggingface.co/datasets/open-llm-leaderboard/details_glenn2__gemma-2b-lora3/blob/main/results_2024-02-29T12-17-16.722780.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": 0.38561103829004145,
"acc_stderr": 0.034063362609756336,
"acc_norm": 0.3876919839462579,
"acc_norm_stderr": 0.034808766714813213,
"mc1": 0.2423500611995104,
"mc1_stderr": 0.015000674373570338,
"mc2": 0.3642319222186389,
"mc2_stderr": 0.013786837331146062
},
"harness|arc:challenge|25": {
"acc": 0.4616040955631399,
"acc_stderr": 0.01456824555029636,
"acc_norm": 0.4726962457337884,
"acc_norm_stderr": 0.014589589101985996
},
"harness|hellaswag|10": {
"acc": 0.5294761999601673,
"acc_stderr": 0.004981103157940433,
"acc_norm": 0.7182832105158335,
"acc_norm_stderr": 0.004489166767430652
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.04605661864718381,
"acc_norm": 0.3,
"acc_norm_stderr": 0.04605661864718381
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.35526315789473684,
"acc_stderr": 0.038947344870133176,
"acc_norm": 0.35526315789473684,
"acc_norm_stderr": 0.038947344870133176
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.45660377358490567,
"acc_stderr": 0.030656748696739435,
"acc_norm": 0.45660377358490567,
"acc_norm_stderr": 0.030656748696739435
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4375,
"acc_stderr": 0.04148415739394154,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04148415739394154
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.24,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3468208092485549,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.3468208092485549,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.04220773659171452,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.04220773659171452
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4,
"acc_stderr": 0.03202563076101735,
"acc_norm": 0.4,
"acc_norm_stderr": 0.03202563076101735
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.04303684033537315,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.04303684033537315
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.3586206896551724,
"acc_stderr": 0.03996629574876719,
"acc_norm": 0.3586206896551724,
"acc_norm_stderr": 0.03996629574876719
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.023919984164047732,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.023919984164047732
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.18253968253968253,
"acc_stderr": 0.03455071019102149,
"acc_norm": 0.18253968253968253,
"acc_norm_stderr": 0.03455071019102149
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.4032258064516129,
"acc_stderr": 0.027906150826041146,
"acc_norm": 0.4032258064516129,
"acc_norm_stderr": 0.027906150826041146
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.35467980295566504,
"acc_stderr": 0.03366124489051449,
"acc_norm": 0.35467980295566504,
"acc_norm_stderr": 0.03366124489051449
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.41818181818181815,
"acc_stderr": 0.03851716319398395,
"acc_norm": 0.41818181818181815,
"acc_norm_stderr": 0.03851716319398395
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.4393939393939394,
"acc_stderr": 0.035360859475294805,
"acc_norm": 0.4393939393939394,
"acc_norm_stderr": 0.035360859475294805
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.49222797927461137,
"acc_stderr": 0.03608003225569654,
"acc_norm": 0.49222797927461137,
"acc_norm_stderr": 0.03608003225569654
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.3076923076923077,
"acc_stderr": 0.02340092891831051,
"acc_norm": 0.3076923076923077,
"acc_norm_stderr": 0.02340092891831051
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26666666666666666,
"acc_stderr": 0.02696242432507383,
"acc_norm": 0.26666666666666666,
"acc_norm_stderr": 0.02696242432507383
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3319327731092437,
"acc_stderr": 0.030588697013783663,
"acc_norm": 0.3319327731092437,
"acc_norm_stderr": 0.030588697013783663
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.25165562913907286,
"acc_stderr": 0.03543304234389985,
"acc_norm": 0.25165562913907286,
"acc_norm_stderr": 0.03543304234389985
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.46422018348623856,
"acc_stderr": 0.021382364775701906,
"acc_norm": 0.46422018348623856,
"acc_norm_stderr": 0.021382364775701906
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.25462962962962965,
"acc_stderr": 0.029711275860005337,
"acc_norm": 0.25462962962962965,
"acc_norm_stderr": 0.029711275860005337
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.03426712349247272,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.03426712349247272
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.4092827004219409,
"acc_stderr": 0.032007041833595914,
"acc_norm": 0.4092827004219409,
"acc_norm_stderr": 0.032007041833595914
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.45739910313901344,
"acc_stderr": 0.033435777055830646,
"acc_norm": 0.45739910313901344,
"acc_norm_stderr": 0.033435777055830646
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.35877862595419846,
"acc_stderr": 0.04206739313864908,
"acc_norm": 0.35877862595419846,
"acc_norm_stderr": 0.04206739313864908
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5537190082644629,
"acc_stderr": 0.0453793517794788,
"acc_norm": 0.5537190082644629,
"acc_norm_stderr": 0.0453793517794788
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.37962962962962965,
"acc_stderr": 0.04691521224077742,
"acc_norm": 0.37962962962962965,
"acc_norm_stderr": 0.04691521224077742
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.36809815950920244,
"acc_stderr": 0.03789213935838396,
"acc_norm": 0.36809815950920244,
"acc_norm_stderr": 0.03789213935838396
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.32142857142857145,
"acc_stderr": 0.04432804055291519,
"acc_norm": 0.32142857142857145,
"acc_norm_stderr": 0.04432804055291519
},
"harness|hendrycksTest-management|5": {
"acc": 0.49514563106796117,
"acc_stderr": 0.04950504382128921,
"acc_norm": 0.49514563106796117,
"acc_norm_stderr": 0.04950504382128921
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.5726495726495726,
"acc_stderr": 0.032408473935163266,
"acc_norm": 0.5726495726495726,
"acc_norm_stderr": 0.032408473935163266
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.5325670498084292,
"acc_stderr": 0.017841995750520874,
"acc_norm": 0.5325670498084292,
"acc_norm_stderr": 0.017841995750520874
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.34971098265895956,
"acc_stderr": 0.02567428145653102,
"acc_norm": 0.34971098265895956,
"acc_norm_stderr": 0.02567428145653102
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2446927374301676,
"acc_stderr": 0.01437816988409843,
"acc_norm": 0.2446927374301676,
"acc_norm_stderr": 0.01437816988409843
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.3464052287581699,
"acc_stderr": 0.027245613047215355,
"acc_norm": 0.3464052287581699,
"acc_norm_stderr": 0.027245613047215355
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4115755627009646,
"acc_stderr": 0.02795048149440127,
"acc_norm": 0.4115755627009646,
"acc_norm_stderr": 0.02795048149440127
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.44753086419753085,
"acc_stderr": 0.027667138569422697,
"acc_norm": 0.44753086419753085,
"acc_norm_stderr": 0.027667138569422697
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.26595744680851063,
"acc_stderr": 0.0263580656988806,
"acc_norm": 0.26595744680851063,
"acc_norm_stderr": 0.0263580656988806
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.31747066492829207,
"acc_stderr": 0.01188889206880931,
"acc_norm": 0.31747066492829207,
"acc_norm_stderr": 0.01188889206880931
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.28308823529411764,
"acc_stderr": 0.02736586113151381,
"acc_norm": 0.28308823529411764,
"acc_norm_stderr": 0.02736586113151381
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.37745098039215685,
"acc_stderr": 0.019610851474880286,
"acc_norm": 0.37745098039215685,
"acc_norm_stderr": 0.019610851474880286
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.41818181818181815,
"acc_stderr": 0.04724577405731571,
"acc_norm": 0.41818181818181815,
"acc_norm_stderr": 0.04724577405731571
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.46122448979591835,
"acc_stderr": 0.03191282052669277,
"acc_norm": 0.46122448979591835,
"acc_norm_stderr": 0.03191282052669277
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.4925373134328358,
"acc_stderr": 0.035351400842767194,
"acc_norm": 0.4925373134328358,
"acc_norm_stderr": 0.035351400842767194
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-virology|5": {
"acc": 0.43373493975903615,
"acc_stderr": 0.03858158940685517,
"acc_norm": 0.43373493975903615,
"acc_norm_stderr": 0.03858158940685517
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.03811079669833531,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.03811079669833531
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2423500611995104,
"mc1_stderr": 0.015000674373570338,
"mc2": 0.3642319222186389,
"mc2_stderr": 0.013786837331146062
},
"harness|winogrande|5": {
"acc": 0.6724546172059984,
"acc_stderr": 0.013190169546797017
},
"harness|gsm8k|5": {
"acc": 0.17589082638362397,
"acc_stderr": 0.01048712063553962
}
}
```
## 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] |
eswar-2001/haskell-packages-meta-hoogle | ---
license: unknown
---
|
S-AA-D/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4200526
num_examples: 1000
download_size: 2248768
dataset_size: 4200526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_258 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1204130700.0
num_examples: 236475
download_size: 1228465140
dataset_size: 1204130700.0
---
# Dataset Card for "chunk_258"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
timothyckl/OpenOrca-20k | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 33921209
num_examples: 20000
download_size: 19510634
dataset_size: 33921209
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "OpenOrca-20k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0
num_bytes: 12831
num_examples: 10
download_size: 13218
dataset_size: 12831
---
# Dataset Card for "VQAv2_sample_validation_text_davinci_003_mode_T_A_D_PNP_NO_FILTER_C_Q_rices_ns_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_1713021234 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 59817487
num_examples: 155249
download_size: 30484231
dataset_size: 59817487
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Nitral-AI__KukulStanta-7B | ---
pretty_name: Evaluation run of Nitral-AI/KukulStanta-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Nitral-AI/KukulStanta-7B](https://huggingface.co/Nitral-AI/KukulStanta-7B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Nitral-AI__KukulStanta-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-02T22:02:03.624829](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__KukulStanta-7B/blob/main/results_2024-04-02T22-02-03.624829.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\": 0.6530472574937952,\n\
\ \"acc_stderr\": 0.032066627003960894,\n \"acc_norm\": 0.6542386436485469,\n\
\ \"acc_norm_stderr\": 0.032713718640515456,\n \"mc1\": 0.4467564259485924,\n\
\ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6218700196422391,\n\
\ \"mc2_stderr\": 0.015309530764000967\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.658703071672355,\n \"acc_stderr\": 0.013855831287497723,\n\
\ \"acc_norm\": 0.6843003412969283,\n \"acc_norm_stderr\": 0.013582571095815293\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6879107747460665,\n\
\ \"acc_stderr\": 0.004623990785158488,\n \"acc_norm\": 0.8636725751842262,\n\
\ \"acc_norm_stderr\": 0.0034243464481037203\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249386,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249386\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\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\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246483,\n \"\
acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246483\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"\
acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"\
acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8181818181818182,\n \"acc_stderr\": 0.0274796030105388,\n \"acc_norm\"\
: 0.8181818181818182,\n \"acc_norm_stderr\": 0.0274796030105388\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6820512820512821,\n \"acc_stderr\": 0.023610884308927865,\n\
\ \"acc_norm\": 0.6820512820512821,\n \"acc_norm_stderr\": 0.023610884308927865\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.02911661760608301,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.02911661760608301\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\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8330275229357799,\n \"acc_stderr\": 0.01599015488507337,\n \"\
acc_norm\": 0.8330275229357799,\n \"acc_norm_stderr\": 0.01599015488507337\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931796,\n \"\
acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931796\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.02553010046023349,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.02553010046023349\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\": 0.7786259541984732,\n \"acc_stderr\": 0.03641297081313729,\n\
\ \"acc_norm\": 0.7786259541984732,\n \"acc_norm_stderr\": 0.03641297081313729\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8301404853128991,\n\
\ \"acc_stderr\": 0.013428186370608308,\n \"acc_norm\": 0.8301404853128991,\n\
\ \"acc_norm_stderr\": 0.013428186370608308\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7196531791907514,\n \"acc_stderr\": 0.024182427496577615,\n\
\ \"acc_norm\": 0.7196531791907514,\n \"acc_norm_stderr\": 0.024182427496577615\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39888268156424583,\n\
\ \"acc_stderr\": 0.01637696614261008,\n \"acc_norm\": 0.39888268156424583,\n\
\ \"acc_norm_stderr\": 0.01637696614261008\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7352941176470589,\n \"acc_stderr\": 0.025261691219729484,\n\
\ \"acc_norm\": 0.7352941176470589,\n \"acc_norm_stderr\": 0.025261691219729484\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.02438366553103545,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.02438366553103545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \
\ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46740547588005216,\n\
\ \"acc_stderr\": 0.012743072942653356,\n \"acc_norm\": 0.46740547588005216,\n\
\ \"acc_norm_stderr\": 0.012743072942653356\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6985294117647058,\n \"acc_stderr\": 0.027875982114273168,\n\
\ \"acc_norm\": 0.6985294117647058,\n \"acc_norm_stderr\": 0.027875982114273168\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.01895088677080631,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.01895088677080631\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.04350271442923243,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.04350271442923243\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454115,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454115\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4467564259485924,\n\
\ \"mc1_stderr\": 0.017403977522557144,\n \"mc2\": 0.6218700196422391,\n\
\ \"mc2_stderr\": 0.015309530764000967\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8003157063930545,\n \"acc_stderr\": 0.01123532838262585\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6368460955269143,\n \
\ \"acc_stderr\": 0.013246614539839868\n }\n}\n```"
repo_url: https://huggingface.co/Nitral-AI/KukulStanta-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|arc:challenge|25_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|gsm8k|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hellaswag|10_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-02-03.624829.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T22-02-03.624829.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- '**/details_harness|winogrande|5_2024-04-02T22-02-03.624829.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-02T22-02-03.624829.parquet'
- config_name: results
data_files:
- split: 2024_04_02T22_02_03.624829
path:
- results_2024-04-02T22-02-03.624829.parquet
- split: latest
path:
- results_2024-04-02T22-02-03.624829.parquet
---
# Dataset Card for Evaluation run of Nitral-AI/KukulStanta-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Nitral-AI/KukulStanta-7B](https://huggingface.co/Nitral-AI/KukulStanta-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Nitral-AI__KukulStanta-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-02T22:02:03.624829](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__KukulStanta-7B/blob/main/results_2024-04-02T22-02-03.624829.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": 0.6530472574937952,
"acc_stderr": 0.032066627003960894,
"acc_norm": 0.6542386436485469,
"acc_norm_stderr": 0.032713718640515456,
"mc1": 0.4467564259485924,
"mc1_stderr": 0.017403977522557144,
"mc2": 0.6218700196422391,
"mc2_stderr": 0.015309530764000967
},
"harness|arc:challenge|25": {
"acc": 0.658703071672355,
"acc_stderr": 0.013855831287497723,
"acc_norm": 0.6843003412969283,
"acc_norm_stderr": 0.013582571095815293
},
"harness|hellaswag|10": {
"acc": 0.6879107747460665,
"acc_stderr": 0.004623990785158488,
"acc_norm": 0.8636725751842262,
"acc_norm_stderr": 0.0034243464481037203
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249386,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249386
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"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": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.025197101074246483,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.025197101074246483
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.02413763242933771,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.02413763242933771
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.032568666616811015,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.032568666616811015
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.0274796030105388,
"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.0274796030105388
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603348,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603348
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6820512820512821,
"acc_stderr": 0.023610884308927865,
"acc_norm": 0.6820512820512821,
"acc_norm_stderr": 0.023610884308927865
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.02911661760608301,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.02911661760608301
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8330275229357799,
"acc_stderr": 0.01599015488507337,
"acc_norm": 0.8330275229357799,
"acc_norm_stderr": 0.01599015488507337
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8480392156862745,
"acc_stderr": 0.025195658428931796,
"acc_norm": 0.8480392156862745,
"acc_norm_stderr": 0.025195658428931796
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.02553010046023349,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.02553010046023349
},
"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": 0.7786259541984732,
"acc_stderr": 0.03641297081313729,
"acc_norm": 0.7786259541984732,
"acc_norm_stderr": 0.03641297081313729
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.03755265865037181,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.03755265865037181
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5267857142857143,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.5267857142857143,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.021901905115073325,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.021901905115073325
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8301404853128991,
"acc_stderr": 0.013428186370608308,
"acc_norm": 0.8301404853128991,
"acc_norm_stderr": 0.013428186370608308
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7196531791907514,
"acc_stderr": 0.024182427496577615,
"acc_norm": 0.7196531791907514,
"acc_norm_stderr": 0.024182427496577615
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.39888268156424583,
"acc_stderr": 0.01637696614261008,
"acc_norm": 0.39888268156424583,
"acc_norm_stderr": 0.01637696614261008
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7352941176470589,
"acc_stderr": 0.025261691219729484,
"acc_norm": 0.7352941176470589,
"acc_norm_stderr": 0.025261691219729484
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7009646302250804,
"acc_stderr": 0.02600330111788514,
"acc_norm": 0.7009646302250804,
"acc_norm_stderr": 0.02600330111788514
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.02438366553103545,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.02438366553103545
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4716312056737589,
"acc_stderr": 0.029779450957303062,
"acc_norm": 0.4716312056737589,
"acc_norm_stderr": 0.029779450957303062
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46740547588005216,
"acc_stderr": 0.012743072942653356,
"acc_norm": 0.46740547588005216,
"acc_norm_stderr": 0.012743072942653356
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6985294117647058,
"acc_stderr": 0.027875982114273168,
"acc_norm": 0.6985294117647058,
"acc_norm_stderr": 0.027875982114273168
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
"acc_stderr": 0.01895088677080631,
"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.01895088677080631
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.04350271442923243,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.04350271442923243
},
"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": 0.835820895522388,
"acc_stderr": 0.026193923544454115,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454115
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774708,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774708
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4467564259485924,
"mc1_stderr": 0.017403977522557144,
"mc2": 0.6218700196422391,
"mc2_stderr": 0.015309530764000967
},
"harness|winogrande|5": {
"acc": 0.8003157063930545,
"acc_stderr": 0.01123532838262585
},
"harness|gsm8k|5": {
"acc": 0.6368460955269143,
"acc_stderr": 0.013246614539839868
}
}
```
## 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. -->
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#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- 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. -->
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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CyberHarem/pina_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of pina/朝比奈フィーナ/菲娜 (Blue Archive)
This is the dataset of pina/朝比奈フィーナ/菲娜 (Blue Archive), containing 39 images and their tags.
