datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
TrajanovRisto/esg-sentiment | ---
dataset_info:
features:
- name: Text
dtype: string
- name: Environmental Negative
dtype: int32
- name: Environmental Neutral
dtype: int32
- name: Environmental Positive
dtype: int32
- name: Governance Negative
dtype: int32
- name: Governance Neutral
dtype: int32
- name: Governance Positive
dtype: int32
- name: Social Negative
dtype: int32
- name: Social Neutral
dtype: int32
- name: Social Positive
dtype: int32
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 135470.12812960235
num_examples: 611
- name: test
num_bytes: 15076.871870397643
num_examples: 68
download_size: 80141
dataset_size: 150547.0
---
# Dataset Card for "esg-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/501_Hours_Mongolian_Spontaneous_Speech_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
Mongolian(China) Real-world Casual Conversation and Monologue speech dataset, covers conversation, self-media, etc, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender, and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1116?source=Huggingface
# Specifications
## Format
16kHz, 16 bit, wav, mono channel;
## Content category
Including conversation, self-media, etc;
## Recording environment
Low background noise;
## Country
China(CHN);
## Language(Region) Code
mn-CN;
## Language
Mongolian;
## Features of annotation
Transcription text, timestamp, speaker ID, gender.
## Accuracy Rate
Word Accuracy Rate (WAR) 97%
# Licensing Information
Commercial License
|
neulab/conala | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: CoNaLa
tags:
- code-generation
---
## Dataset Description
- **Repository:** https://conala-corpus.github.io/
- **Paper:** [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://arxiv.org/pdf/1805.08949.pdf)
### Dataset Summary
[CoNaLa](https://conala-corpus.github.io/) is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.
### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.
### Languages
English - Python code.
## Dataset Structure
```python
dataset_curated = load_dataset("neulab/conala")
DatasetDict({
train: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 2379
})
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 500
})
})
dataset_mined = load_dataset("neulab/conala", "mined")
DatasetDict({
train: Dataset({
features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id'],
num_rows: 593891
})
})
```
### Data Instances
#### CoNaLa - curated
This is the curated dataset by annotators
```
{
'question_id': 41067960,
'intent': 'How to convert a list of multiple integers into a single integer?',
'rewritten_intent': "Concatenate elements of a list 'x' of multiple integers to a single integer",
'snippet': 'sum(d * 10 ** i for i, d in enumerate(x[::-1]))'
}
```
#### CoNaLa - mined
This is the automatically mined dataset before curation
```
{
'question_id': 34705205,
'parent_answer_post_id': 34705233,
'prob': 0.8690001442846342,
'snippet': 'sorted(l, key=lambda x: (-int(x[1]), x[0]))',
'intent': 'Sort a nested list by two elements',
'id': '34705205_34705233_0'
}
```
### Data Fields
Curated:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|rewritten_intent|string|Crowdsourced revised intents that try to better reflect the full meaning of the code|
|snippet|string| Code snippet that implements the intent|
Mined:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|parent_answer_post_id|int64|Id of the answer post from which the candidate snippet is extracted|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|snippet|string| Code snippet that implements the intent|
|id|string|Unique id for this intent/snippet pair|
|prob|float64|Probability given by the mining model|
### Data Splits
There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.
## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original [paper](https://arxiv.org/pdf/1805.08949.pdf)
### Citation Information
```
@inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
}
``` |
CyberHarem/nanna_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nanna (Fire Emblem)
This is the dataset of nanna (Fire Emblem), containing 82 images and their tags.
The core tags of this character are `blonde_hair, short_hair, green_eyes, hair_ornament`, 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 | 82 | 88.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanna_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 82 | 55.89 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanna_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 168 | 106.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanna_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 82 | 80.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanna_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 168 | 144.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nanna_fireemblem/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/nanna_fireemblem',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, solo, cape, boots, breastplate, pauldrons, bangs, holding_sword, simple_background, white_gloves, elbow_gloves, full_body, smile, white_background, blue_eyes, looking_at_viewer, open_mouth, pink_dress, black_thighhighs, earrings, pelvic_curtain, wing_hair_ornament |
| 1 | 10 |  |  |  |  |  | hetero, open_mouth, penis, 1girl, sex, vaginal, 1boy, medium_breasts, nipples, solo_focus, blush, mosaic_censoring, cape, cum_in_pussy, jewelry, navel, nude, shoulder_armor, sweat, gloves, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | cape | boots | breastplate | pauldrons | bangs | holding_sword | simple_background | white_gloves | elbow_gloves | full_body | smile | white_background | blue_eyes | looking_at_viewer | open_mouth | pink_dress | black_thighhighs | earrings | pelvic_curtain | wing_hair_ornament | hetero | penis | sex | vaginal | 1boy | medium_breasts | nipples | solo_focus | blush | mosaic_censoring | cum_in_pussy | jewelry | navel | nude | shoulder_armor | sweat | gloves | spread_legs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------|:--------|:--------------|:------------|:--------|:----------------|:--------------------|:---------------|:---------------|:------------|:--------|:-------------------|:------------|:--------------------|:-------------|:-------------|:-------------------|:-----------|:-----------------|:---------------------|:---------|:--------|:------|:----------|:-------|:-----------------|:----------|:-------------|:--------|:-------------------|:---------------|:----------|:--------|:-------|:-----------------|:--------|:---------|:--------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | X | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
MesutUnutur/guanaco-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966692
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/vanguard_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of vanguard/ヴァンガード/前卫 (Azur Lane)
This is the dataset of vanguard/ヴァンガード/前卫 (Azur Lane), containing 53 images and their tags.
The core tags of this character are `long_hair, blonde_hair, blue_eyes, breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 53 | 89.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vanguard_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 53 | 44.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vanguard_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 122 | 89.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vanguard_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 53 | 74.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vanguard_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 122 | 132.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/vanguard_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/vanguard_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, black_gloves, looking_at_viewer, solo, white_dress, simple_background, upper_body, white_background, braid, closed_mouth, medium_breasts, official_alternate_costume, smile |
| 1 | 5 |  |  |  |  |  | 1girl, black_gloves, holding_sword, solo, white_cape, white_dress, white_thighhighs, black_footwear, closed_mouth, full_body, high_heels, long_sleeves, looking_at_viewer, smile, hair_ribbon, rigging, sheath, turret, aiguillette, black_choker, rapier |
| 2 | 11 |  |  |  |  |  | 1girl, black_gloves, looking_at_viewer, official_alternate_costume, solo, white_dress, sleeveless_dress, black_pantyhose, high_heels, sitting, black_footwear, elbow_gloves, full_body, key, white_background, white_flower, brown_pantyhose, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | looking_at_viewer | solo | white_dress | simple_background | upper_body | white_background | braid | closed_mouth | medium_breasts | official_alternate_costume | smile | holding_sword | white_cape | white_thighhighs | black_footwear | full_body | high_heels | long_sleeves | hair_ribbon | rigging | sheath | turret | aiguillette | black_choker | rapier | sleeveless_dress | black_pantyhose | sitting | elbow_gloves | key | white_flower | brown_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:--------------------|:-------|:--------------|:--------------------|:-------------|:-------------------|:--------|:---------------|:-----------------|:-----------------------------|:--------|:----------------|:-------------|:-------------------|:-----------------|:------------|:-------------|:---------------|:--------------|:----------|:---------|:---------|:--------------|:---------------|:---------|:-------------------|:------------------|:----------|:---------------|:------|:---------------|:------------------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | X | X | X | | X | | | | X | | | | | X | X | X | | | | | | | | | X | X | X | X | X | X | X |
|
irds/mmarco_v2_dt_dev | ---
pretty_name: '`mmarco/v2/dt/dev`'
viewer: false
source_datasets: ['irds/mmarco_v2_dt']
task_categories:
- text-retrieval
---
# Dataset Card for `mmarco/v2/dt/dev`
The `mmarco/v2/dt/dev` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/v2/dt/dev).
# Data
This dataset provides:
- `queries` (i.e., topics); count=101,093
- `qrels`: (relevance assessments); count=59,273
- For `docs`, use [`irds/mmarco_v2_dt`](https://huggingface.co/datasets/irds/mmarco_v2_dt)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/mmarco_v2_dt_dev', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/mmarco_v2_dt_dev', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
|
LeoTungAnh/kdd210_hourly_336 | ---
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: feat_static_cat
sequence: uint64
- name: feat_dynamic_real
sequence:
sequence: float32
- name: item_id
dtype: string
- name: target
sequence: float64
splits:
- name: train
num_bytes: 17187159
num_examples: 210
- name: validation
num_bytes: 17751639
num_examples: 210
- name: test
num_bytes: 18316119
num_examples: 210
download_size: 46384794
dataset_size: 53254917
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "kdd210_hourly_336"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/2f525ab2 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 242
num_examples: 10
download_size: 1429
dataset_size: 242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2f525ab2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
OswaldHe123/novel-text | ---
license: mit
---
|
AviAwasthi/TeslaCarPrices | ---
license: mit
---
|
dariolopez/Llama-2-oasst1-es | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4524060
num_examples: 3909
download_size: 2528456
dataset_size: 4524060
license: apache-2.0
language:
- es
size_categories:
- 1K<n<10K
---
# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2
## Dataset Summary
Subset of the original [OpenAssistant Conversations Dataset (OASST)](https://huggingface.co/datasets/OpenAssistant/oasst1).