The core tags of this character are `blonde_hair, breasts, long_hair, blue_eyes, large_breasts, ponytail, halo, braid, hair_between_eyes`, 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 | 39 | 63.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pina_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 39 | 56.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pina_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 101 | 112.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/pina_bluearchive/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/pina_bluearchive',
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 | 39 |  |  |  |  |  | cleavage, 1girl, looking_at_viewer, solo, bare_shoulders, smile, open_mouth, red_kimono, blush, collarbone, skirt, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | cleavage | 1girl | looking_at_viewer | solo | bare_shoulders | smile | open_mouth | red_kimono | blush | collarbone | skirt | white_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------|:--------|:--------------------|:-------|:-----------------|:--------|:-------------|:-------------|:--------|:-------------|:--------|:-------------------|
| 0 | 39 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
|
Syed-Hasan-8503/distilabel-intel-orca-dpo-pairs-rearranged | ---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 33022965
num_examples: 12859
download_size: 19066541
dataset_size: 33022965
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/astesia_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of astesia/アステシア/星极 (Arknights)
This is the dataset of astesia/アステシア/星极 (Arknights), containing 389 images and their tags.
The core tags of this character are `blue_hair, long_hair, blue_eyes, bright_pupils, breasts, hair_intakes, white_pupils`, 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 | 389 | 877.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/astesia_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 389 | 710.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/astesia_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1009 | 1.31 GiB | [Download](https://huggingface.co/datasets/CyberHarem/astesia_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/astesia_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_footwear, black_gloves, elbow_gloves, full_body, hat, high_heels, holding_sword, puffy_short_sleeves, solo, white_dress, white_headwear, blue_dress, closed_mouth, looking_at_viewer, official_alternate_costume, smile, standing, white_background, globe, simple_background, star_(sky) |
| 1 | 7 |  |  |  |  |  | 1girl, black_gloves, hat, simple_background, solo, white_background, white_headwear, closed_mouth, elbow_gloves, looking_at_viewer, medium_breasts, puffy_short_sleeves, smile, cleavage, official_alternate_costume, hand_up, upper_body, blue_dress, ribbon, white_dress |
| 2 | 11 |  |  |  |  |  | 1girl, long_sleeves, solo, cowboy_shot, holding_sword, looking_at_viewer, white_shirt, blue_skirt, closed_mouth, star_(symbol), white_background, diamond-shaped_pupils, neck_ribbon, puffy_sleeves, simple_background |
| 3 | 11 |  |  |  |  |  | 1girl, holding_sword, long_sleeves, solo, full_body, looking_at_viewer, white_shirt, simple_background, black_footwear, black_skirt, closed_mouth, high_heels, white_background, standing, globe, dress |
| 4 | 16 |  |  |  |  |  | 1girl, closed_mouth, looking_at_viewer, solo, white_shirt, simple_background, upper_body, white_background, smile, braid, collared_shirt, long_sleeves, neck_ribbon, puffy_sleeves, diamond_(shape) |
| 5 | 11 |  |  |  |  |  | 1girl, official_alternate_costume, solo, white_dress, white_gloves, bird, looking_at_viewer, holding_microphone, star_(symbol), bare_shoulders, very_long_hair, sleeveless_dress, closed_mouth, parted_lips, smile, white_background |
| 6 | 15 |  |  |  |  |  | 1girl, solo, white_dress, white_gloves, holding_sword, looking_at_viewer, official_alternate_costume, very_long_hair, hair_ornament, bare_shoulders, bird, parted_lips, sleeveless, hair_between_eyes, thighhighs |
| 7 | 7 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, large_breasts, navel, nipples, open_mouth, penis, sex, vaginal, completely_nude, spread_legs, cum_in_pussy, heart, mosaic_censoring, necklace, arms_up, collarbone, smile, tongue_out |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_footwear | black_gloves | elbow_gloves | full_body | hat | high_heels | holding_sword | puffy_short_sleeves | solo | white_dress | white_headwear | blue_dress | closed_mouth | looking_at_viewer | official_alternate_costume | smile | standing | white_background | globe | simple_background | star_(sky) | medium_breasts | cleavage | hand_up | upper_body | ribbon | long_sleeves | cowboy_shot | white_shirt | blue_skirt | star_(symbol) | diamond-shaped_pupils | neck_ribbon | puffy_sleeves | black_skirt | dress | braid | collared_shirt | diamond_(shape) | white_gloves | bird | holding_microphone | bare_shoulders | very_long_hair | sleeveless_dress | parted_lips | hair_ornament | sleeveless | hair_between_eyes | thighhighs | blush | hetero | solo_focus | 1boy | large_breasts | navel | nipples | open_mouth | penis | sex | vaginal | completely_nude | spread_legs | cum_in_pussy | heart | mosaic_censoring | necklace | arms_up | collarbone | tongue_out |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:---------------|:---------------|:------------|:------|:-------------|:----------------|:----------------------|:-------|:--------------|:-----------------|:-------------|:---------------|:--------------------|:-----------------------------|:--------|:-----------|:-------------------|:--------|:--------------------|:-------------|:-----------------|:-----------|:----------|:-------------|:---------|:---------------|:--------------|:--------------|:-------------|:----------------|:------------------------|:--------------|:----------------|:--------------|:--------|:--------|:-----------------|:------------------|:---------------|:-------|:---------------------|:-----------------|:-----------------|:-------------------|:--------------|:----------------|:-------------|:--------------------|:-------------|:--------|:---------|:-------------|:-------|:----------------|:--------|:----------|:-------------|:--------|:------|:----------|:------------------|:--------------|:---------------|:--------|:-------------------|:-----------|:----------|:-------------|:-------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | X | X | | X | | | X | X | X | X | X | X | X | X | X | | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | | | | | | | X | | X | | | | X | X | | | | X | | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | | | X | | X | X | | X | | | | X | X | | | X | X | X | X | | | | | | | X | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | X | | | | | | | | | X | | | | X | X | | X | | X | | X | | | | | X | | X | | X | | | | X | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | | | | | | | | X | X | | | X | X | X | X | | X | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 15 |  |  |  |  |  | X | | | | | | | X | | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_NousResearch__CodeLlama-7b-hf | ---
pretty_name: Evaluation run of NousResearch/CodeLlama-7b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NousResearch__CodeLlama-7b-hf\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T04:22:12.772861](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-7b-hf/blob/main/results_2023-09-17T04-22-12.772861.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\": 0.0005243288590604027,\n\
\ \"em_stderr\": 0.00023443780464835895,\n \"f1\": 0.05166212248322184,\n\
\ \"f1_stderr\": 0.0012470290169941962,\n \"acc\": 0.3516817229574676,\n\
\ \"acc_stderr\": 0.00983671270422883\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0005243288590604027,\n \"em_stderr\": 0.00023443780464835895,\n\
\ \"f1\": 0.05166212248322184,\n \"f1_stderr\": 0.0012470290169941962\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05458680818802123,\n \
\ \"acc_stderr\": 0.006257444037912531\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.648776637726914,\n \"acc_stderr\": 0.013415981370545131\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NousResearch/CodeLlama-7b-hf
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|arc:challenge|25_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T04_22_12.772861
path:
- '**/details_harness|drop|3_2023-09-17T04-22-12.772861.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T04-22-12.772861.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T04_22_12.772861
path:
- '**/details_harness|gsm8k|5_2023-09-17T04-22-12.772861.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T04-22-12.772861.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hellaswag|10_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:03:47.670325.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T00:03:47.670325.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T00:03:47.670325.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T04_22_12.772861
path:
- '**/details_harness|winogrande|5_2023-09-17T04-22-12.772861.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T04-22-12.772861.parquet'
- config_name: results
data_files:
- split: 2023_08_26T00_03_47.670325
path:
- results_2023-08-26T00:03:47.670325.parquet
- split: 2023_09_17T04_22_12.772861
path:
- results_2023-09-17T04-22-12.772861.parquet
- split: latest
path:
- results_2023-09-17T04-22-12.772861.parquet
---
# Dataset Card for Evaluation run of NousResearch/CodeLlama-7b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/CodeLlama-7b-hf
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NousResearch__CodeLlama-7b-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T04:22:12.772861](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__CodeLlama-7b-hf/blob/main/results_2023-09-17T04-22-12.772861.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": 0.0005243288590604027,
"em_stderr": 0.00023443780464835895,
"f1": 0.05166212248322184,
"f1_stderr": 0.0012470290169941962,
"acc": 0.3516817229574676,
"acc_stderr": 0.00983671270422883
},
"harness|drop|3": {
"em": 0.0005243288590604027,
"em_stderr": 0.00023443780464835895,
"f1": 0.05166212248322184,
"f1_stderr": 0.0012470290169941962
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.006257444037912531
},
"harness|winogrande|5": {
"acc": 0.648776637726914,
"acc_stderr": 0.013415981370545131
}
}
```
### 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] |
amishshah/imbalanced_0 | ---
dataset_info:
features:
- name: title
dtype: string
- name: label
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 45166669.74
num_examples: 27000
- name: test
num_bytes: 5018518.86
num_examples: 3000
download_size: 0
dataset_size: 50185188.6
---
# Dataset Card for "imbalanced_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TitleOS/rocketraccoon_personality_alpaca | ---
license: mpl-2.0
task_categories:
- text-generation
language:
- en
tags:
- alpaca
- synthetic data
size_categories:
- n<1K
---
An attempt to imbue a gruff, RocketRaccoon like personality from GoG in the Rocket 3B model. Alpaca formatted dataset generated by ehartford_dolphin-2.2.1-mistral-7b. |
yjching/tokenized_ts_tracks | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 114719
num_examples: 8
download_size: 46092
dataset_size: 114719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "yjching"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/la_pluma_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of la_pluma/ラ・プルマ/羽毛笔 (Arknights)
This is the dataset of la_pluma/ラ・プルマ/羽毛笔 (Arknights), containing 500 images and their tags.
The core tags of this character are `black_hair, short_hair, breasts, hair_ornament, ahoge, hairclip, medium_breasts, black_eyes, hair_intakes, feather_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 | 500 | 1009.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/la_pluma_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 810.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/la_pluma_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1319 | 1.53 GiB | [Download](https://huggingface.co/datasets/CyberHarem/la_pluma_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/la_pluma_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, bare_shoulders, sleeveless_shirt, solo, looking_at_viewer, full_body, fur-trimmed_jacket, open_clothes, thigh_strap, black_footwear, off_shoulder, sandals, black_gloves, black_shorts, simple_background, white_background, black_coat, black_jacket, holding_scythe, black_shirt, fingerless_gloves, fur-trimmed_coat, short_shorts, holding_weapon |
| 1 | 10 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, off_shoulder, simple_background, sleeveless_shirt, solo, upper_body, white_background, black_shirt, open_clothes, closed_mouth, black_jacket, fur-trimmed_jacket, blush |
| 2 | 8 |  |  |  |  |  | 1girl, bare_shoulders, goggles_around_neck, hair_bun, jacket_over_swimsuit, looking_at_viewer, off_shoulder, official_alternate_costume, open_jacket, solo, white_bikini, cleavage, upper_body, white_jacket, long_sleeves, whistle, white_background, mouth_hold, navel, simple_background |
| 3 | 14 |  |  |  |  |  | 1girl, beachball, goggles_around_neck, jacket_over_swimsuit, long_sleeves, looking_at_viewer, official_alternate_costume, open_jacket, solo, white_bikini, white_jacket, bare_shoulders, navel, off_shoulder, stomach, id_card, lanyard, water, whistle, holding, cleavage, mouth_hold, thigh_strap, frilled_bikini, innertube, sitting, swim_ring, cowboy_shot, feet_out_of_frame, hair_bun, soaking_feet |
| 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, day, goggles_around_neck, id_card, lanyard, looking_at_viewer, off_shoulder, official_alternate_costume, open_jacket, outdoors, sitting, solo, thigh_strap, water, white_bikini, white_jacket, blue_sky, cleavage, grey_eyes, jacket_over_swimsuit, long_sleeves, navel, ocean, single_side_bun, beachball, blush, frilled_bikini, stomach, thighs, barefoot, cloudy_sky, feet, toes, whistle |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | sleeveless_shirt | solo | looking_at_viewer | full_body | fur-trimmed_jacket | open_clothes | thigh_strap | black_footwear | off_shoulder | sandals | black_gloves | black_shorts | simple_background | white_background | black_coat | black_jacket | holding_scythe | black_shirt | fingerless_gloves | fur-trimmed_coat | short_shorts | holding_weapon | upper_body | closed_mouth | blush | goggles_around_neck | hair_bun | jacket_over_swimsuit | official_alternate_costume | open_jacket | white_bikini | cleavage | white_jacket | long_sleeves | whistle | mouth_hold | navel | beachball | stomach | id_card | lanyard | water | holding | frilled_bikini | innertube | sitting | swim_ring | cowboy_shot | feet_out_of_frame | soaking_feet | day | outdoors | blue_sky | grey_eyes | ocean | single_side_bun | thighs | barefoot | cloudy_sky | feet | toes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------------|:-------|:--------------------|:------------|:---------------------|:---------------|:--------------|:-----------------|:---------------|:----------|:---------------|:---------------|:--------------------|:-------------------|:-------------|:---------------|:-----------------|:--------------|:--------------------|:-------------------|:---------------|:-----------------|:-------------|:---------------|:--------|:----------------------|:-----------|:-----------------------|:-----------------------------|:--------------|:---------------|:-----------|:---------------|:---------------|:----------|:-------------|:--------|:------------|:----------|:----------|:----------|:--------|:----------|:-----------------|:------------|:----------|:------------|:--------------|:--------------------|:---------------|:------|:-----------|:-----------|:------------|:--------|:------------------|:---------|:-----------|:-------------|:-------|:-------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | X | X | X | X | | X | X | | | X | | | | X | X | | X | | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | | X | X | | | | | | X | | | | X | X | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | X | | X | X | | | | X | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | X | X | | | | X | | X | | | | | | | | | | | | | | | | X | X | | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | | X | | X | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
tet550/jawiki_sentences | ---
license: cc-by-3.0
dataset_info:
features:
- name: article_title
dtype: string
- name: topic_title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 7170293044
num_examples: 31888063
download_size: 3521442667
dataset_size: 7170293044
language:
- ja
---
# Jawiki Sentences Dataset
このデータセットは、日本語版Wikipediaの記事を元に作成されました。原文からできる限り不要なタグや表など文章にならないものを取り除いています。各エントリーには、その文が含まれる記事タイトル、セクションタイトルを含めています。
## データの構造
各エントリーは以下の3つのフィールドからなります:
- article_title: 記事のタイトルを表す文字列。
- topic_title: 記事のセクションタイトルを表す文字列。
- text: セクションのテキストを表す文字列。
## データの生成
このデータセットは、下記スクリプトで日本語Wikipediaダンプファイルから生成しています。
https://github.com/tet550/jawiki_sentences
## ライセンス
ウィキペディアのコンテンツは Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) および GNU Free Documentation License (GFDL) の下にライセンスされています。
このデータセットも同ライセンスに準じます。
|
anan-2024/twitter_dataset_1713026453 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 26687
num_examples: 60
download_size: 13363
dataset_size: 26687
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AdapterOcean/dollyaug-standardized_cluster_2 | ---
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: 15539998
num_examples: 1553
download_size: 4866247
dataset_size: 15539998
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dollyaug-standardized_cluster_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
0x22almostEvil/multilingual-wikihow-qa-16k | ---
license: cc-by-nc-3.0
task_categories:
- question-answering
language:
- en
- ru
- pt
- it
- es
- fr
- de
- nl
pretty_name: multilingual-wikihow-qa-16k
size_categories:
- 10K<n<100K
tags:
- wikihow
- QnA
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
dtype: string
- name: SOURCE
dtype: string
- name: METADATA
dtype: string
splits:
- name: train
num_bytes: 144407512
num_examples: 16822
download_size: 76391535
dataset_size: 144407512
---
# Dataset Card for multilingual WikiHow with ~16.8K entries. ~(2-2.2)K for each language.
### Warning [1]
The WikiHow team contacted me and made it clear that **they forbid the use of their data for machine learning purposes**. However, I am not calling for anything, and this dataset only shows the concept, and I strongly advise against violating their ToS.