* Filtered by `lang=es`.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
* Select the best ranked output (Some instructions had multiple outputs ranked by humans).
* Select only the first level of the tree conversation.
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs). |
batterydata/cner | ---
language:
- en
license:
- apache-2.0
task_categories:
- token-classification
pretty_name: 'Chemical Named Entity Recognition (CNER) Dataset for BatteryDataExtractor'
---
# CNER Dataset
## Original Data Source
#### CHEMDNER
M. Krallinger, O. Rabal, F. Leitner, M. Vazquez, D. Salgado,
Z. Lu, R. Leaman, Y. Lu, D. Ji, D. M. Lowe et al., J. Cheminf.,
2015, 7, 1–17.
#### MatScholar
I. Weston, V. Tshitoyan, J. Dagdelen, O. Kononova, A. Tre-
wartha, K. A. Persson, G. Ceder and A. Jain, J. Chem. Inf.
Model., 2019, 59, 3692–3702.
#### SOFC
A. Friedrich, H. Adel, F. Tomazic, J. Hingerl, R. Benteau,
A. Maruscyk and L. Lange, The SOFC-exp corpus and neural
approaches to information extraction in the materials science
domain, 2020, https://arxiv.org/abs/2006.03039.
#### BioNLP
G. Crichton, S. Pyysalo, B. Chiu and A. Korhonen, BMC Bioinf.,
2017, 18, 1–14.
## Citation
BatteryDataExtractor: battery-aware text-mining software embedded with BERT models |
whatafok/lul | ---
license: other
---
|
gardner/nz_legislation | ---
license: other
language:
- en
pretty_name: NZ Legislation
size_categories:
- 1K<n<10K
---
## Overview
This is an initial version of public acts collected from legislation.govt.nz. The preamble sections of the acts have been excluded from this dataset.
Feedback is welcome: gardner@bickford.nz
The data is in `jsonl` format and each line contains:
```json
{
"id": "DLM415522",
"year": "1974",
"title": "Ngarimu VC and 28th (Maori) Battalion Memorial Scholarship Fund Amendment Act 1974",
"text": "1: Short Title\nThis Act may be cited as the Ngarimu VC and 28th (Maori) Battalion Memorial Scholarship Fund Amendment Act 1974, and shall be read together with and deemed part of the Ngarimu VC and 28th (Maori) Battalion Memorial Scholarship Fund Act 1945\n2:\n3:\n4: New sections substituted\n1: This subsection substituted section 14 section 15\n2: Notwithstanding anything in subsection (1) subsection (1)\n3: Notwithstanding anything in section 15 subsection (1)"
}
```
## Reproduction
The code to reproduce this dataset can be found at https://github.com/gardner/nz_legislation
## Copyright
The legislation text data in this dataset repository has **no copyright**.
From the Legislation.govt.nz [website](https://legislation.govt.nz/about.aspx#copyright):
> There is no copyright in New Zealand Acts, Bills, or the secondary legislation published on this website (see [section 27 of the Copyright Act 1994](https://legislation.govt.nz/act/public/1994/0143/latest/DLM345939.html)). All Acts, Bills, Supplementary Order Papers, and secondary legislation published on this website may be reproduced free of charge in any format or media without requiring specific permission.
|
aintech/vdf_qdrant-web-site-docs-2024-04-05 |
---
tags:
- vdf
- vector-io
- vector-dataset
- vector-embeddings
---
This is a dataset created using [vector-io](https://github.com/ai-northstar-tech/vector-io)
|
open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-timedial-unit-080082 | ---
pretty_name: Evaluation run of Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082)\
\ 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_Charlie911__vicuna-7b-v1.5-lora-timedial-unit-080082\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-23T18:02:30.843384](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-timedial-unit-080082/blob/main/results_2023-10-23T18-02-30.843384.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\": 0.004718959731543624,\n\
\ \"em_stderr\": 0.0007018360183131064,\n \"f1\": 0.06889890939597328,\n\
\ \"f1_stderr\": 0.0015900969200350048,\n \"acc\": 0.4076319447477909,\n\
\ \"acc_stderr\": 0.009880788504185114\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.004718959731543624,\n \"em_stderr\": 0.0007018360183131064,\n\
\ \"f1\": 0.06889890939597328,\n \"f1_stderr\": 0.0015900969200350048\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.07808946171341925,\n \
\ \"acc_stderr\": 0.007390654481108218\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7371744277821626,\n \"acc_stderr\": 0.012370922527262008\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|arc:challenge|25_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_23T18_02_30.843384
path:
- '**/details_harness|drop|3_2023-10-23T18-02-30.843384.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-23T18-02-30.843384.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_23T18_02_30.843384
path:
- '**/details_harness|gsm8k|5_2023-10-23T18-02-30.843384.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-23T18-02-30.843384.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hellaswag|10_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T17-45-25.017539.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-11T17-45-25.017539.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-11T17-45-25.017539.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_23T18_02_30.843384
path:
- '**/details_harness|winogrande|5_2023-10-23T18-02-30.843384.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-23T18-02-30.843384.parquet'
- config_name: results
data_files:
- split: 2023_10_11T17_45_25.017539
path:
- results_2023-10-11T17-45-25.017539.parquet
- split: 2023_10_23T18_02_30.843384
path:
- results_2023-10-23T18-02-30.843384.parquet
- split: latest
path:
- results_2023-10-23T18-02-30.843384.parquet
---
# Dataset Card for Evaluation run of Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082
- **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 [Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082](https://huggingface.co/Charlie911/vicuna-7b-v1.5-lora-timedial-unit-080082) 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_Charlie911__vicuna-7b-v1.5-lora-timedial-unit-080082",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T18:02:30.843384](https://huggingface.co/datasets/open-llm-leaderboard/details_Charlie911__vicuna-7b-v1.5-lora-timedial-unit-080082/blob/main/results_2023-10-23T18-02-30.843384.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": 0.004718959731543624,
"em_stderr": 0.0007018360183131064,
"f1": 0.06889890939597328,
"f1_stderr": 0.0015900969200350048,
"acc": 0.4076319447477909,
"acc_stderr": 0.009880788504185114
},
"harness|drop|3": {
"em": 0.004718959731543624,
"em_stderr": 0.0007018360183131064,
"f1": 0.06889890939597328,
"f1_stderr": 0.0015900969200350048
},
"harness|gsm8k|5": {
"acc": 0.07808946171341925,
"acc_stderr": 0.007390654481108218
},
"harness|winogrande|5": {
"acc": 0.7371744277821626,
"acc_stderr": 0.012370922527262008
}
}
```
### 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] |
armeet/faces-1600 | ---
license: unknown
---
|
Adipta/log-dall-e | ---
license: openrail
---
|
etan18/SampleMCDataset | ---
license: unknown
---
|
DavidMOBrien/8000-java-preprocessed-v2 | ---
dataset_info:
features:
- name: before
dtype: string
- name: after
dtype: string
- name: repo
dtype: string
- name: type
dtype: string
- name: __index_level_0__
dtype: int64
splits:
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num_bytes: 556419873
num_examples: 322448
- name: test
num_bytes: 76892752
num_examples: 44883
- name: valid
num_bytes: 73527268
num_examples: 45083
download_size: 292278962
dataset_size: 706839893
---
# Dataset Card for "8000-java-preprocessed-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
asan2707/KLXM_Person | ---
license: other
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_24_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 954
num_examples: 32
download_size: 2023
dataset_size: 954
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_24_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
james-burton/OrientalMuseum_min6-name | ---
dataset_info:
features:
- name: obj_num
dtype: string
- name: file
dtype: string
- name: image
dtype: image
- name: root
dtype: string
- name: description
dtype: string
- name: label
dtype:
class_label:
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'1': Ajaeng Holder
'2': Album Painting
'3': Amulet Mould
'4': Animal Figurine
'5': Animal Mummy
'6': Animal bone
'7': Arm Guard
'8': Axe Head
'9': Axle-caps
'10': Ball
'11': Ballista Bolt
'12': Band
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'23': Cabinet
'24': Cannon
'25': Cap
'26': Carved stone
'27': Case
'28': Cash Box
'29': Chest
'30': Cigar Holder
'31': Clapper
'32': Clay pipe (smoking)
'33': Comb
'34': Cosmetic and Medical Equipment and Implements
'35': Cricket pot
'36': Cross-bow Lock
'37': Cup And Saucer
'38': Cup, Saucer
'39': Cushion Cover
'40': DVDs
'41': Dagger
'42': Dice Box
'43': Dice Shaker
'44': Disc
'45': Domestic Equipment and Utensils
'46': Double Dagger
'47': Ear Protector
'48': Ear Stud
'49': Earring
'50': Erotic Figurine
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'53': Finger Ring
'54': Funerary Cone
'55': Funerary goods
'56': Funerary money
'57': Hand Jade
'58': Hand Protector
'59': Handwarmer
'60': Hanging
'61': Heart Scarab
'62': Human Figurine
'63': Incense Holder
'64': Inkstick
'65': Kite
'66': Knee Protector
'67': Kohl Pot
'68': Letter
'69': Lock
'70': Majiang set
'71': Manuscript Page
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'73': Mica Painting
'74': Miniature Painting
'75': Miniature Portrait
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'77': Mould
'78': Mouth Jade
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'80': Mouth-piece
'81': Mummy Label
'82': Nail Protector
'83': Nose Protector
'84': Oracle Bone
'85': Ostraka
'86': Palette
'87': Panel
'88': Part
'89': Pelmet
'90': Pencase
'91': Pendant
'92': Perfumer
'93': Phylactery
'94': Pigstick
'95': Pipe
'96': Pipe Case
'97': Pipe Holder
'98': Pith Painting
'99': Plaque
'100': Plate
'101': Poh Kam
'102': Prayer Wheel
'103': Rank Square
'104': Rubber
'105': Sake Cup
'106': Scabbard Chape
'107': Scabbard Slide
'108': Scarab Seal
'109': Scarf
'110': Score Board
'111': Screen
'112': Seal
'113': Seal Paste Pot
'114': Shield
'115': Shroud Weight
'116': Sleeve Band
'117': Sleeve Weight
'118': Slide
'119': Soles
'120': Spillikins
'121': Staff Head
'122': Stamp
'123': Stand
'124': Stand of Incense Burner
'125': Stem Bowl
'126': Stem Cup
'127': Story Cloth
'128': Sword Guard
'129': Table
'130': Table Runner
'131': Thangka
'132': Tomb Figure
'133': Tomb Model
'134': Washer
'135': Water Dropper
'136': Water Pot
'137': Wine Pot
'138': Woodblock Print
'139': Writing Desk
'140': accessories
'141': adzes
'142': albums
'143': altar components
'144': amphorae
'145': amulets
'146': anchors
'147': animation cels
'148': animation drawings
'149': anklets
'150': armbands
'151': armor
'152': armrests
'153': arrowheads
'154': arrows
'155': autograph albums
'156': axes
'157': 'axes: woodworking tools'
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'162': bangles
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'171': blades
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'173': boilers
'174': booklets
'175': books
'176': bottles
'177': bowls
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'179': bracelets
'180': bread
'181': brick
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'183': brush washers
'184': brushes
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'186': buckles
'187': business cards
'188': caddies
'189': calligraphy
'190': candelabras
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'193': canopic jars
'194': card cases
'195': cards
'196': carvings
'197': cases
'198': celestial globes
'199': censers
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'202': charms
'203': charts
'204': chess sets
'205': chessmen
'206': chisels
'207': chopsticks
'208': cigarette cases
'209': cigarette holders
'210': cippi
'211': claypipe
'212': cloth
'213': clothing
'214': coats
'215': coffins
'216': coins
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'218': compact discs
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'221': covers
'222': cuffs
'223': cups
'224': deels
'225': deity figurine
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'230': dolls
'231': doors
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'234': drums
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'236': earrings
'237': embroidery
'238': ensembles
'239': envelopes
'240': 'equipment for personal use: grooming, hygiene and health care'
'241': ewers
'242': fans
'243': female figurine
'244': fiddles
'245': figures
'246': figurines
'247': finials
'248': flagons
'249': flags
'250': flasks
'251': fragments
'252': furniture components
'253': gameboards
'254': gaming counters
'255': ge
'256': glassware
'257': gongs
'258': gowns
'259': greeting cards
'260': hair ornaments
'261': hairpins
'262': handles
'263': handscrolls
'264': harnesses
'265': hats
'266': headdresses
'267': headrests
'268': heads
'269': headscarves
'270': hobs
'271': houses
'272': illuminated manuscripts
'273': incense burners
'274': incense sticks
'275': ink bottles
'276': inkstands
'277': inkstones
'278': inkwells
'279': inlays
'280': jackets
'281': jar seal
'282': jars
'283': jewelry
'284': juglets
'285': jugs
'286': keys
'287': kimonos
'288': knives
'289': ladles
'290': lamps
'291': lanterns
'292': lanyards
'293': lids
'294': maces
'295': manuscripts
'296': maps
'297': masks
'298': medals
'299': miniatures
'300': mirrors
'301': models
'302': money
'303': mounts
'304': mugs
'305': mummies
'306': musical instruments
'307': nails
'308': necklaces
'309': needles
'310': netsukes
'311': nozzles
'312': obelisks
'313': oil lamps
'314': ornaments
'315': pages
'316': paintings
'317': paper money
'318': paperweights
'319': papyrus
'320': pectorals
'321': pendants
'322': pestles
'323': petticoats
'324': photograph albums
'325': photographs
'326': pictures
'327': pins
'328': pipes
'329': playing card boxes
'330': playing cards
'331': plumb bobs
'332': plume holders
'333': poker
'334': postage stamps
'335': postcards
'336': posters
'337': pots
'338': pottery
'339': prayers
'340': printing blocks
'341': printing plates
'342': prints
'343': punch bowls
'344': puppets
'345': purses
'346': puzzles
'347': quilts
'348': razors
'349': reliefs
'350': rifles
'351': rings
'352': robes
'353': roofing tile
'354': rose bowls
'355': rubbings
'356': rugs
'357': rulers
'358': sandals
'359': saris
'360': sarongs
'361': sashes
'362': saucers
'363': scabbards
'364': scaraboids
'365': scarabs
'366': scepters
'367': scissors
'368': scrolls
'369': sculpture
'370': seed
'371': seppa
'372': shadow puppets
'373': shawls
'374': shears
'375': shell
'376': sherds
'377': shields
'378': shoes
'379': shrines
'380': sistra
'381': situlae
'382': sketches
'383': skewers
'384': skirts
'385': snuff bottles
'386': socks
'387': spatulas
'388': spearheads
'389': spears
'390': spittoons
'391': spoons
'392': statues
'393': statuettes
'394': steelyards
'395': stelae
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'397': stirrup jars
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'399': stoppers
'400': straps
'401': studs
'402': swords
'403': tablets
'404': tacks
'405': talismans
'406': tallies
'407': tangrams
'408': tankards
'409': tea bowls
'410': tea caddies
'411': tea kettles
'412': teacups
'413': teapots
'414': telephones
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'416': tiles
'417': toggles
'418': toilet caskets
'419': tools
'420': toys
'421': trays
'422': trophies
'423': trousers
'424': tubes
'425': tureens
'426': tweezers
'427': typewriters
'428': underwear
'429': unidentified
'430': urinals
'431': ushabti
'432': utensils
'433': vases
'434': vessels
'435': waistcoats
'436': watches
'437': weight
'438': weights
'439': whistles
'440': whorls
'441': wood blocks
'442': writing boards
- name: other_name
dtype: string
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dataset_size: 3862573021.698
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
zolak/twitter_dataset_50_1713141170 | ---
dataset_info:
features:
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dtype: string
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splits:
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num_examples: 636
download_size: 129076
dataset_size: 244974
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chagasclone/rogerio | ---
license: openrail
---
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-_fil_self_160m_bo2_100_kl_0.1_prm_160m_thr_0.1_seed_2 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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download_size: 698630987
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configs:
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data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
- split: epoch_15
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
- split: epoch_16
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
ashwaninbs/reuters_articles | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 13792576
num_examples: 17262
- name: validation
num_bytes: 1870389
num_examples: 2158
- name: test
num_bytes: 1379190
num_examples: 2158
download_size: 10073411
dataset_size: 17042155
---
# Dataset Card for "reuters_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Oysiyl/google-android-toy | ---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 854639.0
num_examples: 15
download_size: 855753
dataset_size: 854639.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Q-bert__Terminis-7B | ---
pretty_name: Evaluation run of Q-bert/Terminis-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Q-bert/Terminis-7B](https://huggingface.co/Q-bert/Terminis-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_Q-bert__Terminis-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T14:00:12.819562](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Terminis-7B/blob/main/results_2023-12-13T14-00-12.819562.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\": 0.6432896842220583,\n\
\ \"acc_stderr\": 0.03235458873777211,\n \"acc_norm\": 0.6451073101562141,\n\
\ \"acc_norm_stderr\": 0.03300952286826437,\n \"mc1\": 0.5214198286413708,\n\
\ \"mc1_stderr\": 0.01748743214471164,\n \"mc2\": 0.6731465711305621,\n\
\ \"mc2_stderr\": 0.015142056894568223\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6518771331058021,\n \"acc_stderr\": 0.01392100859517935,\n\
\ \"acc_norm\": 0.6791808873720137,\n \"acc_norm_stderr\": 0.013640943091946531\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6804421429994025,\n\
\ \"acc_stderr\": 0.004653523038369371,\n \"acc_norm\": 0.8621788488348935,\n\
\ \"acc_norm_stderr\": 0.003440076775300575\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998905,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998905\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\": 0.7358490566037735,\n \"acc_stderr\": 0.02713429162874171,\n\
\ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.02713429162874171\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\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\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n\
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416906\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\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.593103448275862,\n \"acc_stderr\": 0.04093793981266236,\n\
\ \"acc_norm\": 0.593103448275862,\n \"acc_norm_stderr\": 0.04093793981266236\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3783068783068783,\n \"acc_stderr\": 0.024976954053155247,\n \"\
acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.024976954053155247\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\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5467980295566502,\n \"acc_stderr\": 0.03502544650845872,\n\
\ \"acc_norm\": 0.5467980295566502,\n \"acc_norm_stderr\": 0.03502544650845872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494563,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494563\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.024121125416941197,\n\
\ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.024121125416941197\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.028972648884844267,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.028972648884844267\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7226890756302521,\n \"acc_stderr\": 0.029079374539480007,\n\
\ \"acc_norm\": 0.7226890756302521,\n \"acc_norm_stderr\": 0.029079374539480007\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163265,\n \"\
acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163265\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069425,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069425\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\": 0.7557251908396947,\n \"acc_stderr\": 0.03768335959728744,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.03768335959728744\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934724,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934724\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165616\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\
\ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\
\ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7023121387283237,\n \"acc_stderr\": 0.024617055388677003,\n\
\ \"acc_norm\": 0.7023121387283237,\n \"acc_norm_stderr\": 0.024617055388677003\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4692737430167598,\n\
\ \"acc_stderr\": 0.016690896161944385,\n \"acc_norm\": 0.4692737430167598,\n\
\ \"acc_norm_stderr\": 0.016690896161944385\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n\
\ \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.02592237178881877,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.02592237178881877\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.025407197798890155,\n\
\ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.025407197798890155\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n\
\ \"acc_stderr\": 0.012733671880342506,\n \"acc_norm\": 0.4621903520208605,\n\
\ \"acc_norm_stderr\": 0.012733671880342506\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.02928941340940319,\n\
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.02928941340940319\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.630718954248366,\n \"acc_stderr\": 0.01952431674486635,\n \
\ \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.01952431674486635\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.02879518557429129,\n\
\ \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.02879518557429129\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\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\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5214198286413708,\n\
\ \"mc1_stderr\": 0.01748743214471164,\n \"mc2\": 0.6731465711305621,\n\
\ \"mc2_stderr\": 0.015142056894568223\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8129439621152328,\n \"acc_stderr\": 0.010959716435242912\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5754359363153905,\n \
\ \"acc_stderr\": 0.013614835574956378\n }\n}\n```"
repo_url: https://huggingface.co/Q-bert/Terminis-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_12_13T14_00_12.819562
path:
- '**/details_harness|arc:challenge|25_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|gsm8k|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hellaswag|10_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T14-00-12.819562.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T14-00-12.819562.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- '**/details_harness|winogrande|5_2023-12-13T14-00-12.819562.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T14-00-12.819562.parquet'
- config_name: results
data_files:
- split: 2023_12_13T14_00_12.819562
path:
- results_2023-12-13T14-00-12.819562.parquet
- split: latest
path:
- results_2023-12-13T14-00-12.819562.parquet
---
# Dataset Card for Evaluation run of Q-bert/Terminis-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Q-bert/Terminis-7B](https://huggingface.co/Q-bert/Terminis-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_Q-bert__Terminis-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T14:00:12.819562](https://huggingface.co/datasets/open-llm-leaderboard/details_Q-bert__Terminis-7B/blob/main/results_2023-12-13T14-00-12.819562.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": 0.6432896842220583,
"acc_stderr": 0.03235458873777211,
"acc_norm": 0.6451073101562141,
"acc_norm_stderr": 0.03300952286826437,
"mc1": 0.5214198286413708,
"mc1_stderr": 0.01748743214471164,
"mc2": 0.6731465711305621,
"mc2_stderr": 0.015142056894568223
},
"harness|arc:challenge|25": {
"acc": 0.6518771331058021,
"acc_stderr": 0.01392100859517935,
"acc_norm": 0.6791808873720137,
"acc_norm_stderr": 0.013640943091946531
},
"harness|hellaswag|10": {
"acc": 0.6804421429994025,
"acc_stderr": 0.004653523038369371,
"acc_norm": 0.8621788488348935,
"acc_norm_stderr": 0.003440076775300575
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998905,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998905
},
"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": 0.7358490566037735,
"acc_stderr": 0.02713429162874171,
"acc_norm": 0.7358490566037735,
"acc_norm_stderr": 0.02713429162874171
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"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": 0.05009082659620333,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416906,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416906
},
"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": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5872340425531914,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.5872340425531914,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.593103448275862,
"acc_stderr": 0.04093793981266236,
"acc_norm": 0.593103448275862,
"acc_norm_stderr": 0.04093793981266236
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3783068783068783,
"acc_stderr": 0.024976954053155247,
"acc_norm": 0.3783068783068783,
"acc_norm_stderr": 0.024976954053155247
},
"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": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.023415293433568525,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.023415293433568525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5467980295566502,
"acc_stderr": 0.03502544650845872,
"acc_norm": 0.5467980295566502,
"acc_norm_stderr": 0.03502544650845872
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009181,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.02937661648494563,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.02937661648494563
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6538461538461539,
"acc_stderr": 0.024121125416941197,
"acc_norm": 0.6538461538461539,
"acc_norm_stderr": 0.024121125416941197
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.028972648884844267,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.028972648884844267
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7226890756302521,
"acc_stderr": 0.029079374539480007,
"acc_norm": 0.7226890756302521,
"acc_norm_stderr": 0.029079374539480007
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
"acc_stderr": 0.015776239256163265,
"acc_norm": 0.8385321100917431,
"acc_norm_stderr": 0.015776239256163265
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5231481481481481,
"acc_stderr": 0.03406315360711507,
"acc_norm": 0.5231481481481481,
"acc_norm_stderr": 0.03406315360711507
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8235294117647058,
"acc_stderr": 0.02675640153807897,
"acc_norm": 0.8235294117647058,
"acc_norm_stderr": 0.02675640153807897
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7721518987341772,
"acc_stderr": 0.027303484599069425,
"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.027303484599069425
},
"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": 0.7557251908396947,
"acc_stderr": 0.03768335959728744,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.03768335959728744
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.04236511258094633,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.04236511258094633
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.03192193448934724,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.03192193448934724
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.7378640776699029,
"acc_stderr": 0.04354631077260595,
"acc_norm": 0.7378640776699029,
"acc_norm_stderr": 0.04354631077260595
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165616,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8084291187739464,
"acc_stderr": 0.014072859310451949,
"acc_norm": 0.8084291187739464,
"acc_norm_stderr": 0.014072859310451949
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7023121387283237,
"acc_stderr": 0.024617055388677003,
"acc_norm": 0.7023121387283237,
"acc_norm_stderr": 0.024617055388677003
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4692737430167598,
"acc_stderr": 0.016690896161944385,
"acc_norm": 0.4692737430167598,
"acc_norm_stderr": 0.016690896161944385
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7156862745098039,
"acc_stderr": 0.02582916327275748,
"acc_norm": 0.7156862745098039,
"acc_norm_stderr": 0.02582916327275748
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.02592237178881877,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.02592237178881877
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.025407197798890155,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.025407197798890155
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4621903520208605,
"acc_stderr": 0.012733671880342506,
"acc_norm": 0.4621903520208605,
"acc_norm_stderr": 0.012733671880342506
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6323529411764706,
"acc_stderr": 0.02928941340940319,
"acc_norm": 0.6323529411764706,
"acc_norm_stderr": 0.02928941340940319
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.630718954248366,
"acc_stderr": 0.01952431674486635,
"acc_norm": 0.630718954248366,
"acc_norm_stderr": 0.01952431674486635
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.0449429086625209,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.0449429086625209
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7183673469387755,
"acc_stderr": 0.02879518557429129,
"acc_norm": 0.7183673469387755,
"acc_norm_stderr": 0.02879518557429129
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.025196929874827072,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827072
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"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": 0.847953216374269,
"acc_stderr": 0.027539122889061452,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061452
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5214198286413708,
"mc1_stderr": 0.01748743214471164,
"mc2": 0.6731465711305621,
"mc2_stderr": 0.015142056894568223
},
"harness|winogrande|5": {
"acc": 0.8129439621152328,
"acc_stderr": 0.010959716435242912
},
"harness|gsm8k|5": {
"acc": 0.5754359363153905,
"acc_stderr": 0.013614835574956378
}
}
```
## 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:**
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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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OneFly7/llama2-SST2-double-end-token | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: label_text
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8587845
num_examples: 67349
- name: validation
num_bytes: 142004
num_examples: 872
download_size: 3308564
dataset_size: 8729849
---
# Dataset Card for "llama2-SST2-double-end-token"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_stsb_no_preverbal_negator | ---
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: 10858
num_examples: 52
- name: test
num_bytes: 5856
num_examples: 47
- name: train
num_bytes: 13232
num_examples: 82
download_size: 28447
dataset_size: 29946
---
# Dataset Card for "MULTI_VALUE_stsb_no_preverbal_negator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_clibrain__Llama-2-7b-ft-instruct-es | ---
pretty_name: Evaluation run of clibrain/Llama-2-7b-ft-instruct-es
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [clibrain/Llama-2-7b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es)\
\ 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_clibrain__Llama-2-7b-ft-instruct-es\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T14:05:09.748904](https://huggingface.co/datasets/open-llm-leaderboard/details_clibrain__Llama-2-7b-ft-instruct-es/blob/main/results_2023-09-17T14-05-09.748904.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\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.00037786091964606556,\n \"f1\": 0.059617239932886215,\n\