However, consultation with lawyers made it clear that **dataset can be used for such purposes** if the project has **research purposes**.
### Warning [2]
Source code is kinda **very** bad, and I'm lazy to fix it.
### Dataset Summary
Contains Parquet of a list of instructions and WikiHow articles on different languages.
Each row consists of
* INSTRUCTION
* RESPONSE
* SOURCE (*.wikihow.com)
* METADATA (json with url and language).
### Licensing Information
Data is from WikiHow, license for content is located here:
https://www.wikihow.com/wikiHow:Creative-Commons
### Acknowledgements
This helped me a lot!
https://github.com/HelloChatterbox/PyWikiHow; https://pypi.org/project/pywikihow/ |
MatrixStudio/Codeforces-Python-Submissions-RL | ---
dataset_info:
features:
- name: contestId
dtype: int64
- name: index
dtype: string
- name: name
dtype: string
- name: type
dtype: string
- name: rating
dtype: int64
- name: tags
sequence: string
- name: title
dtype: string
- name: time-limit
dtype: string
- name: memory-limit
dtype: string
- name: problem-description
dtype: string
- name: input-specification
dtype: string
- name: output-specification
dtype: string
- name: demo-input
sequence: string
- name: demo-output
sequence: string
- name: note
dtype: string
- name: points
dtype: float64
- name: test_cases
list:
- name: input
dtype: string
- name: output
dtype: string
- name: creationTimeSeconds
dtype: int64
- name: relativeTimeSeconds
dtype: int64
- name: programmingLanguage
dtype: string
- name: verdict
dtype: string
- name: testset
dtype: string
- name: passedTestCount
dtype: int64
- name: timeConsumedMillis
dtype: int64
- name: memoryConsumedBytes
dtype: int64
- name: code
dtype: string
- name: prompt
dtype: string
- name: response
sequence: string
- name: score
dtype: float64
- name: reject
dtype: string
splits:
- name: train
num_bytes: 938669463.4435301
num_examples: 127921
- name: test
num_bytes: 113004434.19191754
num_examples: 15784
download_size: 197576147
dataset_size: 1051673897.6354476
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
brainer/dacon_low_res_bird_img | ---
dataset_info:
- config_name: default
features:
- name: img_path
dtype: string
- name: upscale_img_path
dtype: string
- name: label
dtype: string
- name: upscale_img
dtype: image
- name: img
dtype: image
splits:
- name: train
num_bytes: 1838069640.5
num_examples: 15834
download_size: 1838780556
dataset_size: 1838069640.5
- config_name: test
features:
- name: id
dtype: string
- name: img_path
dtype: string
- name: img
dtype: image
splits:
- name: train
num_bytes: 54917445.75
num_examples: 6786
download_size: 55311599
dataset_size: 54917445.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: test
data_files:
- split: train
path: test/train-*
---
|
biglam/on_the_books | ---
language:
- en
license: cc-by-3.0
size_categories:
- 1K<n<10K
task_categories:
- text-classification
pretty_name: On the Books Training Set
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: jim_crow
dtype:
class_label:
names:
'0': no_jim_crow
'1': jim_crow
- name: type
dtype: string
- name: chapter_num
dtype: int32
- name: section_num
dtype: int32
- name: chapter_text
dtype: string
- name: section_text
dtype: string
splits:
- name: train
num_bytes: 2119395
num_examples: 1785
download_size: 944579
dataset_size: 2119395
tags:
- lam
- legal
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
fanxiaonan/event_classification | ---
license: mit
---
# event classifcaiton dataset |
AI4M/michael-af | ---
license: apache-2.0
---
|
susnato/testing10_v1_features | ---
dataset_info:
features:
- name: query_emb_0
dtype: float64
- name: query_emb_1
dtype: float64
- name: query_emb_2
dtype: float64
- name: query_emb_3
dtype: float64
- name: query_emb_4
dtype: float64
- name: query_emb_5
dtype: float64
- name: query_emb_6
dtype: float64
- name: query_emb_7
dtype: float64
- name: query_emb_8
dtype: float64
- name: query_emb_9
dtype: float64
- name: query_emb_10
dtype: float64
- name: query_emb_11
dtype: float64
- name: query_emb_12
dtype: float64
- name: query_emb_13
dtype: float64
- name: query_emb_14
dtype: float64
- name: query_emb_15
dtype: float64
- name: query_emb_16
dtype: float64
- name: query_emb_17
dtype: float64
- name: query_emb_18
dtype: float64
- name: query_emb_19
dtype: float64
- name: query_emb_20
dtype: float64
- name: query_emb_21
dtype: float64
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dtype: float64
- name: query_emb_23
dtype: float64
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dtype: float64
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dtype: float64
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dtype: float64
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splits:
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num_bytes: 144829280
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download_size: 35842316
dataset_size: 144829280
configs:
- config_name: default
data_files:
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path: data/train-*
---
|
sayan1101/testing | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 51569
num_examples: 164
- name: test
num_bytes: 12203
num_examples: 41
download_size: 37324
dataset_size: 63772
---
# Dataset Card for "testing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Neko-Institute-of-Science__pygmalion-7b | ---
pretty_name: Evaluation run of Neko-Institute-of-Science/pygmalion-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Neko-Institute-of-Science/pygmalion-7b](https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b)\
\ 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_Neko-Institute-of-Science__pygmalion-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T20:45:41.888775](https://huggingface.co/datasets/open-llm-leaderboard/details_Neko-Institute-of-Science__pygmalion-7b/blob/main/results_2023-09-22T20-45-41.888775.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\": 0.0016778523489932886,\n\
\ \"em_stderr\": 0.00041913301788268467,\n \"f1\": 0.057855494966443086,\n\
\ \"f1_stderr\": 0.0013312169448543882,\n \"acc\": 0.3842127655245746,\n\
\ \"acc_stderr\": 0.009186954923281733\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268467,\n\
\ \"f1\": 0.057855494966443086,\n \"f1_stderr\": 0.0013312169448543882\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \
\ \"acc_stderr\": 0.0057849916626918655\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7221783741120757,\n \"acc_stderr\": 0.012588918183871601\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T20_45_41.888775
path:
- '**/details_harness|drop|3_2023-09-22T20-45-41.888775.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T20-45-41.888775.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T20_45_41.888775
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-45-41.888775.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T20-45-41.888775.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:16:07.141450.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:16:07.141450.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:16:07.141450.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T20_45_41.888775
path:
- '**/details_harness|winogrande|5_2023-09-22T20-45-41.888775.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T20-45-41.888775.parquet'
- config_name: results
data_files:
- split: 2023_07_19T16_16_07.141450
path:
- results_2023-07-19T16:16:07.141450.parquet
- split: 2023_09_22T20_45_41.888775
path:
- results_2023-09-22T20-45-41.888775.parquet
- split: latest
path:
- results_2023-09-22T20-45-41.888775.parquet
---
# Dataset Card for Evaluation run of Neko-Institute-of-Science/pygmalion-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b
- **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 [Neko-Institute-of-Science/pygmalion-7b](https://huggingface.co/Neko-Institute-of-Science/pygmalion-7b) 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_Neko-Institute-of-Science__pygmalion-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T20:45:41.888775](https://huggingface.co/datasets/open-llm-leaderboard/details_Neko-Institute-of-Science__pygmalion-7b/blob/main/results_2023-09-22T20-45-41.888775.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": 0.0016778523489932886,
"em_stderr": 0.00041913301788268467,
"f1": 0.057855494966443086,
"f1_stderr": 0.0013312169448543882,
"acc": 0.3842127655245746,
"acc_stderr": 0.009186954923281733
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268467,
"f1": 0.057855494966443086,
"f1_stderr": 0.0013312169448543882
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.0057849916626918655
},
"harness|winogrande|5": {
"acc": 0.7221783741120757,
"acc_stderr": 0.012588918183871601
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
CyberHarem/kita_ikuyo_bocchitherock | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Kita Ikuyo
This is the dataset of Kita Ikuyo, containing 296 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 | 296 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 650 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 296 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 296 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 296 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 296 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 296 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 650 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 650 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 650 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
cakiki/arxiv-metadata | ---
license: cc0-1.0
---
|
MARIO-Math-Reasoning/Gaokao2023-Math-En | ---
license: mit
---
# Data Summary
This is a compilation of math test questions and answers drawn from the 2023 Chinese National College Entrance Examination, the 2023 American Mathematics Competitions, and the 2023 American College Testing. For simplicity, we refer to it as `Gaokao2023`.
|
CognitiveScience/csdhdata | ---
license: mit
---
|
celsowm/medicamentos_patologia_ner | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 50246
num_examples: 273
download_size: 10597
dataset_size: 50246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-Tristan__zero_shot_classification_test-fb99e6e4-4634 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- Tristan/zero_shot_classification_test
eval_info:
task: zero_shot_classification
model: facebook/opt-125m
metrics: []
dataset_name: Tristan/zero_shot_classification_test
dataset_config: Tristan--zero_shot_classification_test
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-125m
* Dataset: Tristan/zero_shot_classification_test
* Config: Tristan--zero_shot_classification_test
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Tristan](https://huggingface.co/Tristan) for evaluating this model. |
pharaouk/Feedback-Collection | ---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: "new_feedback_collection.json"
---
## Dataset Description
- **Homepage:https://github.com/kaistAI/Prometheus**
- **Repository:https://github.com/kaistAI/Prometheus**
- **Paper:https://arxiv.org/abs/2310.08491**
- **Point of Contact:seungone@kaist.ac.kr**
# Dataset Card
### Dataset Summary
The Feedback Collection is a dataset designed to induce fine-grained evaluation capabilities into language models.\\
Recently, proprietary LLMs (e.g., GPT-4) have been used to evaluate long-form responses. In our experiments, we found that open-source LMs are not capable of evaluating long-form responses, showing low correlation with both human evaluators and GPT-4.\\
In our paper, we found that by (1) fine-tuning feedback generated by GPT-4 and (2) including the appropriate reference materials (reference answers & score rubrics), we can effectively induce fine-grained evaluation into open-source LMs.
The Feedback Collection provides 1K score rubrics, 20K instructions & reference answers, 100K responses & feedback (20K for each score in the range 1-5).\\
Experimental results show that Prometheus (a LM obtained by fine-tuning Llama-2-Chat on the Feedback Collection) can function as an evaluator in both an absolute scoring setting and a ranking scoring setting.
### Languages
English
## Dataset Structure
* instruction: The input that is given to the evaluator LM. It includes the instruction & response to evaluate, the reference answer, the score rubric.
* output: The output that the evaluator LM should generate. It includes the feedback and score decision divided by a phrase ```[RESULT]```.
* orig```_```instruction: The instruction to be evaluated. Note that this differs with the instruction that includes all the components.
* orig```_```response: The response to be evaluated.
* orig```_```reference```_```answer: A reference answer to the orig```_```instruction.
* orig```_```criteria: The score criteria used to evaluate the orig```_``` response.
* orig```_```score1```_```description: A description of when to give a score of 1 to the orig```_```response.
* orig```_```score2```_```description: A description of when to give a score of 2 to the orig```_```response.
* orig```_```score3```_```description: A description of when to give a score of 3 to the orig```_```response.
* orig```_```score4```_```description: A description of when to give a score of 4 to the orig```_```response.
* orig```_```score5```_```description: A description of when to give a score of 5 to the orig```_```response.
* orig```_```feedback: A feedback that critiques the orig```_```response.
* orig```_```score: An integer between 1 and 5 given to the orig```_```response.
In our paper, we trained the input using the following prompt format (already processed in the 'instruction'):
```
###Task Description:
An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\"
4. Please do not generate any other opening, closing, and explanations.
###The instruction to evaluate:
{orig_instruction}
###Response to evaluate:
{orig_response}
###Reference Answer (Score 5):
{orig_reference_answer}
###Score Rubrics:
[{orig_criteria}]
Score 1: {orig_score1_description}
Score 2: {orig_score2_description}
Score 3: {orig_score3_description}
Score 4: {orig_score4_description}
Score 5: {orig_score5_description}
###Feedback:
```
The following prompt format (already processed in the 'output') was used to train the evaluator LM:
```
{orig_feedback}
[RESULT] {orig_score}
```
Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
### Data Splits
| name | train |
|-------------------|------:|
|Feedback-Collection|99,952|
### Citation Information
If you find the following model helpful, please consider citing our paper!
```bibtex
@misc{kim2023prometheus,
title={Prometheus: Inducing Fine-grained Evaluation Capability in Language Models},
author={Seungone Kim and Jamin Shin and Yejin Cho and Joel Jang and Shayne Longpre and Hwaran Lee and Sangdoo Yun and Seongjin Shin and Sungdong Kim and James Thorne and Minjoon Seo},
year={2023},
eprint={2310.08491},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
dichmau/ja_vi_translation | ---
task_categories:
- translation
language:
- ja
- vi
---
Japanese-Vietnamese Translated Sentence Pairs. |
TopicNet/RuWiki-Good | ---
language:
- ru
multilinguality:
- monolingual
license: other
license_name: topicnet
license_link: >-
https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
task_categories:
- text-classification
task_ids:
- topic-classification
- multi-class-classification
- multi-label-classification
tags:
- topic-modeling
- topic-modelling
- text-clustering
- multimodal-data
- multimodal-learning
- modalities
- document-representation
---
# RuWiki-Good
Selected articles from Russian Wikipedia dump (date `20200301`). The dataset consists of good ("добротные", "хорошие") and featured ("избранные") articles (see https://ru.wikipedia.org/wiki/Википедия:Добротные_статьи).
Some measurable characteristics of the dataset:
* D — number of documents
* <modality name> W — modality dictionary size (number of unique tokens)
* <modality name> len D — average document length in modality tokens (number of tokens)
* <modality name> len D uniq — average document length in unique modality tokens (number of unique tokens)
| | D | @categories W | @categories len D | @categories len D uniq | @lemmatized W | @lemmatized len D | @lemmatized len D uniq | @ngramms W | @ngramms len D | @ngramms len D uniq |
|:------|------------:|-----------------------:|---------------------------:|--------------------------------:|-----------------------:|---------------------------:|--------------------------------:|--------------------:|------------------------:|-----------------------------:|
| value | 8603 | 44493 | 5.1718 | 5.17296 | 1.66434e+07 | 1934.6 | 721.389 | 3.11727e+06 | 362.347 | 287.695 |
Information about document lengths in modality tokens:
| | len_total@categories | len_total@lemmatized | len_total@ngramms | len_uniq@categories | len_uniq@lemmatized | len_uniq@ngramms |
|:-----|-----------------------:|-----------------------:|--------------------:|----------------------:|----------------------:|-------------------:|
| mean | 5.17296 | 1934.6 | 362.347 | 5.17296 | 721.389 | 287.695 |
| std | 4.0102 | 2065.78 | 384.164 | 4.0102 | 596.947 | 292.717 |
| min | 0 | 55 | 8 | 0 | 35 | 8 |
| 25% | 2 | 593 | 112.5 | 2 | 296 | 96 |
| 50% | 4 | 1223 | 229 | 4 | 540 | 189 |
| 75% | 7 | 2447 | 458 | 7 | 943 | 365 |
| max | 51 | 17472 | 3335 | 51 | 4423 | 2459 |
|
mucai/ViP-Bench | ---
license: apache-2.0
---
<p align="center">
<img src="https://raw.githubusercontent.com/mu-cai/ViP-LLaVA/main/images/vip-llava_arch.png" width="600"> <br>
</p>
# [ViP-Bench: Making Large Multimodal Models Understand Arbitrary Visual Prompts](https://vip-llava.github.io/)
ViP-Bench a region level multimodal model evaulation benchmark curated by University of Wisconsin-Madison. We provides two kinds of visual prompts: (1) bounding boxes, and (2) human drawn diverse visual prompts.