\ \"f1_stderr\": 0.0013507073733013888,\n \"acc\": 0.4045158699907191,\n\
\ \"acc_stderr\": 0.009256588130982506\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964606556,\n\
\ \"f1\": 0.059617239932886215,\n \"f1_stderr\": 0.0013507073733013888\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05686125852918878,\n \
\ \"acc_stderr\": 0.006378790242099664\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.01213438601986535\n\
\ }\n}\n```"
repo_url: https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es
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_09T22_51_22.839971
path:
- '**/details_harness|arc:challenge|25_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T14_05_09.748904
path:
- '**/details_harness|drop|3_2023-09-17T14-05-09.748904.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T14-05-09.748904.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T14_05_09.748904
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-05-09.748904.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T14-05-09.748904.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hellaswag|10_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T22:51:22.839971.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T22:51:22.839971.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T22:51:22.839971.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T14_05_09.748904
path:
- '**/details_harness|winogrande|5_2023-09-17T14-05-09.748904.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T14-05-09.748904.parquet'
- config_name: results
data_files:
- split: 2023_08_09T22_51_22.839971
path:
- results_2023-08-09T22:51:22.839971.parquet
- split: 2023_09_17T14_05_09.748904
path:
- results_2023-09-17T14-05-09.748904.parquet
- split: latest
path:
- results_2023-09-17T14-05-09.748904.parquet
---
# Dataset Card for Evaluation run of clibrain/Llama-2-7b-ft-instruct-es
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es
- **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 [clibrain/Llama-2-7b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-7b-ft-instruct-es) 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_clibrain__Llama-2-7b-ft-instruct-es",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T14:05:09.748904](https://huggingface.co/datasets/open-llm-leaderboard/details_clibrain__Llama-2-7b-ft-instruct-es/blob/main/results_2023-09-17T14-05-09.748904.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": 0.001363255033557047,
"em_stderr": 0.00037786091964606556,
"f1": 0.059617239932886215,
"f1_stderr": 0.0013507073733013888,
"acc": 0.4045158699907191,
"acc_stderr": 0.009256588130982506
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964606556,
"f1": 0.059617239932886215,
"f1_stderr": 0.0013507073733013888
},
"harness|gsm8k|5": {
"acc": 0.05686125852918878,
"acc_stderr": 0.006378790242099664
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.01213438601986535
}
}
```
### 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] |
michaelnath/annotated-code-functions-base | ---
dataset_info:
features:
- name: function
dtype: string
- name: repo_name
dtype: string
- name: features
sequence: float64
splits:
- name: train
num_bytes: 15982023
num_examples: 28383
download_size: 5257318
dataset_size: 15982023
---
# Dataset Card for "annotated-code-functions-base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
methodidacte/penguins | ---
license: unknown
---
Source : https://github.com/allisonhorst/palmerpenguins
Data originally published in :
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081. https://doi.org/10.1371/journal.pone.0090081 |
CyberHarem/quetzalcoatl_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of quetzalcoatl/ケツァル・コアトル/魁札尔·科亚特尔 (Fate/Grand Order)
This is the dataset of quetzalcoatl/ケツァル・コアトル/魁札尔·科亚特尔 (Fate/Grand Order), containing 338 images and their tags.
The core tags of this character are `long_hair, blonde_hair, green_eyes, breasts, large_breasts, headdress, headband`, 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 | 338 | 478.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/quetzalcoatl_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 338 | 422.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/quetzalcoatl_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 758 | 791.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/quetzalcoatl_fgo/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/quetzalcoatl_fgo',
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 | 6 |  |  |  |  |  | 1girl, poncho, smile, solo, collarbone, looking_at_viewer, open_mouth, blush, upper_body, necklace, simple_background |
| 1 | 5 |  |  |  |  |  | 1girl, bracer, cleavage, looking_at_viewer, poncho, smile, solo, ;d, blush, medium_breasts, one_eye_closed, open_mouth, feathers, midriff, navel, necklace, hair_ornament, hand_on_own_hip, teeth, twitter_username |
| 2 | 5 |  |  |  |  |  | 1girl, grin, looking_at_viewer, necklace, solo, poncho, sharp_teeth, white_background, evil_smile, simple_background, upper_body |
| 3 | 5 |  |  |  |  |  | 1girl, bracer, skirt, solo, blue_cape, midriff, navel, open_mouth, poncho, looking_at_viewer, necklace, simple_background, white_background, :d, feathers |
| 4 | 6 |  |  |  |  |  | 1girl, bracer, hair_beads, hair_intakes, low-tied_long_hair, navel, neck_ring, poncho, red_skirt, solo, twitter_username, very_long_hair, white_background, blush, closed_mouth, feathers, looking_at_viewer, midriff, piercing, simple_background, smile, bead_necklace, blue_cape, full_body, green_nails, nail_polish, sandals, brown_footwear |
| 5 | 17 |  |  |  |  |  | 1girl, christmas, smile, solo, looking_at_viewer, bell, cleavage, open_mouth, fur_trim, navel, red_bikini, santa_bikini, one_eye_closed, blush, fur-trimmed_bikini, jewelry, ;d, feathers, very_long_hair |
| 6 | 13 |  |  |  |  |  | 1boy, 1girl, blush, hetero, sex, nipples, penis, vaginal, girl_on_top, navel, spread_legs, open_mouth, solo_focus, completely_nude, smile, sweat, abs, simple_background, thighs, uncensored, cowgirl_position, cum_in_pussy, huge_breasts, looking_at_viewer, muscular_female, pov, white_background |
| 7 | 6 |  |  |  |  |  | 1boy, 1girl, blush, dark-skinned_male, hetero, interracial, sweat, uncensored, cum, erection, open_mouth, teeth, ass, heart, necklace, nipples, pussy, solo_focus, testicles, veiny_penis, anus, completely_nude, large_penis, outdoors, parted_bangs, rolling_eyes, smile, tongue |
| 8 | 11 |  |  |  |  |  | 1girl, solo, looking_at_viewer, smile, blush, earrings, green_nails, nail_polish, turtleneck_sweater, ribbed_sweater, simple_background, sleeveless, upper_body, white_sweater, bracelet, holding_cup, white_background, bare_shoulders, hat, long_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | poncho | smile | solo | collarbone | looking_at_viewer | open_mouth | blush | upper_body | necklace | simple_background | bracer | cleavage | ;d | medium_breasts | one_eye_closed | feathers | midriff | navel | hair_ornament | hand_on_own_hip | teeth | twitter_username | grin | sharp_teeth | white_background | evil_smile | skirt | blue_cape | :d | hair_beads | hair_intakes | low-tied_long_hair | neck_ring | red_skirt | very_long_hair | closed_mouth | piercing | bead_necklace | full_body | green_nails | nail_polish | sandals | brown_footwear | christmas | bell | fur_trim | red_bikini | santa_bikini | fur-trimmed_bikini | jewelry | 1boy | hetero | sex | nipples | penis | vaginal | girl_on_top | spread_legs | solo_focus | completely_nude | sweat | abs | thighs | uncensored | cowgirl_position | cum_in_pussy | huge_breasts | muscular_female | pov | dark-skinned_male | interracial | cum | erection | ass | heart | pussy | testicles | veiny_penis | anus | large_penis | outdoors | parted_bangs | rolling_eyes | tongue | earrings | turtleneck_sweater | ribbed_sweater | sleeveless | white_sweater | bracelet | holding_cup | bare_shoulders | hat | long_sleeves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------|:-------|:-------------|:--------------------|:-------------|:--------|:-------------|:-----------|:--------------------|:---------|:-----------|:-----|:-----------------|:-----------------|:-----------|:----------|:--------|:----------------|:------------------|:--------|:-------------------|:-------|:--------------|:-------------------|:-------------|:--------|:------------|:-----|:-------------|:---------------|:---------------------|:------------|:------------|:-----------------|:---------------|:-----------|:----------------|:------------|:--------------|:--------------|:----------|:-----------------|:------------|:-------|:-----------|:-------------|:---------------|:---------------------|:----------|:-------|:---------|:------|:----------|:--------|:----------|:--------------|:--------------|:-------------|:------------------|:--------|:------|:---------|:-------------|:-------------------|:---------------|:---------------|:------------------|:------|:--------------------|:--------------|:------|:-----------|:------|:--------|:--------|:------------|:--------------|:-------|:--------------|:-----------|:---------------|:---------------|:---------|:-----------|:---------------------|:-----------------|:-------------|:----------------|:-----------|:--------------|:-----------------|:------|:---------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | | X | X | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | X | | X | | | X | X | X | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | | X | | X | X | | | X | X | X | | | | | X | X | X | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | X | | X | | X | | | X | X | | | | | X | X | X | | | | X | | | X | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 17 |  |  |  |  |  | X | | X | X | | X | X | X | | | | | X | X | | X | X | | X | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | 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 | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | | X | X | | X | | X | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
hojzas/proj8-label-validation | ---
license: apache-2.0
---
|
shidowake/oasst1-chat-ja-subset-from-kunishou_subset_split_1 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 5900409.475100942
num_examples: 3220
download_size: 3038948
dataset_size: 5900409.475100942
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ovior/twitter_dataset_1713077932 | ---
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: 2695575
num_examples: 8246
download_size: 1523315
dataset_size: 2695575
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
maghwa/OpenHermes-2-AR-10K-5 | ---
dataset_info:
features:
- name: skip_prompt_formatting
dtype: 'null'
- name: hash
dtype: 'null'
- name: language
dtype: 'null'
- name: system_prompt
dtype: 'null'
- name: views
dtype: float64