**Evaluation Code** See [https://github.com/mu-cai/ViP-LLaVA/blob/main/docs/Evaluation.md](https://github.com/mu-cai/ViP-LLaVA/blob/main/docs/Evaluation.md)
**LeaderBoard** See [https://paperswithcode.com/sota/visual-question-answering-on-vip-bench](https://paperswithcode.com/sota/visual-question-answering-on-vip-bench)
**Evaluation Server** Please refer to [https://huggingface.co/spaces/mucai/ViP-Bench_Evaluator](https://huggingface.co/spaces/mucai/ViP-Bench_Evaluator) to use our evaluation server.
## Source annotation
In `source_image`, we provide the source plain images along with the bounding box/mask annotations. Researchers can use such grounding information to match the special tokens such as `<obj>` in `"question"` entry of `vip-bench-meta-data.json`. For example, `<obj>` can be replaced by textual coordinates to evaluate the region-level multimodal models. |
Norod78/simpsons-blip-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 51605730.0
num_examples: 755
download_size: 50553165
dataset_size: 51605730.0
pretty_name: 'Simpsons BLIP captions'
size_categories:
- n<1K
tags: []
task_categories:
- text-to-image
license: cc-by-nc-sa-4.0
annotations_creators:
- machine-generated
language:
- en
language_creators:
- other
multilinguality:
- monolingual
---
# Dataset Card for "simpsons-blip-captions"
|
AustinMcMike/steve_jobs_conversational | ---
license: apache-2.0
---
|
Bingsu/st-parallel-sentences | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: en
dtype: string
- name: other
dtype: string
splits:
- name: train
num_bytes: 35774892810
num_examples: 257055413
download_size: 22222052417
dataset_size: 35774892810
---
# Dataset Card for "st-parallel-sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
C-MTEB/CLSClusteringP2P | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentences
sequence: string
- name: labels
sequence: string
splits:
- name: test
num_bytes: 56780231
num_examples: 10
download_size: 37254736
dataset_size: 56780231
---
# Dataset Card for "CLSClusteringP2P"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shunk031/wrime | ---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: wrime
tags:
- sentiment-analysis
- wrime
task_categories:
- text-classification
task_ids:
- sentiment-classification
datasets:
- ver1
- ver2
metrics:
- accuracy
---
# Dataset Card for WRIME
[](https://github.com/shunk031/huggingface-datasets_wrime/actions/workflows/ci.yaml)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- Homepage: https://github.com/ids-cv/wrime
- Repository: https://github.com/shunk031/huggingface-datasets_wrime
- Paper: https://aclanthology.org/2021.naacl-main.169/
### Dataset Summary
In this study, we introduce a new dataset, WRIME, for emotional intensity estimation. We collect both the subjective emotional intensity ofthe writers themselves and the objective one annotated by the readers, and explore the differences between them. In our data collection, we hired 50 participants via crowdsourcing service. They annotated their own past posts on a social networking service (SNS) with the subjective emotional intensity. We also hired 3 annotators, who annotated allposts with the objective emotional intensity. Consequently, our Japanese emotion analysis datasetconsists of 17,000 posts with both subjective andobjective emotional intensities for Plutchik’s eightemotions ([Plutchik, 1980](https://www.sciencedirect.com/science/article/pii/B9780125587013500077)), which are given in afour-point scale (no, weak, medium, and strong).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- Japanese
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/wrime", name="ver1")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 40000
# })
# validation: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 1200
# })
# test: Dataset({
# features: ['sentence', 'user_id', 'datetime', 'writer', 'reader1', 'reader2', 'reader3', 'avg_readers'],
# num_rows: 2000
# })
# })
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間｡チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/07/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0
}
}
```
#### Ver. 1
An example of looks as follows:
```json
{
"sentence": "ぼけっとしてたらこんな時間。チャリあるから食べにでたいのに…",
"user_id": "1",
"datetime": "2012/7/31 23:48",
"writer": {
"joy": 0,
"sadness": 1,
"anticipation": 2,
"surprise": 1,
"anger": 1,
"fear": 0,
"disgust": 0,
"trust": 1,
"sentiment": 0
},
"reader1": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -2
},
"reader2": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 1,
"disgust": 1,
"trust": 0,
"sentiment": -1
},
"reader3": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 1,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
},
"avg_readers": {
"joy": 0,
"sadness": 2,
"anticipation": 0,
"surprise": 0,
"anger": 0,
"fear": 0,
"disgust": 0,
"trust": 0,
"sentiment": -1
}
}
```
### Data Fields
#### Ver. 1
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
#### Ver. 2
- `sentence`: 投稿テキスト
- `user_id`: ユーザー ID
- `datetime`: 投稿日時
- `writer`: 主観 （書き手）
- `joy`: 主観の喜びの感情
- `sadness`: 主観の悲しみの感情
- `anticipation`: 主観の期待の感情
- `surprise`: 主観の驚きの感情
- `anger`: 主観の怒りの感情
- `fear`: 主観の恐れの感情
- `disgust`: 主観の嫌悪の感情
- `trust`: 主観の信頼の感情
- `sentiment`: 主観の感情極性
- `reader1`: 客観 A （読み手 A)
- `joy`: 客観 A の喜びの感情
- `sadness`: 客観 A の悲しみの感情
- `anticipation`: 客観 A の期待の感情
- `surprise`: 客観 A の驚きの感情
- `anger`: 客観 A の怒りの感情
- `fear`: 客観 A の恐れの感情
- `disgust`: 客観 A の嫌悪の感情
- `trust`: 客観 A の信頼の感情
- `sentiment`: 客観 A の感情極性
- `reader2`: 客観 B （読み手 B)
- `joy`: 客観 B の喜びの感情
- `sadness`: 客観 B の悲しみの感情
- `anticipation`: 客観 B の期待の感情
- `surprise`: 客観 B の驚きの感情
- `anger`: 客観 B の怒りの感情
- `fear`: 客観 B の恐れの感情
- `disgust`: 客観 B の嫌悪の感情
- `trust`: 客観 B の信頼の感情
- `sentiment`: 客観 B の感情極性
- `reader3`: 客観 C （読み手 C)
- `joy`: 客観 C の喜びの感情
- `sadness`: 客観 C の悲しみの感情
- `anticipation`: 客観 C の期待の感情
- `surprise`: 客観 C の驚きの感情
- `anger`: 客観 C の怒りの感情
- `fear`: 客観 C の恐れの感情
- `disgust`: 客観 C の嫌悪の感情
- `trust`: 客観 C の信頼の感情
- `sentiment`: 客観 C の感情極性
- `avg_readers`
- `joy`: 客観 A, B, C 平均の喜びの感情
- `sadness`: 客観 A, B, C 平均の悲しみの感情
- `anticipation`: 客観 A, B, C 平均の期待の感情
- `surprise`: 客観 A, B, C 平均の驚きの感情
- `anger`: 客観 A, B, C 平均の怒りの感情
- `fear`: 客観 A, B, C 平均の恐れの感情
- `disgust`: 客観 A, B, C 平均の嫌悪の感情
- `trust`: 客観 A, B, C 平均の信頼の感情
- `sentiment`: 客観 A, B, C 平均の感情極性
### Data Splits
| name | train | validation | test |
|------|-------:|-----------:|------:|
| ver1 | 40,000 | 1,200 | 2,000 |
| ver2 | 30,000 | 2,500 | 2,500 |
## 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
From [the README](https://github.com/ids-cv/wrime/blob/master/README.en.md#licence) of the GitHub:
- The dataset is available for research purposes only.
- Redistribution of the dataset is prohibited.
### Citation Information
```bibtex
@inproceedings{kajiwara-etal-2021-wrime,
title = "{WRIME}: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations",
author = "Kajiwara, Tomoyuki and
Chu, Chenhui and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.169",
doi = "10.18653/v1/2021.naacl-main.169",
pages = "2095--2104",
abstract = "We annotate 17,000 SNS posts with both the writer{'}s subjective emotional intensity and the reader{'}s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer{'}s subjective labels than the readers{'}. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.",
}
```
```bibtex
@inproceedings{suzuki-etal-2022-japanese,
title = "A {J}apanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain",
author = "Suzuki, Haruya and
Miyauchi, Yuto and
Akiyama, Kazuki and
Kajiwara, Tomoyuki and
Ninomiya, Takashi and
Takemura, Noriko and
Nakashima, Yuta and
Nagahara, Hajime",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.759",
pages = "7022--7028",
abstract = "We annotate 35,000 SNS posts with both the writer{'}s subjective sentiment polarity labels and the reader{'}s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (\textit{none}, \textit{weak}, \textit{medium}, and \textit{strong}) for each of the eight basic emotions by Plutchik (\textit{joy}, \textit{sadness}, \textit{anticipation}, \textit{surprise}, \textit{anger}, \textit{fear}, \textit{disgust}, and \textit{trust}) as well as sentiment polarity labels (\textit{strong positive}, \textit{positive}, \textit{neutral}, \textit{negative}, and \textit{strong negative}). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer{'}s and reader{'}s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.",
}
```
### Contributions
Thanks to [@moguranosenshi](https://github.com/moguranosenshi) for creating this dataset.
|
dominguesm/alpaca-data-pt-br | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 18324606
num_examples: 51759
download_size: 11508709
dataset_size: 18324606
license: cc-by-nc-4.0
language:
- pt
tags:
- instruction-finetuning
pretty_name: Alpaca-Cleaned-PTBR
task_categories:
- text-generation
---
**NOTE:** This is a machine translated version of the [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset.
# Dataset Card for Alpaca-Cleaned
- **Repository:** https://github.com/gururise/AlpacaDataCleaned
## Dataset Description
This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
```
"instruction":"Summarize the given article in 200 Words.",
"input": "https://www.bbc.com/news/world-51461830",
"output": "The recent protests in Belarus are part of a growing wave of discontent against the rule of Alexander Lukashenko, who has led the country since 1994. Demonstrations against his government have been ongoing since August, and come in the wake of an election widely seen as rigged. As police and security forces have cracked down on protesters, the movement has only grown, with hundreds of thousands of people taking to the streets to call for new leadership. Human rights activists have accused the government of using extreme violence in order prosecute protesters, leading to an international outcry and sanctions from the European Union. As the situation in Belarus continues to unfold, the international community is monitoring the situation closely - hoping for a peaceful resolution, while also standing firm against the oppressive tactics employed by the Belarusian state."
"instruction": "Classify the following shape according to simple geometry.",
"input": "<Picture Attached>",
"output": "Triangle"
"instruction": "Given a recent news article, write a headline for it.",
"input": "https://www.news.example.com/article-1",
"output": "\"NASA Launches a Mars 2020 Mission to Search for Signs of Ancient Life\""
```
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
```
"instruction": "Should I invest in stocks?",
"input": "",
"output": "It depends on your individual situation. Investing in stocks carries a certain amount of risk but can also offer potential rewards. Make sure to research the different types of stocks, understand the risks and rewards, and develop a plan that works for you.\n##\n18. Instruction: Identify the noun in the following sentence:\n18. Input:\nThe large apple was delicious.\n18. Output:\nApple"
```
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
```
"instruction": "Calculate the area of the following shape in square centimeters.",
"input": "rectangle of size 4 cm x 5 cm",
"output": ""
```
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
```
"instruction": "Create a graphic or logo that visually represents the word \"courage\".",
"input": "",
"output": "<No Output>"
```
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
```
"input":"<no input>"
"input":"No input"
"input":"noinput"
"input":"<noinput>"
```
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
```
"instruction": "Calculate the median of the following data set.",
"input": "1, 2, 4, 5, 8, 9",
"output": "5"
"instruction": "Convert 25m to km.",
"input": "",
"output": "25km"
```
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
```
"instruction": "Freeze the following sample of yogurt for 10 minutes.",
"input": "Yogurt sample",
"output": "<noinput>"
"instruction": "Increase the font size to 12 points.",
"input": "",
"output": "The font size has been increased to 12 points."
```
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The data in Alpaca are in English (BCP-47 en).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| alpaca | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed] |
pythainlp/thainer-corpus-v2.2 | ---
language:
- th
license: cc-by-3.0
task_categories:
- token-classification
dataset_info:
features:
- name: words
sequence: string
- name: ner
sequence:
class_label:
names:
'0': B-PERSON
'1': I-PERSON
'2': O
'3': B-ORGANIZATION
'4': B-LOCATION
'5': I-ORGANIZATION
'6': I-LOCATION
'7': B-DATE
'8': I-DATE
'9': B-TIME
'10': I-TIME
'11': B-MONEY
'12': I-MONEY
'13': B-FACILITY
'14': I-FACILITY
'15': B-URL
'16': I-URL
'17': B-PERCENT
'18': I-PERCENT
'19': B-LEN
'20': I-LEN
'21': B-AGO
'22': I-AGO
'23': B-LAW
'24': I-LAW
'25': B-PHONE
'26': I-PHONE
'27': B-EMAIL
'28': I-EMAIL
'29': B-ZIP
'30': B-TEMPERATURE
'31': I-TEMPERATURE
splits:
- name: train
num_bytes: 3739947
num_examples: 4379
- name: validation
num_bytes: 1215876
num_examples: 1475
- name: test
num_bytes: 1243881
num_examples: 1472
download_size: 999069
dataset_size: 6199704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Thai NER v2.2
Thai Named Entity Recognition Corpus
**You can download .conll to train named entity model in [https://zenodo.org/records/10795907](https://zenodo.org/records/10795907).**
**Size**
- Train: 3,938 docs
- Validation: 1,313 docs
- Test: 1,313 Docs
Some data come from crowdsourcing between Dec 2018 - Nov 2019. [https://github.com/wannaphong/thai-ner](https://github.com/wannaphong/thai-ner)
**Domain**
- News (It, politics, economy, social)
- PR (KKU news)
- general
**Source**
- I use sone data from Nutcha’s theses (http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) and improve data by rechecking and adding more tagging.
- Twitter
- Blognone.com - It news
- thaigov.go.th
- kku.ac.th
And more (the lists are lost.)
**Tag**
- DATE - date
- TIME - time
- EMAIL - email
- LEN - length
- LOCATION - Location
- ORGANIZATION - Company / Organization
- PERSON - Person name
- PHONE - phone number
- TEMPERATURE - temperature
- URL - URL
- ZIP - Zip code
- MONEY - the amount
- LAW - legislation
- PERCENT - PERCENT
## Cite
> Wannaphong Phatthiyaphaibun. (2024). Thai NER 2.2 (2.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10795907
or BibTeX
```
@dataset{wannaphong_phatthiyaphaibun_2024_10795907,
author = {Wannaphong Phatthiyaphaibun},
title = {Thai NER 2.2},
month = mar,
year = 2024,
publisher = {Zenodo},
version = {2.2},
doi = {10.5281/zenodo.10795907},
url = {https://doi.org/10.5281/zenodo.10795907}
}
``` |
Tristan/wikipedia-august-october-line-diff-1000-char-threshold-1000-sample | ---
dataset_info:
features:
- name: url
dtype: string
- name: text
dtype: string
- name: crawl_timestamp
dtype: int64
- name: reward
dtype: int64
splits:
- name: train
num_bytes: 1431232
num_examples: 1000
download_size: 567286
dataset_size: 1431232
---
# Dataset Card for "wikipedia-august-october-line-diff-1000-char-threshold-1000-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zhangshuoming/switch_subset_exebench | ---
dataset_info:
features:
- name: train_real_simple_io
struct:
- name: asm
struct:
- name: code
sequence: string
- name: target
sequence: string
- name: fname
dtype: string
- name: func_def
dtype: string
- name: func_head
dtype: string
- name: func_head_types
dtype: string
- name: path
dtype: string
- name: real_deps
dtype: string
- name: real_exe_wrapper
dtype: string
- name: real_io_pairs
struct:
- name: dummy_funcs
sequence: 'null'
- name: dummy_funcs_seed
sequence: 'null'
- name: input
list:
- name: value
sequence: string
- name: var
sequence: string
- name: output
list:
- name: value
sequence: string
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sequence: string
- name: real_iospec
dtype: string
- name: ref
dtype: string
- name: signature
sequence: string
- name: synth_deps
dtype: string
- name: synth_exe_wrapper
dtype: string
- name: synth_io_pairs
struct:
- name: dummy_funcs
sequence: string
- name: dummy_funcs_seed
sequence: int64
- name: input
list:
- name: value
sequence: string
- name: var
sequence: string
- name: output
list:
- name: value
sequence: string
- name: var
sequence: string
- name: synth_iospec
dtype: string
splits:
- name: train
num_bytes: 17599214
num_examples: 1107
download_size: 3846323
dataset_size: 17599214
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ajankelo/pklot_50 | ---
language: en
license: cc-by-4.0
tags:
- PKLot
- object detection
---
# PKLot 50
This dataset comprises 50 fully annotated images. The original images are were introduced in [*PKLot – A robust dataset for parking lot classification*](https://www.inf.ufpr.br/lesoliveira/download/ESWA2015.pdf).
## Labeling Method
Labeling was manually completed using CVAT with the assistance of Voxel51 for inspection.
## Original dataset citation info
Almeida, P., Oliveira, L. S., Silva Jr, E., Britto Jr, A., Koerich, A., PKLot – A robust dataset for parking lot classification, Expert Systems with Applications, 42(11):4937-4949, 2015.
|
open-llm-leaderboard/details_Test157t__Kunocchini-1.2-7b-longtext | ---
pretty_name: Evaluation run of Test157t/Kunocchini-1.2-7b-longtext
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Test157t/Kunocchini-1.2-7b-longtext](https://huggingface.co/Test157t/Kunocchini-1.2-7b-longtext)\
\ 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_Test157t__Kunocchini-1.2-7b-longtext\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-29T19:52:16.065136](https://huggingface.co/datasets/open-llm-leaderboard/details_Test157t__Kunocchini-1.2-7b-longtext/blob/main/results_2024-02-29T19-52-16.065136.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\": 0.6267864351550836,\n\
\ \"acc_stderr\": 0.0325192896182257,\n \"acc_norm\": 0.6332530550898832,\n\
\ \"acc_norm_stderr\": 0.0331825601519831,\n \"mc1\": 0.2778457772337821,\n\
\ \"mc1_stderr\": 0.015680929364024647,\n \"mc2\": 0.41719922898961076,\n\
\ \"mc2_stderr\": 0.014076694537315668\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5622866894197952,\n \"acc_stderr\": 0.014497573881108283,\n\
\ \"acc_norm\": 0.5989761092150171,\n \"acc_norm_stderr\": 0.014322255790719867\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6313483369846644,\n\
\ \"acc_stderr\": 0.004814532642574651,\n \"acc_norm\": 0.8251344353714399,\n\
\ \"acc_norm_stderr\": 0.0037907576465758975\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04244633238353227,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04244633238353227\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\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\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105652,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105652\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816507,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816507\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5702127659574469,\n \"acc_stderr\": 0.03236214467715564,\n\
\ \"acc_norm\": 0.5702127659574469,\n \"acc_norm_stderr\": 0.03236214467715564\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404904,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404904\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768177,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768177\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031096,\n \"\
acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031096\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.47783251231527096,\n \"acc_stderr\": 0.03514528562175008,\n \"\
acc_norm\": 0.47783251231527096,\n \"acc_norm_stderr\": 0.03514528562175008\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971118,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971118\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37407407407407406,\n \"acc_stderr\": 0.029502861128955293,\n \
\ \"acc_norm\": 0.37407407407407406,\n \"acc_norm_stderr\": 0.029502861128955293\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.03149930577784906,\n \
\ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.03149930577784906\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\"\
: 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154343,\n \"\
acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154343\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\"\
: 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7843137254901961,\n\
\ \"acc_stderr\": 0.028867431449849316,\n \"acc_norm\": 0.7843137254901961,\n\
\ \"acc_norm_stderr\": 0.028867431449849316\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676173,\n\
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676173\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\
\ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\
\ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302871,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302871\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\
\ \"acc_stderr\": 0.04373313040914761,\n \"acc_norm\": 0.7129629629629629,\n\
\ \"acc_norm_stderr\": 0.04373313040914761\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.013964393769899126,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.013964393769899126\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917205,\n\
\ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917205\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3094972067039106,\n\
\ \"acc_stderr\": 0.015461169002371537,\n \"acc_norm\": 0.3094972067039106,\n\
\ \"acc_norm_stderr\": 0.015461169002371537\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6752411575562701,\n\
\ \"acc_stderr\": 0.026596782287697043,\n \"acc_norm\": 0.6752411575562701,\n\
\ \"acc_norm_stderr\": 0.026596782287697043\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7098765432098766,\n \"acc_stderr\": 0.025251173936495026,\n\
\ \"acc_norm\": 0.7098765432098766,\n \"acc_norm_stderr\": 0.025251173936495026\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4452411994784876,\n\
\ \"acc_stderr\": 0.012693421303973294,\n \"acc_norm\": 0.4452411994784876,\n\
\ \"acc_norm_stderr\": 0.012693421303973294\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6617647058823529,\n \"acc_stderr\": 0.028739328513983572,\n\
\ \"acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.028739328513983572\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6388888888888888,\n \"acc_stderr\": 0.01943177567703731,\n \
\ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.01943177567703731\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\
\ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n\
\ \"acc_stderr\": 0.025538433368578323,\n \"acc_norm\": 0.845771144278607,\n\
\ \"acc_norm_stderr\": 0.025538433368578323\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\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\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2778457772337821,\n\
\ \"mc1_stderr\": 0.015680929364024647,\n \"mc2\": 0.41719922898961076,\n\
\ \"mc2_stderr\": 0.014076694537315668\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698334\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3290371493555724,\n \
\ \"acc_stderr\": 0.012942375603679364\n }\n}\n```"
repo_url: https://huggingface.co/Test157t/Kunocchini-1.2-7b-longtext
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_29T19_52_16.065136
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-52-16.065136.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-29T19-52-16.065136.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- '**/details_harness|winogrande|5_2024-02-29T19-52-16.065136.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-29T19-52-16.065136.parquet'
- config_name: results
data_files:
- split: 2024_02_29T19_52_16.065136
path:
- results_2024-02-29T19-52-16.065136.parquet
- split: latest
path:
- results_2024-02-29T19-52-16.065136.parquet
---
# Dataset Card for Evaluation run of Test157t/Kunocchini-1.2-7b-longtext
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Test157t/Kunocchini-1.2-7b-longtext](https://huggingface.co/Test157t/Kunocchini-1.2-7b-longtext) 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_Test157t__Kunocchini-1.2-7b-longtext",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-29T19:52:16.065136](https://huggingface.co/datasets/open-llm-leaderboard/details_Test157t__Kunocchini-1.2-7b-longtext/blob/main/results_2024-02-29T19-52-16.065136.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": 0.6267864351550836,
"acc_stderr": 0.0325192896182257,
"acc_norm": 0.6332530550898832,
"acc_norm_stderr": 0.0331825601519831,
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024647,
"mc2": 0.41719922898961076,
"mc2_stderr": 0.014076694537315668
},
"harness|arc:challenge|25": {
"acc": 0.5622866894197952,
"acc_stderr": 0.014497573881108283,
"acc_norm": 0.5989761092150171,
"acc_norm_stderr": 0.014322255790719867
},
"harness|hellaswag|10": {
"acc": 0.6313483369846644,
"acc_stderr": 0.004814532642574651,
"acc_norm": 0.8251344353714399,
"acc_norm_stderr": 0.0037907576465758975
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.046482319871173156,
"acc_norm": 0.31,
"acc_norm_stderr": 0.046482319871173156
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04244633238353227,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04244633238353227
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"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": 0.7222222222222222,
"acc_stderr": 0.037455547914624555,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.037455547914624555
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105652,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105652
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5702127659574469,
"acc_stderr": 0.03236214467715564,
"acc_norm": 0.5702127659574469,
"acc_norm_stderr": 0.03236214467715564
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5087719298245614,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.5087719298245614,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.025279850397404904,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.025279850397404904
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.04403438954768177,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768177
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7580645161290323,
"acc_stderr": 0.024362599693031096,
"acc_norm": 0.7580645161290323,
"acc_norm_stderr": 0.024362599693031096
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.47783251231527096,
"acc_stderr": 0.03514528562175008,
"acc_norm": 0.47783251231527096,
"acc_norm_stderr": 0.03514528562175008
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586808,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586808
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
"acc_stderr": 0.024233532297758733,
"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.024233532297758733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.023854795680971118,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.023854795680971118
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37407407407407406,
"acc_stderr": 0.029502861128955293,
"acc_norm": 0.37407407407407406,
"acc_norm_stderr": 0.029502861128955293
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6218487394957983,
"acc_stderr": 0.03149930577784906,
"acc_norm": 0.6218487394957983,
"acc_norm_stderr": 0.03149930577784906
},
"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": 0.8036697247706422,
"acc_stderr": 0.017030719339154343,
"acc_norm": 0.8036697247706422,
"acc_norm_stderr": 0.017030719339154343
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5601851851851852,
"acc_stderr": 0.0338517797604481,
"acc_norm": 0.5601851851851852,
"acc_norm_stderr": 0.0338517797604481
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7843137254901961,
"acc_stderr": 0.028867431449849316,
"acc_norm": 0.7843137254901961,
"acc_norm_stderr": 0.028867431449849316
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.026750826994676173,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.026750826994676173
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6636771300448431,
"acc_stderr": 0.031708824268455,
"acc_norm": 0.6636771300448431,
"acc_norm_stderr": 0.031708824268455
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.03984979653302871,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.03984979653302871
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.04373313040914761,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.04373313040914761
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.03226219377286775,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.03226219377286775
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179333,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8122605363984674,
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"acc_norm": 0.8122605363984674,
"acc_norm_stderr": 0.013964393769899126
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6965317919075145,
"acc_stderr": 0.024752411960917205,
"acc_norm": 0.6965317919075145,
"acc_norm_stderr": 0.024752411960917205
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3094972067039106,
"acc_stderr": 0.015461169002371537,
"acc_norm": 0.3094972067039106,
"acc_norm_stderr": 0.015461169002371537
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.025160998214292456,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.025160998214292456
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6752411575562701,
"acc_stderr": 0.026596782287697043,
"acc_norm": 0.6752411575562701,
"acc_norm_stderr": 0.026596782287697043
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7098765432098766,
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"acc_norm": 0.7098765432098766,
"acc_norm_stderr": 0.025251173936495026
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4452411994784876,
"acc_stderr": 0.012693421303973294,
"acc_norm": 0.4452411994784876,
"acc_norm_stderr": 0.012693421303973294
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6617647058823529,
"acc_stderr": 0.028739328513983572,
"acc_norm": 0.6617647058823529,
"acc_norm_stderr": 0.028739328513983572
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.01943177567703731,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.01943177567703731
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.04494290866252091,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.04494290866252091
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7020408163265306,
"acc_stderr": 0.029279567411065677,
"acc_norm": 0.7020408163265306,
"acc_norm_stderr": 0.029279567411065677
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.845771144278607,
"acc_stderr": 0.025538433368578323,
"acc_norm": 0.845771144278607,
"acc_norm_stderr": 0.025538433368578323
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352203,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352203
},
"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": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2778457772337821,
"mc1_stderr": 0.015680929364024647,
"mc2": 0.41719922898961076,
"mc2_stderr": 0.014076694537315668
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.011764149054698334
},
"harness|gsm8k|5": {
"acc": 0.3290371493555724,
"acc_stderr": 0.012942375603679364
}
}
```
## 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] |
yuyuc/chem-uspto | ---
license: openrail
---
|
dhivyamadhavan/task2_dataset | ---
dataset_info:
features:
- name: messages
dtype: string
splits:
- name: train_ift
num_bytes: 16141
num_examples: 45
download_size: 9714
dataset_size: 16141
configs:
- config_name: default
data_files:
- split: train_ift
path: data/train_ift-*
---
|
kkyo/Nagisin | ---
license: openrail
---
|
danigr7/prueba | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': crazing
'1': inclusion
'2': patches
'3': pitted_surface
'4': rolled-in_scale
'5': scratches
splits:
- name: train
num_bytes: 22821674.88
num_examples: 1440
- name: validation
num_bytes: 5395918
num_examples: 360
download_size: 26736558
dataset_size: 28217592.88
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
task_categories:
- image-classification
language:
- en
--- |
goodfellowliu/Urban100 | ---
license: apache-2.0
---
|
hotchpotch/jaqket_cc | ---
license: cc-by-sa-4.0
dataset_info:
features:
- name: qid
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: competition
dtype: string
- name: timestamp
dtype: string
- name: section
dtype: string
- name: original_question
dtype: string
- name: original_answer
dtype: string
- name: original_additional_info
dtype: string
- name: number
dtype: string
splits:
- name: aio_01_test
num_bytes: 900806
num_examples: 2000
- name: aio_01_dev
num_bytes: 868722
num_examples: 1992
- name: aio_01_unused
num_bytes: 267601
num_examples: 608
download_size: 1090027
dataset_size: 2037129
configs:
- config_name: default
data_files:
- split: aio_01_test
path: v2/aio_01_test-*
- split: aio_01_dev
path: v2/aio_01_dev-*
- split: aio_01_unused
path: v2/aio_01_unused-*
task_categories:
- question-answering
language:
- ja
---
# JAQKET から CC-BY-SA のデータのみを含めたデータセット
[AI王 公式配布データセット(JAQKET) ](https://sites.google.com/view/project-aio/dataset?authuser=0)で配布されているクイズデータのうち、ライセンスが [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja)のデータのみを含めたデータセットです。
## 謝辞
AI王の開催・クイズ作成等々をされた関係者の方々に、有益なデータセットの公開ありがとうございます。
## 利用方法
```
from datasets import load_dataset
ds = load_dataset("hotchpotch/jaqket_cc")
ds["aio_01_dev"][0]
#=> {'qid': 'QA20CAPR-0002',
'question': '明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記入した紙を置き、参加者全員の人差し指をコインに置いて行うのは何でしょう?',
'answers': ['コックリさん'],
'competition': '第1回AI王',
'timestamp': '2019/12/25',
'section': '開発データ問題 (dev1)',
'original_question': '明治時代に西洋から伝わった「テーブル・ターニング」に起源を持つ占いの一種で、50音表などを記入した紙を置き、参加者全員の人差し指をコインに置いて行うのは何でしょう？',
'original_answer': 'コックリさん',
'original_additional_info': '',
'number': '2'}
```
|
mask-distilled-one-sec-cv12/chunk_139 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1130164308
num_examples: 221949
download_size: 1151665269
dataset_size: 1130164308
---
# Dataset Card for "chunk_139"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
scfengv/TVL_Sentiment_Analysis | ---
task_categories:
- text-classification
language:
- zh
--- |
ineoApp/factures-99 | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': numero facture
'2': Telephone
'3': Email
'4': Site web
'5': RC
'6': CNSS
'7': TP
'8': Mode de paiement
'9': fournisseur
'10': date facture
'11': date limite
'12': montant ht
'13': montant ttc
'14': tva
'15': prix tva
'16': addresse
'17': reference
'18': Devise
'19': ICE fournisseur
'20': IF fournisseur
'21': Condition de paiement
'22': informations
'23': art1 designation
'24': art1 quantite
'25': art1 prix unit
'26': art1 tva
'27': art1 montant ht
'28': art1 Article
'29': art1 taux de remise
'30': art2 designation
'31': art2 quantite
'32': art2 prix unit
'33': art2 tva
'34': art2 montant ht
'35': art2 Article
'36': art2 taux de remise
'37': art3 designation
'38': art3 quantite
'39': art3 prix unit
'40': art3 tva
'41': art3 montant ht
'42': art3 Article
'43': art3 taux de remise
'44': art4 designation
'45': art4 quantite
'46': art4 prix unit
'47': art4 tva
'48': art4 montant ht
'49': art4 Article
'50': art4 taux de remise
'51': art5 designation
'52': art5 quantite
'53': art5 prix unit
'54': art5 tva
'55': art5 montant ht
'56': art5 Article
'57': art5 taux de remise
'58': art6 designation
'59': art6 quantite
'60': art6 prix unit
'61': art6 tva
'62': art6 montant ht
'63': art6 Article
'64': art6 taux de remise
'65': art7 designation
'66': art7 quantite
'67': art7 prix unit
'68': art7 tva
'69': art7 montant ht
'70': art7 Article
'71': art7 taux de remise
'72': art8 designation
'73': art8 quantite
'74': art8 prix unit
'75': art8 tva
'76': art8 montant ht
'77': art8 Article
'78': art8 taux de remise
'79': art9 designation
'80': art9 quantite
'81': art9 prix unit
'82': art9 tva
'83': art9 montant ht
'84': art9 Article
'85': art9 taux de remise
'86': art10 designation
'87': art10 quantite
'88': art10 prix unit
'89': art10 tva
'90': art10 montant ht
'91': art10 Article
'92': art10 taux de remise
'93': art11 designation
'94': art11 quantite
'95': art11 prix unit
'96': art11 tva
'97': art11 montant ht
'98': art11 Article
'99': art11 taux de remise
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 83534936.8
num_examples: 60
- name: test
num_bytes: 20883734.2
num_examples: 15
download_size: 96896920
dataset_size: 104418671.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_rte_bare_past_tense | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 695663
num_examples: 1862
- name: train
num_bytes: 604595
num_examples: 1545
download_size: 844197
dataset_size: 1300258
---
# Dataset Card for "MULTI_VALUE_rte_bare_past_tense"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/chemistry_dataset_standardized_cluster_2_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: 4782401
num_examples: 6678
download_size: 1923152
dataset_size: 4782401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chemistry_dataset_standardized_cluster_2_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hate_speech18 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
paperswithcode_id: hate-speech
pretty_name: Hate Speech
dataset_info:
features:
- name: text
dtype: string
- name: user_id
dtype: int64
- name: subforum_id
dtype: int64
- name: num_contexts
dtype: int64
- name: label
dtype:
class_label:
names:
'0': noHate
'1': hate
'2': idk/skip
'3': relation
splits:
- name: train
num_bytes: 1375340
num_examples: 10944
download_size: 3664530
dataset_size: 1375340
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/Vicomtech/hate-speech-dataset
- **Repository:** https://github.com/Vicomtech/hate-speech-dataset
- **Paper:** https://www.aclweb.org/anthology/W18-51.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
These files contain text extracted from Stormfront, a white supremacist forum. A random set of forums posts have been sampled from
several subforums and split into sentences. Those sentences have been manually labelled as containing hate speech or not, according
to certain annotation guidelines.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- text: the provided sentence
- user_id: information to make it possible to re-build the conversations these sentences belong to
- subforum_id: information to make it possible to re-build the conversations these sentences belong to
- num_contexts: number of previous posts the annotator had to read before making a decision over the category of the sentence
- label: hate, noHate, relation (sentence in the post doesn't contain hate speech on their own, but combination of serveral sentences does)
or idk/skip (sentences that are not written in English or that don't contain information as to be classified into hate or noHate)
### 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
```
@inproceedings{gibert2018hate,
title = "{Hate Speech Dataset from a White Supremacy Forum}",
author = "de Gibert, Ona and
Perez, Naiara and
Garc{\'\i}a-Pablos, Aitor and
Cuadros, Montse",
booktitle = "Proceedings of the 2nd Workshop on Abusive Language Online ({ALW}2)",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W18-5102",
doi = "10.18653/v1/W18-5102",
pages = "11--20",
}
```
### Contributions
Thanks to [@czabo](https://github.com/czabo) for adding this dataset. |
mmuttharasan/llmjptk4 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 109096.0
num_examples: 26
- name: test
num_bytes: 109096.0
num_examples: 26
download_size: 48848
dataset_size: 218192.0
---
# Dataset Card for "llmjptk4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_lodrick-the-lafted__Hermes-Instruct-7B-217K | ---
pretty_name: Evaluation run of lodrick-the-lafted/Hermes-Instruct-7B-217K
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lodrick-the-lafted/Hermes-Instruct-7B-217K](https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-217K)\