- name: conversations
dtype: string
- name: source
dtype: string
- name: topic
dtype: 'null'
- name: model_name
dtype: 'null'
- name: model
dtype: 'null'
- name: category
dtype: 'null'
- name: avatarUrl
dtype: 'null'
- name: title
dtype: 'null'
- name: custom_instruction
dtype: 'null'
- name: id
dtype: string
- name: idx
dtype: 'null'
splits:
- name: train
num_bytes: 20968753
num_examples: 10001
download_size: 7431232
dataset_size: 20968753
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mii-llm/lmsys-it | ---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: model
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: language
dtype: string
- name: openai_moderation
list:
- name: categories
struct:
- name: harassment
dtype: bool
- name: harassment/threatening
dtype: bool
- name: hate
dtype: bool
- name: hate/threatening
dtype: bool
- name: self-harm
dtype: bool
- name: self-harm/instructions
dtype: bool
- name: self-harm/intent
dtype: bool
- name: sexual
dtype: bool
- name: sexual/minors
dtype: bool
- name: violence
dtype: bool
- name: violence/graphic
dtype: bool
- name: category_scores
struct:
- name: harassment
dtype: float64
- name: harassment/threatening
dtype: float64
- name: hate
dtype: float64
- name: hate/threatening
dtype: float64
- name: self-harm
dtype: float64
- name: self-harm/instructions
dtype: float64
- name: self-harm/intent
dtype: float64
- name: sexual
dtype: float64
- name: sexual/minors
dtype: float64
- name: violence
dtype: float64
- name: violence/graphic
dtype: float64
- name: flagged
dtype: bool
- name: redacted
dtype: bool
splits:
- name: train
num_bytes: 37720979.53632
num_examples: 14362
download_size: 24887831
dataset_size: 37720979.53632
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MarianaMolina007/NASAART | ---
license: cc
---
|
liuyanchen1015/MULTI_VALUE_stsb_their_them | ---
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: 6524
num_examples: 33
- name: test
num_bytes: 2601
num_examples: 13
- name: train
num_bytes: 12823
num_examples: 61
download_size: 23967
dataset_size: 21948
---
# Dataset Card for "MULTI_VALUE_stsb_their_them"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ManoharEldhandi/indian_food_images | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': burger
'1': butter_naan
'2': chai
'3': chapati
'4': chole_bhature
'5': dal_makhani
'6': dhokla
'7': fried_rice
'8': idli
'9': jalebi
'10': kaathi_rolls
'11': kadai_paneer
'12': kulfi
'13': masala_dosa
'14': momos
'15': paani_puri
'16': pakode
'17': pav_bhaji
'18': pizza
'19': samosa
splits:
- name: train
num_bytes: 1200414082.0794334
num_examples: 5328
- name: test
num_bytes: 222276428.3925666
num_examples: 941
download_size: 1601712089
dataset_size: 1422690510.4720001
---
# Dataset Card for "indian_food_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dmitrijsk/rick | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: context_name
dtype: string
- name: context_name_line
dtype: string
- name: line
dtype: string
splits:
- name: train
num_bytes: 592370.4808013355
num_examples: 499
- name: validation
num_bytes: 59355.75959933222
num_examples: 50
- name: test
num_bytes: 59355.75959933222
num_examples: 50
download_size: 417787
dataset_size: 711082.0
---
# Dataset Card for "rick"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vtiyyal1/AskDocsEmpathy_2k_dpo | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 6052481.0
num_examples: 2124
download_size: 2405206
dataset_size: 6052481.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sounana/dataset | ---
dataset_info:
features:
- name: audio
struct:
- name: bytes
dtype: 'null'
- name: path
dtype: string
- name: sentence
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 11830499
num_examples: 49283
download_size: 2821300
dataset_size: 11830499
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
haiyan1/image | ---
license: apache-2.0
---
|
EleutherAI/pile-deduped-pythia-random-sampled | ---
dataset_info:
features:
- name: Index
dtype: int64
- name: 70M
dtype: float64
- name: 160M
dtype: float64
- name: 410M
dtype: float64
- name: 1B
dtype: float64
- name: 1.4B
dtype: float64
- name: 2.8B
dtype: float64
- name: 6.9B
dtype: float64
- name: 12B
dtype: float64
- name: Tokens
sequence: uint16
splits:
- name: train
num_bytes: 1020000000
num_examples: 5000000
download_size: 915854656
dataset_size: 1020000000
---
# Dataset Card for "pile-deduped-pythia-random-sampled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BangumiBase/rezero | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Re:zero
This is the image base of bangumi Re:Zero, we detected 92 characters, 9641 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 3392 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 111 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 52 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 54 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 22 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 34 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 63 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 38 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 59 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 25 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 33 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 41 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 135 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 20 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 78 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 96 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 36 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 24 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 27 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 72 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 18 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 19 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 8 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 20 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 38 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 44 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 10 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 116 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 41 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 25 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 17 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 151 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 24 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 75 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 26 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 40 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 71 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 279 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 715 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 41 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 58 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 49 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 87 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 20 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 20 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 596 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 14 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 31 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 24 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 379 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 10 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 7 | [Download](54/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 55 | 7 | [Download](55/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 56 | 54 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 20 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 215 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 13 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 12 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 37 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 47 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 18 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 327 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 44 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 155 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 17 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 13 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 39 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 92 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 53 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 28 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 85 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 75 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 28 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 11 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 14 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 7 | [Download](78/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 79 | 9 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 17 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 11 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 14 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 6 | [Download](83/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 84 | 20 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 20 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 16 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 26 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 190 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 5 | [Download](89/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 90 | 8 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 375 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
liuyanchen1015/MULTI_VALUE_rte_relativizer_doubling | ---
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: 344173
num_examples: 755
- name: train
num_bytes: 305139
num_examples: 660
download_size: 425091
dataset_size: 649312
---
# Dataset Card for "MULTI_VALUE_rte_relativizer_doubling"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
flozi00/conversations | ---
language:
- de
task_categories:
- conversational
- text-generation
dataset_info:
features:
- name: raw
dtype: string
- name: from
dtype: string
- name: labels
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: first_message
dtype: string
- name: first_answer
dtype: string
splits:
- name: train
num_bytes: 80567935.1091266
num_examples: 23275
download_size: 46600297
dataset_size: 80567935.1091266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This dataset is an uncensored and massively cleaned, double checked merge of several german datasets / subsets
The mission of this work is building an high quality dataset for the german llm community.
This repo is continously updated and old parts being replaced with never.