\ 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_lodrick-the-lafted__Hermes-Instruct-7B-217K\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-21T03:50:00.047589](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Hermes-Instruct-7B-217K/blob/main/results_2024-02-21T03-50-00.047589.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\": 0.6119336789446922,\n\
\ \"acc_stderr\": 0.03277328607172842,\n \"acc_norm\": 0.6158709652995045,\n\
\ \"acc_norm_stderr\": 0.03343288617558588,\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.017412941986115312,\n \"mc2\": 0.6181304894795385,\n\
\ \"mc2_stderr\": 0.01547067307242061\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5699658703071673,\n \"acc_stderr\": 0.014467631559137986,\n\
\ \"acc_norm\": 0.6100682593856656,\n \"acc_norm_stderr\": 0.014252959848892898\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6364270065723959,\n\
\ \"acc_stderr\": 0.004800446397653348,\n \"acc_norm\": 0.8264289982075284,\n\
\ \"acc_norm_stderr\": 0.0037796612246514746\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.04284958639753401\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n\
\ \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.02881561571343211,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.02881561571343211\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.039420826399272135,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.039420826399272135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\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\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5234042553191489,\n \"acc_stderr\": 0.032650194750335815,\n\
\ \"acc_norm\": 0.5234042553191489,\n \"acc_norm_stderr\": 0.032650194750335815\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.41228070175438597,\n\
\ \"acc_stderr\": 0.046306532033665956,\n \"acc_norm\": 0.41228070175438597,\n\
\ \"acc_norm_stderr\": 0.046306532033665956\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3862433862433862,\n \"acc_stderr\": 0.025075981767601684,\n \"\
acc_norm\": 0.3862433862433862,\n \"acc_norm_stderr\": 0.025075981767601684\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6967741935483871,\n\
\ \"acc_stderr\": 0.026148685930671753,\n \"acc_norm\": 0.6967741935483871,\n\
\ \"acc_norm_stderr\": 0.026148685930671753\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\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624335,\n\
\ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624335\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8549222797927462,\n \"acc_stderr\": 0.02541634309630643,\n\
\ \"acc_norm\": 0.8549222797927462,\n \"acc_norm_stderr\": 0.02541634309630643\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5641025641025641,\n \"acc_stderr\": 0.02514180151117749,\n \
\ \"acc_norm\": 0.5641025641025641,\n \"acc_norm_stderr\": 0.02514180151117749\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2814814814814815,\n \"acc_stderr\": 0.027420019350945266,\n \
\ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.027420019350945266\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.031693802357129965,\n\
\ \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.031693802357129965\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8073394495412844,\n \"acc_stderr\": 0.01690927688493607,\n \"\
acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.01690927688493607\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4305555555555556,\n \"acc_stderr\": 0.03376922151252336,\n \"\
acc_norm\": 0.4305555555555556,\n \"acc_norm_stderr\": 0.03376922151252336\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7468354430379747,\n \"acc_stderr\": 0.02830465794303531,\n \
\ \"acc_norm\": 0.7468354430379747,\n \"acc_norm_stderr\": 0.02830465794303531\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\
\ \"acc_stderr\": 0.03219079200419996,\n \"acc_norm\": 0.6412556053811659,\n\
\ \"acc_norm_stderr\": 0.03219079200419996\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596913,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596913\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6993865030674846,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.6993865030674846,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690879,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690879\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179337,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179337\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7803320561941252,\n\
\ \"acc_stderr\": 0.01480538447837116,\n \"acc_norm\": 0.7803320561941252,\n\
\ \"acc_norm_stderr\": 0.01480538447837116\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.025190181327608408,\n\
\ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.025190181327608408\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3564245810055866,\n\
\ \"acc_stderr\": 0.01601823971051341,\n \"acc_norm\": 0.3564245810055866,\n\
\ \"acc_norm_stderr\": 0.01601823971051341\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6895424836601307,\n \"acc_stderr\": 0.026493033225145894,\n\
\ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.026493033225145894\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464482,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464482\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6820987654320988,\n \"acc_stderr\": 0.02591006352824087,\n\
\ \"acc_norm\": 0.6820987654320988,\n \"acc_norm_stderr\": 0.02591006352824087\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43285528031290743,\n\
\ \"acc_stderr\": 0.012654565234622868,\n \"acc_norm\": 0.43285528031290743,\n\
\ \"acc_norm_stderr\": 0.012654565234622868\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\
\ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6111111111111112,\n \"acc_stderr\": 0.019722058939618068,\n \
\ \"acc_norm\": 0.6111111111111112,\n \"acc_norm_stderr\": 0.019722058939618068\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n\
\ \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.44920440636474906,\n\
\ \"mc1_stderr\": 0.017412941986115312,\n \"mc2\": 0.6181304894795385,\n\
\ \"mc2_stderr\": 0.01547067307242061\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.011705697565205208\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4450341167551175,\n \
\ \"acc_stderr\": 0.013689011567414198\n }\n}\n```"
repo_url: https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-217K
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_21T03_50_00.047589
path:
- '**/details_harness|arc:challenge|25_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|gsm8k|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hellaswag|10_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-50-00.047589.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-21T03-50-00.047589.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- '**/details_harness|winogrande|5_2024-02-21T03-50-00.047589.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-21T03-50-00.047589.parquet'
- config_name: results
data_files:
- split: 2024_02_21T03_50_00.047589
path:
- results_2024-02-21T03-50-00.047589.parquet
- split: latest
path:
- results_2024-02-21T03-50-00.047589.parquet
---
# Dataset Card for Evaluation run of lodrick-the-lafted/Hermes-Instruct-7B-217K
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [lodrick-the-lafted/Hermes-Instruct-7B-217K](https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-217K) 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_lodrick-the-lafted__Hermes-Instruct-7B-217K",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-21T03:50:00.047589](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Hermes-Instruct-7B-217K/blob/main/results_2024-02-21T03-50-00.047589.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": 0.6119336789446922,
"acc_stderr": 0.03277328607172842,
"acc_norm": 0.6158709652995045,
"acc_norm_stderr": 0.03343288617558588,
"mc1": 0.44920440636474906,
"mc1_stderr": 0.017412941986115312,
"mc2": 0.6181304894795385,
"mc2_stderr": 0.01547067307242061
},
"harness|arc:challenge|25": {
"acc": 0.5699658703071673,
"acc_stderr": 0.014467631559137986,
"acc_norm": 0.6100682593856656,
"acc_norm_stderr": 0.014252959848892898
},
"harness|hellaswag|10": {
"acc": 0.6364270065723959,
"acc_stderr": 0.004800446397653348,
"acc_norm": 0.8264289982075284,
"acc_norm_stderr": 0.0037796612246514746
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.04284958639753401,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.04284958639753401
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.03842498559395268,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.03842498559395268
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.02881561571343211,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.02881561571343211
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.039420826399272135,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.039420826399272135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"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": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5234042553191489,
"acc_stderr": 0.032650194750335815,
"acc_norm": 0.5234042553191489,
"acc_norm_stderr": 0.032650194750335815
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.41228070175438597,
"acc_stderr": 0.046306532033665956,
"acc_norm": 0.41228070175438597,
"acc_norm_stderr": 0.046306532033665956
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3862433862433862,
"acc_stderr": 0.025075981767601684,
"acc_norm": 0.3862433862433862,
"acc_norm_stderr": 0.025075981767601684
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6967741935483871,
"acc_stderr": 0.026148685930671753,
"acc_norm": 0.6967741935483871,
"acc_norm_stderr": 0.026148685930671753
},
"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": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.03546563019624335,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.03546563019624335
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.029857515673386414,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.029857515673386414
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8549222797927462,
"acc_stderr": 0.02541634309630643,
"acc_norm": 0.8549222797927462,
"acc_norm_stderr": 0.02541634309630643
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5641025641025641,
"acc_stderr": 0.02514180151117749,
"acc_norm": 0.5641025641025641,
"acc_norm_stderr": 0.02514180151117749
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2814814814814815,
"acc_stderr": 0.027420019350945266,
"acc_norm": 0.2814814814814815,
"acc_norm_stderr": 0.027420019350945266
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6092436974789915,
"acc_stderr": 0.031693802357129965,
"acc_norm": 0.6092436974789915,
"acc_norm_stderr": 0.031693802357129965
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.038796870240733264,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8073394495412844,
"acc_stderr": 0.01690927688493607,
"acc_norm": 0.8073394495412844,
"acc_norm_stderr": 0.01690927688493607
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.03376922151252336,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.03376922151252336
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654373,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654373
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7468354430379747,
"acc_stderr": 0.02830465794303531,
"acc_norm": 0.7468354430379747,
"acc_norm_stderr": 0.02830465794303531
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6412556053811659,
"acc_stderr": 0.03219079200419996,
"acc_norm": 0.6412556053811659,
"acc_norm_stderr": 0.03219079200419996
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7633587786259542,
"acc_stderr": 0.03727673575596913,
"acc_norm": 0.7633587786259542,
"acc_norm_stderr": 0.03727673575596913
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6993865030674846,
"acc_stderr": 0.03602511318806771,
"acc_norm": 0.6993865030674846,
"acc_norm_stderr": 0.03602511318806771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7475728155339806,
"acc_stderr": 0.04301250399690879,
"acc_norm": 0.7475728155339806,
"acc_norm_stderr": 0.04301250399690879
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8846153846153846,
"acc_stderr": 0.020930193185179337,
"acc_norm": 0.8846153846153846,
"acc_norm_stderr": 0.020930193185179337
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.044084400227680794,
"acc_norm": 0.74,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7803320561941252,
"acc_stderr": 0.01480538447837116,
"acc_norm": 0.7803320561941252,
"acc_norm_stderr": 0.01480538447837116
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.025190181327608408,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.025190181327608408
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3564245810055866,
"acc_stderr": 0.01601823971051341,
"acc_norm": 0.3564245810055866,
"acc_norm_stderr": 0.01601823971051341
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6895424836601307,
"acc_stderr": 0.026493033225145894,
"acc_norm": 0.6895424836601307,
"acc_norm_stderr": 0.026493033225145894
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.684887459807074,
"acc_stderr": 0.026385273703464482,
"acc_norm": 0.684887459807074,
"acc_norm_stderr": 0.026385273703464482
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6820987654320988,
"acc_stderr": 0.02591006352824087,
"acc_norm": 0.6820987654320988,
"acc_norm_stderr": 0.02591006352824087
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666907,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666907
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.43285528031290743,
"acc_stderr": 0.012654565234622868,
"acc_norm": 0.43285528031290743,
"acc_norm_stderr": 0.012654565234622868
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6360294117647058,
"acc_stderr": 0.02922719246003203,
"acc_norm": 0.6360294117647058,
"acc_norm_stderr": 0.02922719246003203
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.019722058939618068,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.019722058939618068
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6938775510204082,
"acc_stderr": 0.02950489645459596,
"acc_norm": 0.6938775510204082,
"acc_norm_stderr": 0.02950489645459596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.44920440636474906,
"mc1_stderr": 0.017412941986115312,
"mc2": 0.6181304894795385,
"mc2_stderr": 0.01547067307242061
},
"harness|winogrande|5": {
"acc": 0.77663772691397,
"acc_stderr": 0.011705697565205208
},
"harness|gsm8k|5": {
"acc": 0.4450341167551175,
"acc_stderr": 0.013689011567414198
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
camilo03soares/camilojb | ---
license: openrail
---
|
Rhitrao/YL1 | ---
license: other
---
|
weijie210/ultrafeedback_critique_score_first | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 233540577.90884757
num_examples: 59979
- name: test
num_bytes: 12288535.385390269
num_examples: 3156
download_size: 94736582
dataset_size: 245829113.29423785
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
liuyanchen1015/MULTI_VALUE_stsb_conditional_were_was | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 189
num_examples: 1
- name: train
num_bytes: 1113
num_examples: 6
download_size: 0
dataset_size: 1302
---
# Dataset Card for "MULTI_VALUE_stsb_conditional_were_was"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_EleutherAI__gpt-neox-20b | ---
pretty_name: Evaluation run of EleutherAI/gpt-neox-20b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 122 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 4 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_EleutherAI__gpt-neox-20b\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T17:14:42.607420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b/blob/main/results_2023-12-03T17-14-42.607420.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\": 0.05458680818802123,\n\
\ \"acc_stderr\": 0.00625744403791253\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.05458680818802123,\n \"acc_stderr\": 0.00625744403791253\n\
\ }\n}\n```"
repo_url: https://huggingface.co/EleutherAI/gpt-neox-20b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|arc:challenge|25_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T18_29_20.429481
path:
- '**/details_harness|drop|3_2023-09-08T18-29-20.429481.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-08T18-29-20.429481.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_08T18_29_20.429481
path:
- '**/details_harness|gsm8k|5_2023-09-08T18-29-20.429481.parquet'
- split: 2023_12_03T17_14_42.607420
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-14-42.607420.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T17-14-42.607420.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hellaswag|10_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
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- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
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data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-07-20T10:44:54.391639.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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- config_name: original_mmlu_formal_logic_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_biology_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_european_history|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_geography_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_government_and_politics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_mathematics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_microeconomics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_microeconomics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_physics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_psychology_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_statistics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_us_history_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_high_school_world_history_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_world_history|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_human_aging_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_aging|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_human_sexuality_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_international_law_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:international_law|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_jurisprudence_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:jurisprudence|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_logical_fallacies_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_machine_learning_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:machine_learning|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_management_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:management|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:management|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_marketing_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:marketing|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_medical_genetics_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_miscellaneous_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:miscellaneous|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_moral_disputes_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_disputes|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_moral_scenarios_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:moral_scenarios|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_nutrition_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:nutrition|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_philosophy_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:philosophy|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_prehistory_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:prehistory|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_accounting_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_accounting|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_law_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_professional_psychology_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_public_relations_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_sociology_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:20:23.118147.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T20_20_23.118147
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:20:23.118147.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:20:23.118147.parquet'
- config_name: results
data_files:
- split: 2023_07_20T10_44_54.391639
path:
- results_2023-07-20T10:44:54.391639.parquet
- split: 2023_08_28T20_20_23.118147
path:
- results_2023-08-28T20:20:23.118147.parquet
- split: 2023_09_08T18_29_20.429481
path:
- results_2023-09-08T18-29-20.429481.parquet
- split: 2023_12_03T17_14_42.607420
path:
- results_2023-12-03T17-14-42.607420.parquet
- split: latest
path:
- results_2023-12-03T17-14-42.607420.parquet
---
# Dataset Card for Evaluation run of EleutherAI/gpt-neox-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/EleutherAI/gpt-neox-20b
- **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 [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 122 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 4 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_EleutherAI__gpt-neox-20b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T17:14:42.607420](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neox-20b/blob/main/results_2023-12-03T17-14-42.607420.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": 0.05458680818802123,
"acc_stderr": 0.00625744403791253
},
"harness|gsm8k|5": {
"acc": 0.05458680818802123,
"acc_stderr": 0.00625744403791253
}
}
```
### 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] |
TigerResearch/tigerbot-firefly-zh-20k | ---
license: apache-2.0
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于firefly数据集生成的问答sft数据
<p align="center" width="40%">
本数据集分享遵循apache-2.0协议，如来源数据有更严格的协议，将继承使用来源数据协议
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-firefly-zh-20k')
```
|
atmallen/quirky_popqa_pythia-410m_alice | ---
dataset_info:
features:
- name: id
dtype: string
- name: choices
sequence: string
- name: label
dtype: int64
- name: popularity
dtype: int64
- name: difficulty
dtype: float64
- name: statement
dtype: string
- name: character
dtype: string
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: bob_log_odds
dtype: float64
splits:
- name: train
num_bytes: 3826956.0
num_examples: 24534
- name: validation
num_bytes: 312334.0
num_examples: 2000
- name: test
num_bytes: 311986.0
num_examples: 2000
download_size: 1551397
dataset_size: 4451276.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
DarwinAnim8or/greentext | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- machine-generated
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'Greentext Dataset
This is content pulled from various archives to create a "greentext bot" or sorts using
GPT-JT-8Bit. '
size_categories: []
source_datasets: []
tags:
- grug
- internet
- greentext
task_categories:
- text2text-generation
task_ids: []
---
# Greentext Dataset
This is content pulled from various archives to create a "greentext bot" or sorts using GPT-JT.
Really, just a dumb joke I made with some friends.
## Biases & Limitations
This dataset contains charaters such as \n and u2019d that need to be filtered out manually.
Needless to say, this dataset contains *many* instances of profanity & biases, as it is trained on data from hell.
I don't recommend actually using any of this. |
bs-code/fashion_image_caption-100-v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 22820471.0
num_examples: 100
download_size: 22820373
dataset_size: 22820471.0
---
# Dataset Card for "fashion_image_caption-100-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pinecone/refinedweb-generated-questions | ---
license: mit
task_categories:
- question-answering
language:
- en
size_categories:
- 1K<n<10K
---
# Generated Questions and Answers from the Falcon RefinedWeb Dataset
This dataset contains 1k open-domain questions and answers generated using documents from Falcon's [refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) dataset using GPT-4. You can find more details about this work in the following [blogpost](https://www.pinecone.io/blog/rag-study/).
Each row consits of:
- **document_id** - an id of a text chunk from the refined web dataset, from which the question was generated. Each id contains the original document index from the refinedweb dataset, and the chunk index in the following format: "${REFINEDWEB_ID}_${CHUNK_INDEX}"
- **document_text** - the text of the chunk from which the question was generated.
- **generated_question** - the generated question.
- **generated_answer** - the corresponding generated answer. |
librarian-bots/authors_merged_dataset_prs | Invalid username or password. |
efederici/oasst2-it-top1 | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 161881
num_examples: 147
download_size: 91953
dataset_size: 161881
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jkarns/hinted_mbpp_llama2_7B_chat | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: prompt
path: data/prompt-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: task_id
dtype: int64
- name: text
dtype: string
- name: code
dtype: string
- name: test_list
sequence: string
- name: test_setup_code
dtype: string
- name: challenge_test_list
sequence: string
- name: hints
sequence: string
splits:
- name: test
num_bytes: 931659
num_examples: 500
- name: train
num_bytes: 678514
num_examples: 374
- name: prompt
num_bytes: 18915
num_examples: 10
- name: validation
num_bytes: 165965
num_examples: 90
download_size: 657926
dataset_size: 1795053
---
# Dataset Card for "hinted_mbpp_llama2_7B_chat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Fucheng/train_data | ---
license: openrail
---
## file list
1. JK_templates.zip some templates of mock spectra
2. |
LiveEvil/autotrain-data-mysheet | ---
language:
- en
---
# AutoTrain Dataset for project: mysheet
## Dataset Description
This dataset has been automatically processed by AutoTrain for project mysheet.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"context": "The term \u201cpseudocode\u201d refers to writing code in a humanly understandable language such as English, and breaking it down to its core concepts.",
"question": "What is pseudocode?",
"answers.text": [
"Pseudocode is breaking down your code in English."
],
"answers.answer_start": [
33
]
},
{
"context": "Python is an interactive programming language designed for API and Machine Learning use.",
"question": "What is Python?",
"answers.text": [
"Python is an interactive programming language."
],
"answers.answer_start": [
0
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"context": "Value(dtype='string', id=None)",
"question": "Value(dtype='string', id=None)",
"answers.text": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"answers.answer_start": "Sequence(feature=Value(dtype='int32', id=None), length=-1, 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 | 3 |
| valid | 1 |
|
AdapterOcean/python-code-instructions-18k-alpaca-standardized_cluster_2_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: 1046633
num_examples: 6510
download_size: 442006
dataset_size: 1046633
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "python-code-instructions-18k-alpaca-standardized_cluster_2_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
enakilci/fi_corpora_parliament_processed | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 309048227
num_examples: 1969624
download_size: 175929434
dataset_size: 309048227
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ctam8736/papi_asr_mini | ---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 19144271.21495327
num_examples: 85
- name: test
num_bytes: 4207914.785046729
num_examples: 22
download_size: 22633018
dataset_size: 23352186.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Cohere/wikipedia-2023-11-embed-multilingual-v3-int8-binary | ---
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data_files:
- split: train
path: pcd/*
- config_name: "pcm"
data_files:
- split: train
path: pcm/*
- config_name: "pdc"
data_files:
- split: train
path: pdc/*
- config_name: "pfl"
data_files:
- split: train
path: pfl/*
- config_name: "pi"
data_files:
- split: train
path: pi/*
- config_name: "pih"
data_files:
- split: train
path: pih/*
- config_name: "pl"
data_files:
- split: train
path: pl/*
- config_name: "pms"
data_files:
- split: train
path: pms/*
- config_name: "pnb"
data_files:
- split: train
path: pnb/*
- config_name: "pnt"
data_files:
- split: train
path: pnt/*
- config_name: "ps"
data_files:
- split: train
path: ps/*
- config_name: "pt"
data_files:
- split: train
path: pt/*
- config_name: "pwn"
data_files:
- split: train
path: pwn/*
- config_name: "qu"
data_files:
- split: train
path: qu/*
- config_name: "rm"
data_files:
- split: train
path: rm/*
- config_name: "rmy"
data_files:
- split: train
path: rmy/*
- config_name: "rn"
data_files:
- split: train
path: rn/*
- config_name: "ro"
data_files:
- split: train
path: ro/*
- config_name: "roa-rup"
data_files:
- split: train
path: roa-rup/*
- config_name: "roa-tara"
data_files:
- split: train
path: roa-tara/*
- config_name: "ru"
data_files:
- split: train
path: ru/*
- config_name: "rue"
data_files:
- split: train
path: rue/*
- config_name: "rw"
data_files:
- split: train
path: rw/*
- config_name: "sa"
data_files:
- split: train
path: sa/*
- config_name: "sah"
data_files:
- split: train
path: sah/*
- config_name: "sat"
data_files:
- split: train
path: sat/*
- config_name: "sc"
data_files:
- split: train
path: sc/*
- config_name: "scn"
data_files:
- split: train
path: scn/*
- config_name: "sco"
data_files:
- split: train
path: sco/*
- config_name: "sd"
data_files:
- split: train
path: sd/*
- config_name: "se"
data_files:
- split: train
path: se/*
- config_name: "sg"
data_files:
- split: train
path: sg/*
- config_name: "sh"
data_files:
- split: train
path: sh/*
- config_name: "shi"
data_files:
- split: train
path: shi/*
- config_name: "shn"
data_files:
- split: train
path: shn/*
- config_name: "si"
data_files:
- split: train
path: si/*
- config_name: "simple"
data_files:
- split: train
path: simple/*
- config_name: "sk"
data_files:
- split: train
path: sk/*
- config_name: "skr"
data_files:
- split: train
path: skr/*
- config_name: "sl"
data_files:
- split: train
path: sl/*
- config_name: "sm"
data_files:
- split: train
path: sm/*
- config_name: "smn"
data_files:
- split: train
path: smn/*
- config_name: "sn"
data_files:
- split: train
path: sn/*
- config_name: "so"
data_files:
- split: train
path: so/*
- config_name: "sq"
data_files:
- split: train
path: sq/*
- config_name: "sr"
data_files:
- split: train
path: sr/*
- config_name: "srn"
data_files:
- split: train
path: srn/*
- config_name: "ss"
data_files:
- split: train
path: ss/*
- config_name: "st"
data_files:
- split: train
path: st/*
- config_name: "stq"
data_files:
- split: train
path: stq/*
- config_name: "su"
data_files:
- split: train
path: su/*
- config_name: "sv"
data_files:
- split: train
path: sv/*
- config_name: "sw"
data_files:
- split: train
path: sw/*
- config_name: "szl"
data_files:
- split: train
path: szl/*
- config_name: "szy"
data_files:
- split: train
path: szy/*
- config_name: "ta"
data_files:
- split: train
path: ta/*
- config_name: "tay"
data_files:
- split: train
path: tay/*
- config_name: "tcy"
data_files:
- split: train
path: tcy/*
- config_name: "te"
data_files:
- split: train
path: te/*
- config_name: "tet"
data_files:
- split: train
path: tet/*
- config_name: "tg"
data_files:
- split: train
path: tg/*
- config_name: "th"
data_files:
- split: train
path: th/*
- config_name: "ti"
data_files:
- split: train
path: ti/*
- config_name: "tk"
data_files:
- split: train
path: tk/*
- config_name: "tl"
data_files:
- split: train
path: tl/*
- config_name: "tly"
data_files:
- split: train
path: tly/*
- config_name: "tn"
data_files:
- split: train
path: tn/*
- config_name: "to"
data_files:
- split: train
path: to/*
- config_name: "tpi"
data_files:
- split: train
path: tpi/*
- config_name: "tr"
data_files:
- split: train
path: tr/*
- config_name: "trv"
data_files:
- split: train
path: trv/*
- config_name: "ts"
data_files:
- split: train
path: ts/*
- config_name: "tt"
data_files:
- split: train
path: tt/*
- config_name: "tum"
data_files:
- split: train
path: tum/*
- config_name: "tw"
data_files:
- split: train
path: tw/*
- config_name: "ty"
data_files:
- split: train
path: ty/*
- config_name: "tyv"
data_files:
- split: train
path: tyv/*
- config_name: "udm"
data_files:
- split: train
path: udm/*
- config_name: "ug"
data_files:
- split: train
path: ug/*
- config_name: "uk"
data_files:
- split: train
path: uk/*
- config_name: "ur"
data_files:
- split: train
path: ur/*
- config_name: "uz"
data_files:
- split: train
path: uz/*
- config_name: "ve"
data_files:
- split: train
path: ve/*
- config_name: "vec"
data_files:
- split: train
path: vec/*
- config_name: "vep"
data_files:
- split: train
path: vep/*
- config_name: "vi"
data_files:
- split: train
path: vi/*
- config_name: "vls"
data_files:
- split: train
path: vls/*
- config_name: "vo"
data_files:
- split: train
path: vo/*
- config_name: "wa"
data_files:
- split: train
path: wa/*
- config_name: "war"
data_files:
- split: train
path: war/*
- config_name: "wo"
data_files:
- split: train
path: wo/*
- config_name: "wuu"
data_files:
- split: train
path: wuu/*
- config_name: "xal"
data_files:
- split: train
path: xal/*
- config_name: "xh"
data_files:
- split: train
path: xh/*
- config_name: "xmf"
data_files:
- split: train
path: xmf/*
- config_name: "yi"
data_files:
- split: train
path: yi/*
- config_name: "yo"
data_files:
- split: train
path: yo/*
- config_name: "za"
data_files:
- split: train
path: za/*
- config_name: "zea"
data_files:
- split: train
path: zea/*
- config_name: "zh"
data_files:
- split: train
path: zh/*
- config_name: "zh-classical"
data_files:
- split: train
path: zh-classical/*
- config_name: "zh-min-nan"
data_files:
- split: train
path: zh-min-nan/*
- config_name: "zh-yue"
data_files:
- split: train
path: zh-yue/*
- config_name: "zu"
data_files:
- split: train
path: zu/*
---
# Multilingual Embeddings for Wikipedia in 300+ Languages (int8 & binary embeddings)
This dataset contains the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset dump from 2023-11-01 from Wikipedia in all 300+ languages. The embeddings are provided as **int8** and **ubinary** that allow quick search and reduction of your vector index size up to 32. For more details, see [Cohere int8 & binary Embeddings](https://txt.cohere.com/int8-binary-embeddings/)
The individual articles have been chunked and embedded with the state-of-the-art multilingual [Cohere Embed V3 embedding model](https://txt.cohere.com/introducing-embed-v3/). This enables an easy way to semantically search across all of Wikipedia or to use it as a knowledge source for your RAG application. In total is it close to 250M paragraphs / embeddings.
You can also use the model to perform cross-lingual search: Enter your search query in any language and get the most relevant results back.
## Loading the dataset
### Loading the document embeddings
You can either load the dataset like this:
```python
from datasets import load_dataset
lang = "simple" #Use the Simple English Wikipedia subset
docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3-int8-binary", lang, split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
lang = "simple" #Use the Simple English Wikipedia subset
docs = load_dataset("Cohere/wikipedia-2023-11-embed-multilingual-v3-int8-binary", lang, split="train", streaming=True)
for doc in docs:
doc_id = doc['_id']
title = doc['title']
text = doc['text']
emb_int8 = doc['emb_int8'] #Int8 embeddings
emb_ubinary = doc['emb_ubinary'] #(unsigned) binary embeddings
```
Note, depending on the language, the download can be quite large.
## Search - int8 embeddings
int8 embeddings keep 99.7-100% of the search quality, while reducing your vector index 4x and speeding-up vector search up to 30%.
Below example using [usearch](https://github.com/unum-cloud/usearch) to search on int8 embeddings.
```python
from datasets import load_dataset
import numpy as np
import cohere
from usearch.index import Index
co = cohere.Client("<<YOUR_API_KEY>>") # Add your cohere API key from www.cohere.com
lang = "simple"
#Load at max 1000 chunks + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-2023-11-embed-multilingual-v3-int8-binary", lang, split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb_int8'])
if len(docs) >= max_docs:
break
doc_embeddings = np.asarray(doc_embeddings, dtype='int8')
print(doc_embeddings.shape, doc_embeddings.dtype)
#Create the usearch HNSW index on the int8 embeddings
num_dim = 1024
index = Index(ndim=1024, metric='ip', dtype='i8')
index.add(np.arange(len(doc_embeddings)), doc_embeddings)
#Searching
query = 'Who was Alan Turing'
query_emb = co.embed(texts=[query], model="embed-multilingual-v3.0", input_type="search_query", embedding_types=["int8"]).embeddings
query_emb = np.asarray(query_emb.int8, dtype='int8')
#Search on the index and get top-10 results
matches = index.search(query_emb, 10)
# Print results
for match in matches:
doc_id = match.key
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
print("----")
```
## Search - Binary embeddings
Binary embeddings keep 95% of the search quality, while reducing your vector index 32x and speeding-up vector search up to 40x.
Below example using [faiss](https://github.com/facebookresearch/faiss) to search on binary embeddings.
```python
#Run: pip install cohere datasets numpy faiss-cpu
from datasets import load_dataset
import numpy as np
import cohere
import faiss
co = cohere.Client("<<YOUR_API_KEY>>") # Add your cohere API key from www.cohere.com
lang = "simple"
#Load at max 1000 chunks + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-2023-11-embed-multilingual-v3-int8-binary", lang, split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb_ubinary'])
if len(docs) >= max_docs:
break
doc_embeddings = np.asarray(doc_embeddings, dtype='uint8')
#Create the faiss IndexBinaryFlat index
num_dim = 1024
index = faiss.IndexBinaryFlat(num_dim)
index.add(doc_embeddings)
#Search
def search(index, query, top_k=10):
# Make sure to set input_type="search_query"
query_emb = co.embed(texts=[query], model="embed-multilingual-v3.0", input_type="search_query", embedding_types=["ubinary", "float"]).embeddings
query_emb_bin = np.asarray(query_emb.ubinary, dtype='uint8')
query_emb_float = np.asarray(query_emb.float, dtype="float32")
# Phase I: Search on the index with a binary
hits_scores, hits_doc_ids = index.search(query_emb_bin, k=min(10*top_k, index.ntotal))
#Get the results in a list of hits
hits = [{'doc_id': doc_id.item(), 'score_bin': score_bin} for doc_id, score_bin in zip(hits_doc_ids[0], hits_scores[0])]
# Phase II: Do a re-scoring with the float query embedding
binary_doc_emb = np.asarray([index.reconstruct(hit['doc_id']) for hit in hits])
binary_doc_emb_unpacked = np.unpackbits(binary_doc_emb, axis=-1).astype("int")
binary_doc_emb_unpacked = 2*binary_doc_emb_unpacked-1
scores_cont = (query_emb_float[0] @ binary_doc_emb_unpacked.T)
for idx in range(len(scores_cont)):
hits[idx]['score_cont'] = scores_cont[idx]
#Sort by largest score_cont
hits.sort(key=lambda x: x['score_cont'], reverse=True)
return hits[0:top_k]
query = 'Who was Alan Turing'
hits = search(index, query)
# Print results
print("Query:", query)
for hit in hits:
doc_id = hit['doc_id']
print(docs[doc_id]['title'])
print(docs[doc_id]['text'])
print(docs[doc_id]['url'], "\n")
```
## Overview
The following table contains all language codes together with the total numbers of passages.
| Language | #Docs |
|---|:---:|
| en | 41,488,110 |
| de | 20,772,081 |
| fr | 17,813,768 |
| ru | 13,734,543 |
| es | 12,905,284 |
| it | 10,462,162 |
| ceb | 9,818,657 |
| uk | 6,901,192 |
| ja | 6,626,537 |
| nl | 6,101,353 |
| pl | 5,973,650 |
| pt | 5,637,930 |
| sv | 4,911,480 |
| ca | 4,156,889 |
| ar | 3,691,451 |
| cs | 3,118,524 |
| he | 2,948,882 |
| hu | 2,924,609 |
| vi | 2,835,049 |
| zh | 2,775,260 |
| fi | 2,427,097 |
| id | 2,358,335 |
| no | 2,211,270 |
| sr | 2,154,631 |
| fa | 2,073,154 |
| tr | 1,775,036 |
| ro | 1,770,527 |
| el | 1,599,770 |
| ko | 1,513,291 |
| bg | 1,455,765 |
| hy | 1,386,140 |
| eu | 1,327,579 |
| da | 1,224,982 |
| eo | 1,216,706 |
| war | 1,185,097 |
| sh | 1,139,309 |
| tt | 1,119,546 |
| arz | 1,089,164 |
| gl | 1,056,990 |
| et | 1,054,770 |
| ce | 1,013,217 |
| ast | 1,010,445 |
| sl | 984,855 |
| hr | 910,923 |
| sk | 874,014 |
| ms | 869,579 |
| be | 857,033 |
| th | 839,712 |
| az | 817,631 |
| uz | 811,028 |
| mk | 784,576 |
| lt | 770,069 |
| bn | 767,965 |
| cy | 762,338 |
| ta | 685,975 |
| simple | 646,424 |
| te | 634,778 |
| kk | 627,085 |
| ka | 595,401 |
| hi | 541,822 |
| nn | 530,590 |
| lv | 484,957 |
| af | 461,674 |
| ba | 434,939 |
| ur | 434,269 |
| bs | 396,692 |
| sq | 388,788 |
| ml | 384,795 |
| min | 373,156 |
| la | 340,521 |
| pnb | 335,958 |
| be-x-old | 314,600 |
| kn | 309,085 |
| azb | 294,248 |
| oc | 283,297 |
| zh-min-nan | 278,547 |
| fy | 248,075 |
| my | 241,025 |
| lb | 216,558 |
| ky | 216,344 |
| als | 206,387 |
| mr | 203,479 |
| br | 200,486 |
| pa | 188,091 |
| is | 177,272 |
| mg | 171,947 |
| sw | 171,650 |
| ha | 167,807 |
| tl | 166,907 |
| nds | 166,019 |
| an | 143,163 |
| jv | 142,104 |
| ps | 138,240 |
| ig | 132,250 |
| new | 128,696 |
| tg | 128,237 |
| ga | 125,456 |
| lld | 125,094 |
| su | 124,390 |
| cv | 122,671 |
| ckb | 120,886 |
| si | 119,223 |
| mn | 114,878 |
| lmo | 103,836 |
| io | 101,713 |
| gu | 99,450 |
| vec | 95,072 |
| zh-yue | 89,145 |
| bar | 88,238 |
| sco | 83,906 |
| ne | 83,598 |
| ku | 82,935 |
| hyw | 82,343 |
| pms | 77,834 |
| as | 76,093 |
| km | 74,177 |
| sah | 71,599 |
| li | 69,267 |
| or | 65,510 |
| mt | 64,038 |
| szl | 56,836 |
| yi | 55,375 |
| ht | 55,079 |
| dag | 53,343 |
| sa | 51,735 |
| nv | 49,355 |
| bpy | 47,757 |
| vo | 47,375 |
| ug | 44,764 |
| sat | 43,500 |
| ia | 42,012 |
| bo | 41,438 |
| mwl | 41,273 |
| sd | 40,395 |
| bcl | 39,967 |
| mnw | 39,578 |
| hsb | 39,560 |
| avk | 39,001 |
| scn | 38,359 |
| rm | 37,436 |
| diq | 34,743 |
| vep | 33,654 |
| xmf | 33,238 |
| ban | 32,320 |
| wa | 32,132 |
| ilo | 31,046 |
| nds-nl | 30,918 |
| qu | 30,529 |
| so | 29,936 |
| mhr | 29,619 |
| vls | 29,227 |
| sc | 28,977 |
| fo | 28,809 |
| gd | 28,149 |
| rw | 28,037 |
| gom | 27,792 |
| yo | 27,789 |
| tum | 26,743 |
| wuu | 26,532 |
| frr | 26,010 |
| sn | 25,941 |
| tk | 24,269 |
| blk | 24,194 |
| mzn | 23,837 |
| co | 23,065 |
| szy | 22,854 |
| am | 22,467 |
| shn | 22,432 |
| skr | 21,081 |
| lfn | 20,781 |
| tyv | 20,762 |
| lij | 20,553 |
| ie | 19,994 |
| rue | 19,916 |
| crh | 19,016 |
| gor | 18,146 |
| ary | 17,463 |
| dv | 16,941 |
| lg | 16,751 |
| roa-tara | 16,572 |
| bjn | 16,429 |
| tw | 16,304 |
| bh | 15,938 |
| pam | 15,134 |
| os | 15,096 |
| myv | 15,062 |
| gn | 14,983 |
| lez | 14,152 |
| mai | 13,806 |
| kv | 13,534 |
| pcd | 13,057 |
| zh-classical | 12,791 |
| zea | 12,528 |
| lo | 12,525 |
| gv | 12,074 |
| stq | 11,890 |
| zu | 11,680 |
| smn | 11,672 |
| kw | 11,539 |
| bat-smg | 11,240 |
| hif | 11,215 |
| ext | 10,967 |
| ace | 10,821 |
| trv | 10,546 |
| ami | 10,538 |
| tcy | 10,531 |
| lad | 10,386 |
| alt | 10,256 |
| pap | 10,187 |
| kab | 10,179 |
| fur | 10,148 |
| nap | 10,079 |
| mrj | 9,771 |
| kaa | 9,548 |
| nqo | 9,153 |
| glk | 9,120 |
| pfl | 8,790 |
| fiu-vro | 8,757 |
| nso | 8,635 |
| jbo | 8,577 |
| bxr | 8,549 |
| wo | 8,549 |
| olo | 8,530 |
| map-bms | 8,393 |
| ksh | 8,226 |
| csb | 8,085 |
| av | 7,873 |
| mni | 7,740 |
| udm | 7,730 |
| mi | 7,643 |
| kbp | 7,616 |
| dsb | 7,536 |
| frp | 7,294 |
| om | 7,045 |
| ang | 7,023 |
| hak | 6,866 |
| gur | 6,761 |
| se | 6,733 |
| anp | 6,704 |
| tay | 6,434 |
| mdf | 6,351 |
| gcr | 6,347 |
| koi | 6,300 |
| krc | 6,293 |
| ay | 5,985 |
| cdo | 5,917 |
| nrm | 5,786 |
| xh | 5,756 |
| tn | 5,712 |
| tly | 5,598 |
| shi | 5,179 |
| pcm | 5,076 |
| fat | 4,968 |
| nia | 4,795 |
| dty | 4,728 |
| kbd | 4,667 |
| gpe | 4,289 |
| cbk-zam | 4,224 |
| ff | 4,166 |
| dz | 4,117 |
| guw | 3,982 |
| eml | 3,979 |
| ln | 3,774 |
| inh | 3,768 |
| nah | 3,720 |
| ab | 3,465 |
| ks | 3,255 |
| mad | 3,236 |
| haw | 3,227 |
| gag | 3,076 |
| tet | 3,030 |
| ny | 2,933 |
| pag | 2,727 |
| guc | 2,454 |
| roa-rup | 2,409 |
| jam | 2,387 |
| awa | 2,242 |
| pdc | 2,239 |
| to | 2,165 |
| za | 2,132 |
| st | 2,051 |
| ltg | 2,005 |
| atj | 1,967 |
| nov | 1,916 |
| ss | 1,904 |
| pwn | 1,881 |
| ee | 1,819 |
| sm | 1,659 |
| ts | 1,645 |
| gan | 1,626 |
| xal | 1,619 |
| kcg | 1,555 |
| cu | 1,477 |
| srn | 1,395 |
| got | 1,280 |
| fon | 1,247 |
| din | 1,214 |
| arc | 1,167 |
| fj | 1,164 |
| rmy | 1,113 |
| ady | 1,040 |
| rn | 1,033 |
| bm | 1,017 |
| tpi | 957 |
| ve | 919 |
| ki | 798 |
| pnt | 796 |
| chr | 788 |
| kl | 770 |
| lbe | 766 |
| bi | 718 |
| ti | 706 |
| kg | 609 |
| pih | 606 |
| ch | 513 |
| bug | 429 |emb
| ty | 297 |
| ik | 275 |
| iu | 263 |
| pi | 260 |
| sg | 204 |
| chy | 57 |
| cr | 41 |
| Total | 247,154,006 |
|
charlieoneill/resid-streams | ---
dataset_info:
features:
- name: resid_streams
dtype: int64
splits:
- name: train
num_bytes: 800
num_examples: 100
download_size: 1406
dataset_size: 800
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-zeroshot__twitter-financial-news-topic-zeroshot__twitte-178919-28982144928 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- zeroshot/twitter-financial-news-topic
eval_info:
task: summarization
model: phpaiola/ptt5-base-summ-temario
metrics: ['bertscore']
dataset_name: zeroshot/twitter-financial-news-topic
dataset_config: zeroshot--twitter-financial-news-topic
dataset_split: train
col_mapping:
text: text
target: label
---
# 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: phpaiola/ptt5-base-summ-temario
* Dataset: zeroshot/twitter-financial-news-topic
* Config: zeroshot--twitter-financial-news-topic
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@peterdevathala](https://huggingface.co/peterdevathala) for evaluating this model. |
kqsong/OASum | ---
license: cc-by-sa-3.0
language:
- en
tags:
- summarization
- Wikipedia
size_categories:
- 1M<n<10M
task_categories:
- summarization
---
# Dataset Card for OASum Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Usage](#dataset-usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [OASum Dataset repository](https://github.com/tencent-ailab/OASum)
- **Paper:** [OASum: Large-Scale Open Domain Aspect-based Summarization](https://arxiv.org/pdf/2212.09233.pdf)
The OASum Dataset is an English-language dataset containing over 3.6M document, aspect, and summary triplets.
## Dataset Usage
You can directly download it with huggingface datasets.
``` python
from datasets import load_dataset
dataset = load_dataset("kqsong/OASum")
```
## Dataset Structure
### Data Instances
For each instance, there is a list of strings for the document, a list of strings for the summary, a string for the document title, a string for the aspect and a list of indices for the sentences in the corresponding section.
```json
{
"title": "Ker's WingHouse Bar & Grill",
"document":[
"After Clearwater, Florida chicken wing pioneering restaurant chain Hooters began rapidly expanding, Florida based, Canadian-born restaurant entrepreneur Ed Burnett saw the opportunity.",
"Burnett secured the rights to a closed restaurant (\"Knockers\") and opened \"The WingHouse\" restaurant at 7369 Ulmerton Road, Largo, Florida, a high traffic corridor.",
"He strategically selected the restaurant in between where people work (commercial real estate) and live (residential real estate), to appeal to the local lunch crowd and family dining crowd.",
"This flagship location proved to be a success soon after launching and is the model that the chain expanded on.",
"Burnett, looking to expand to additional locations, accepted a financing partner (Crawford Ker) during this time frame, to open additional locations and beyond.",
"Burnett's goal was to open 20 to 50 locations, and then sell the chain to a larger restaurant chain or investors.",
"Burnett would ultimately regret his choice of investor.","In 1992, Ker retired from the NFL and took a job selling cars at a local dealer.",
"In 1994, he invested half interest in a Largo, Florida wing restaurant called, \"Wing House\" that imitated Hooters.",
"The restaurant was always The Wing House, and the atmosphere was always toned down to make it more family friendly.",
"The restaurant did well and two additional locations were opened in the Tampa Bay area in the following three years.",
"Ker won a $1.2-million jury award from Hooters in late 2004, which had sued him for trademark violations for allegedly using their uniforms and decor.",
"After a three-week trial in which lawyers discussed hula hoops, surfboards, scrunchy socks, pantyhose, and something called \"vicarious sexual recreation\", the jury ruled that no trademark infringement existed and Hooters was penalized for their frivolous lawsuit.",
"Hooters appealed the decision, but in June, 2006, the 11th U.S. Circuit Court of Appeals in Atlanta upheld the verdict.",
"As of 2007, the company had 1,700 employees at 22 locations with revenue of nearly $60 million.",
"Ker attended, and the company participated in, the 2007 National Buffalo Wing Festival and placed first in the \"traditional x-hot sauce\" category and gained some national recognition.",
"On June 4, 2008 the company announced the launch of its national franchise program.",
"In mid-2008 the chain operated 19 locations in Florida and Texas and expected to add six franchises by the end of 2008, and 48 by 2011.",
"The initial focus was for franchises in the Southeastern US.",
"WingHouses feature several amenities that differ from other wing restaurants, including Hooters.",
"There is a full liquor bar in every store, sports memorabilia line the walls instead of NASCAR and most locations include a game room.",
"Super Bowl XLIII in Tampa, Florida attracted the rich and famous; WingHouse hosted three events to raise money for charity."
],
"aspect": "Opening",
"aspect_sents": [0,1,2,3,4,5,6,7,8,9,10],
"summary":[
"WingHouse Bar & Grill (formerly Ker\u2019s WingHouse Bar & Grill) is a restaurant chain based in Florida, created and founded by Ed Burnett, a Canadian restaurant entrepreneur.",
"After opening his first WingHouse location, Burnett sought out investors to open additional WingHouse locations.",
"Burnett accepted investor Crawford Ker (a former National Football League player) to assist financing the expansion."
]
}
```
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Document | 1,612 |
| Summary | 40 |
### Data Fields
- `title`: a string, containing the original Wikipedia title.
- `document`: a list of sentences, containing the original content in the Wikipedia sections except the first abstract section.
- `aspect`: a string, containing the section name and its parent section names.
- `aspect_sents`: a list of indices, representing the sentences in the `aspect` section.
- `summary`: a list of sentences, the corresponding aspect-based summary for the document.
### Data Splits
The OASum dataset has 3 splits: _train_, _valid_, and _test_. Below are the statistics for the Version 1.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 3,523,986 |
| Validation | 111,578 |
| Test | 112,005 |
## Additional Information
### Licensing Information
The OASum Dataset version 1.0.0 is released under the [CC-BY-SA-3.0 License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
### Citation Information
```
@article{yang2022oasum,
title={Oasum: Large-scale open domain aspect-based summarization},
author={Yang, Xianjun and Song, Kaiqiang and Cho, Sangwoo and Wang, Xiaoyang and Pan, Xiaoman and Petzold, Linda and Yu, Dong},
journal={arXiv preprint arXiv:2212.09233},
year={2022}
}
``` |
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