Quality for Quantity
https://github.com/flozi00/chat-data-experiments/blob/main/chat_combiner.py |
kowndinya23/bigbench_zero_shot | ---
dataset_info:
- config_name: abstract_narrative_understanding
features:
- name: idx
dtype: int32
- name: inputs
dtype: string
- name: targets
sequence: string
- name: multiple_choice_targets
sequence: string
- name: multiple_choice_scores
sequence: int32
splits:
- name: default
num_bytes: 6560069
num_examples: 3000
- name: train
num_bytes: 5249819
num_examples: 2400
- name: validation
num_bytes: 1310250
num_examples: 600
download_size: 1435670
dataset_size: 13120138
- config_name: anachronisms
features:
- name: idx
dtype: int32
- name: inputs
dtype: string
- name: targets
sequence: string
- name: multiple_choice_targets
sequence: string
- name: multiple_choice_scores
sequence: int32
splits:
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num_bytes: 48826
num_examples: 230
- name: train
num_bytes: 39116
num_examples: 184
- name: validation
num_bytes: 9710
num_examples: 46
download_size: 40931
dataset_size: 97652
- config_name: analogical_similarity
features:
- name: idx
dtype: int32
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download_size: 285403
dataset_size: 2747630
- config_name: analytic_entailment
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- config_name: arithmetic
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- config_name: ascii_word_recognition
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configs:
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data_files:
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path: abstract_narrative_understanding/default-*
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path: abstract_narrative_understanding/train-*
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path: abstract_narrative_understanding/validation-*
- config_name: anachronisms
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path: anachronisms/train-*
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path: anachronisms/validation-*
- config_name: analogical_similarity
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path: analogical_similarity/default-*
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path: analogical_similarity/train-*
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path: analogical_similarity/validation-*
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path: analytic_entailment/default-*
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path: analytic_entailment/train-*
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path: analytic_entailment/validation-*
- config_name: arithmetic
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path: arithmetic/default-*
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path: arithmetic/train-*
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path: arithmetic/validation-*
- config_name: ascii_word_recognition
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path: ascii_word_recognition/default-*
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path: ascii_word_recognition/train-*
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path: ascii_word_recognition/validation-*
- config_name: authorship_verification
data_files:
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path: authorship_verification/default-*
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path: authorship_verification/train-*
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path: authorship_verification/validation-*
- config_name: auto_categorization
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path: auto_categorization/default-*
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path: auto_categorization/train-*
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path: auto_categorization/validation-*
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path: auto_debugging/default-*
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path: auto_debugging/train-*
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path: auto_debugging/validation-*
- config_name: bbq_lite_json
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path: bbq_lite_json/default-*
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path: bbq_lite_json/train-*
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path: bbq_lite_json/validation-*
- config_name: bridging_anaphora_resolution_barqa
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path: bridging_anaphora_resolution_barqa/default-*
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path: bridging_anaphora_resolution_barqa/train-*
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path: bridging_anaphora_resolution_barqa/validation-*
- config_name: causal_judgment
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path: causal_judgment/default-*
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path: causal_judgment/train-*
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path: causal_judgment/validation-*
- config_name: cause_and_effect
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path: cause_and_effect/default-*
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path: cause_and_effect/train-*
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path: cause_and_effect/validation-*
- config_name: checkmate_in_one
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path: checkmate_in_one/default-*
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path: checkmate_in_one/train-*
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path: checkmate_in_one/validation-*
- config_name: chess_state_tracking
data_files:
- split: default
path: chess_state_tracking/default-*
- split: train
path: chess_state_tracking/train-*
- split: validation
path: chess_state_tracking/validation-*
- config_name: chinese_remainder_theorem
data_files:
- split: default
path: chinese_remainder_theorem/default-*
- split: train
path: chinese_remainder_theorem/train-*
- split: validation
path: chinese_remainder_theorem/validation-*
- config_name: cifar10_classification
data_files:
- split: default
path: cifar10_classification/default-*
- split: train
path: cifar10_classification/train-*
- split: validation
path: cifar10_classification/validation-*
- config_name: code_line_description
data_files:
- split: default
path: code_line_description/default-*
- split: train
path: code_line_description/train-*
- split: validation
path: code_line_description/validation-*
- config_name: codenames
data_files:
- split: default
path: codenames/default-*
- split: train
path: codenames/train-*
- split: validation
path: codenames/validation-*
- config_name: color
data_files:
- split: default
path: color/default-*
- split: train
path: color/train-*
- split: validation
path: color/validation-*
- config_name: common_morpheme
data_files:
- split: default
path: common_morpheme/default-*
- split: train
path: common_morpheme/train-*
- split: validation
path: common_morpheme/validation-*
- config_name: conceptual_combinations
data_files:
- split: default
path: conceptual_combinations/default-*
- split: train
path: conceptual_combinations/train-*
- split: validation
path: conceptual_combinations/validation-*
- config_name: conlang_translation
data_files:
- split: default
path: conlang_translation/default-*
- split: train
path: conlang_translation/train-*
- split: validation
path: conlang_translation/validation-*
- config_name: contextual_parametric_knowledge_conflicts
data_files:
- split: default
path: contextual_parametric_knowledge_conflicts/default-*
- split: train
path: contextual_parametric_knowledge_conflicts/train-*
- split: validation
path: contextual_parametric_knowledge_conflicts/validation-*
- config_name: crash_blossom
data_files:
- split: default
path: crash_blossom/default-*
- split: train
path: crash_blossom/train-*
- split: validation
path: crash_blossom/validation-*
- config_name: crass_ai
data_files:
- split: default
path: crass_ai/default-*
- split: train
path: crass_ai/train-*
- split: validation
path: crass_ai/validation-*
- config_name: cryobiology_spanish
data_files:
- split: default
path: cryobiology_spanish/default-*
- split: train
path: cryobiology_spanish/train-*
- split: validation
path: cryobiology_spanish/validation-*
- config_name: cryptonite
data_files:
- split: default
path: cryptonite/default-*
- split: train
path: cryptonite/train-*
- split: validation
path: cryptonite/validation-*
- config_name: cs_algorithms
data_files:
- split: default
path: cs_algorithms/default-*
- split: train
path: cs_algorithms/train-*
- split: validation
path: cs_algorithms/validation-*
- config_name: dark_humor_detection
data_files:
- split: default
path: dark_humor_detection/default-*
- split: train
path: dark_humor_detection/train-*
- split: validation
path: dark_humor_detection/validation-*
- config_name: date_understanding
data_files:
- split: default
path: date_understanding/default-*
- split: train
path: date_understanding/train-*
- split: validation
path: date_understanding/validation-*
- config_name: disambiguation_qa
data_files:
- split: default
path: disambiguation_qa/default-*
- split: train
path: disambiguation_qa/train-*
- split: validation
path: disambiguation_qa/validation-*
- config_name: discourse_marker_prediction
data_files:
- split: default
path: discourse_marker_prediction/default-*
- split: train
path: discourse_marker_prediction/train-*
- split: validation
path: discourse_marker_prediction/validation-*
- config_name: disfl_qa
data_files:
- split: default
path: disfl_qa/default-*
- split: train
path: disfl_qa/train-*
- split: validation
path: disfl_qa/validation-*
- config_name: dyck_languages
data_files:
- split: default
path: dyck_languages/default-*
- split: train
path: dyck_languages/train-*
- split: validation
path: dyck_languages/validation-*
- config_name: elementary_math_qa
data_files:
- split: default
path: elementary_math_qa/default-*
- split: train
path: elementary_math_qa/train-*
- split: validation
path: elementary_math_qa/validation-*
- config_name: emoji_movie
data_files:
- split: default
path: emoji_movie/default-*
- split: train
path: emoji_movie/train-*
- split: validation
path: emoji_movie/validation-*
- config_name: emojis_emotion_prediction
data_files:
- split: default
path: emojis_emotion_prediction/default-*
- split: train
path: emojis_emotion_prediction/train-*
- split: validation
path: emojis_emotion_prediction/validation-*
- config_name: empirical_judgments
data_files:
- split: default
path: empirical_judgments/default-*
- split: train
path: empirical_judgments/train-*
- split: validation
path: empirical_judgments/validation-*
- config_name: english_proverbs
data_files:
- split: default
path: english_proverbs/default-*
- split: train
path: english_proverbs/train-*
- split: validation
path: english_proverbs/validation-*
- config_name: english_russian_proverbs
data_files:
- split: default
path: english_russian_proverbs/default-*
- split: train
path: english_russian_proverbs/train-*
- split: validation
path: english_russian_proverbs/validation-*
- config_name: entailed_polarity
data_files:
- split: default
path: entailed_polarity/default-*
- split: train
path: entailed_polarity/train-*
- split: validation
path: entailed_polarity/validation-*
- config_name: entailed_polarity_hindi
data_files:
- split: default
path: entailed_polarity_hindi/default-*
- split: train
path: entailed_polarity_hindi/train-*
- split: validation
path: entailed_polarity_hindi/validation-*
- config_name: epistemic_reasoning
data_files:
- split: default
path: epistemic_reasoning/default-*
- split: train
path: epistemic_reasoning/train-*
- split: validation
path: epistemic_reasoning/validation-*
- config_name: evaluating_information_essentiality
data_files:
- split: default
path: evaluating_information_essentiality/default-*
- split: train
path: evaluating_information_essentiality/train-*
- split: validation
path: evaluating_information_essentiality/validation-*
- config_name: fact_checker
data_files:
- split: default
path: fact_checker/default-*
- split: train
path: fact_checker/train-*
- split: validation
path: fact_checker/validation-*
- config_name: fantasy_reasoning
data_files:
- split: default
path: fantasy_reasoning/default-*
- split: train
path: fantasy_reasoning/train-*
- split: validation
path: fantasy_reasoning/validation-*
- config_name: few_shot_nlg
data_files:
- split: default
path: few_shot_nlg/default-*
- split: train
path: few_shot_nlg/train-*
- split: validation
path: few_shot_nlg/validation-*
- config_name: figure_of_speech_detection
data_files:
- split: default
path: figure_of_speech_detection/default-*
- split: train
path: figure_of_speech_detection/train-*
- split: validation
path: figure_of_speech_detection/validation-*
- config_name: formal_fallacies_syllogisms_negation
data_files:
- split: default
path: formal_fallacies_syllogisms_negation/default-*
- split: train
path: formal_fallacies_syllogisms_negation/train-*
- split: validation
path: formal_fallacies_syllogisms_negation/validation-*
- config_name: gem
data_files:
- split: default
path: gem/default-*
- split: train
path: gem/train-*
- split: validation
path: gem/validation-*
- config_name: gender_inclusive_sentences_german
data_files:
- split: default
path: gender_inclusive_sentences_german/default-*
- split: train
path: gender_inclusive_sentences_german/train-*
- split: validation
path: gender_inclusive_sentences_german/validation-*
- config_name: general_knowledge
data_files:
- split: default
path: general_knowledge/default-*
- split: train
path: general_knowledge/train-*
- split: validation
path: general_knowledge/validation-*
- config_name: geometric_shapes
data_files:
- split: default
path: geometric_shapes/default-*
- split: train
path: geometric_shapes/train-*
- split: validation
path: geometric_shapes/validation-*
- config_name: goal_step_wikihow
data_files:
- split: default
path: goal_step_wikihow/default-*
- split: train
path: goal_step_wikihow/train-*
- split: validation
path: goal_step_wikihow/validation-*
- config_name: gre_reading_comprehension
data_files:
- split: default
path: gre_reading_comprehension/default-*
- split: train
path: gre_reading_comprehension/train-*
- split: validation
path: gre_reading_comprehension/validation-*
- config_name: hhh_alignment
data_files:
- split: default
path: hhh_alignment/default-*
- split: train
path: hhh_alignment/train-*
- split: validation
path: hhh_alignment/validation-*
- config_name: hindi_question_answering
data_files:
- split: default
path: hindi_question_answering/default-*
- split: train
path: hindi_question_answering/train-*
- split: validation
path: hindi_question_answering/validation-*
- config_name: hindu_knowledge
data_files:
- split: default
path: hindu_knowledge/default-*
- split: train
path: hindu_knowledge/train-*
- split: validation
path: hindu_knowledge/validation-*
- config_name: hinglish_toxicity
data_files:
- split: default
path: hinglish_toxicity/default-*
- split: train
path: hinglish_toxicity/train-*
- split: validation
path: hinglish_toxicity/validation-*
- config_name: human_organs_senses
data_files:
- split: default
path: human_organs_senses/default-*
- split: train
path: human_organs_senses/train-*
- split: validation
path: human_organs_senses/validation-*
- config_name: hyperbaton
data_files:
- split: default
path: hyperbaton/default-*
- split: train
path: hyperbaton/train-*
- split: validation
path: hyperbaton/validation-*
- config_name: identify_math_theorems
data_files:
- split: default
path: identify_math_theorems/default-*
- split: train
path: identify_math_theorems/train-*
- split: validation
path: identify_math_theorems/validation-*
- config_name: identify_odd_metaphor
data_files:
- split: default
path: identify_odd_metaphor/default-*
- split: train
path: identify_odd_metaphor/train-*
- split: validation
path: identify_odd_metaphor/validation-*
- config_name: implicatures
data_files:
- split: default
path: implicatures/default-*
- split: train
path: implicatures/train-*
- split: validation
path: implicatures/validation-*
- config_name: implicit_relations
data_files:
- split: default
path: implicit_relations/default-*
- split: train
path: implicit_relations/train-*
- split: validation
path: implicit_relations/validation-*
- config_name: indic_cause_and_effect
data_files:
- split: default
path: indic_cause_and_effect/default-*
- split: train
path: indic_cause_and_effect/train-*
- split: validation
path: indic_cause_and_effect/validation-*
- config_name: intent_recognition
data_files:
- split: default
path: intent_recognition/default-*
- split: train
path: intent_recognition/train-*
- split: validation
path: intent_recognition/validation-*
- config_name: international_phonetic_alphabet_nli
data_files:
- split: default
path: international_phonetic_alphabet_nli/default-*
- split: train
path: international_phonetic_alphabet_nli/train-*
- split: validation
path: international_phonetic_alphabet_nli/validation-*
- config_name: international_phonetic_alphabet_transliterate
data_files:
- split: default
path: international_phonetic_alphabet_transliterate/default-*
- split: train
path: international_phonetic_alphabet_transliterate/train-*
- split: validation
path: international_phonetic_alphabet_transliterate/validation-*
- config_name: intersect_geometry
data_files:
- split: default
path: intersect_geometry/default-*
- split: train
path: intersect_geometry/train-*
- split: validation
path: intersect_geometry/validation-*
- config_name: irony_identification
data_files:
- split: default
path: irony_identification/default-*
- split: train
path: irony_identification/train-*
- split: validation
path: irony_identification/validation-*
- config_name: kanji_ascii
data_files:
- split: default
path: kanji_ascii/default-*
- split: train
path: kanji_ascii/train-*
- split: validation
path: kanji_ascii/validation-*
- config_name: kannada
data_files:
- split: default
path: kannada/default-*
- split: train
path: kannada/train-*
- split: validation
path: kannada/validation-*
- config_name: key_value_maps
data_files:
- split: default
path: key_value_maps/default-*
- split: train
path: key_value_maps/train-*
- split: validation
path: key_value_maps/validation-*
- config_name: known_unknowns
data_files:
- split: default
path: known_unknowns/default-*
- split: train
path: known_unknowns/train-*
- split: validation
path: known_unknowns/validation-*
- config_name: language_games
data_files:
- split: default
path: language_games/default-*
- split: train
path: language_games/train-*
- split: validation
path: language_games/validation-*
- config_name: language_identification
data_files:
- split: default
path: language_identification/default-*
- split: train
path: language_identification/train-*
- split: validation
path: language_identification/validation-*
- config_name: linguistic_mappings
data_files:
- split: default
path: linguistic_mappings/default-*
- split: train
path: linguistic_mappings/train-*
- split: validation
path: linguistic_mappings/validation-*
- config_name: linguistics_puzzles
data_files:
- split: default
path: linguistics_puzzles/default-*
- split: train
path: linguistics_puzzles/train-*
- split: validation
path: linguistics_puzzles/validation-*
- config_name: logic_grid_puzzle
data_files:
- split: default
path: logic_grid_puzzle/default-*
- split: train
path: logic_grid_puzzle/train-*
- split: validation
path: logic_grid_puzzle/validation-*
- config_name: logical_args
data_files:
- split: default
path: logical_args/default-*
- split: train
path: logical_args/train-*
- split: validation
path: logical_args/validation-*
- config_name: logical_deduction
data_files:
- split: default
path: logical_deduction/default-*
- split: train
path: logical_deduction/train-*
- split: validation
path: logical_deduction/validation-*
- config_name: logical_fallacy_detection
data_files:
- split: default
path: logical_fallacy_detection/default-*
- split: train
path: logical_fallacy_detection/train-*
- split: validation
path: logical_fallacy_detection/validation-*
- config_name: logical_sequence
data_files:
- split: default
path: logical_sequence/default-*
- split: train
path: logical_sequence/train-*
- split: validation
path: logical_sequence/validation-*
- config_name: mathematical_induction
data_files:
- split: default
path: mathematical_induction/default-*
- split: train
path: mathematical_induction/train-*
- split: validation
path: mathematical_induction/validation-*
- config_name: matrixshapes
data_files:
- split: default
path: matrixshapes/default-*
- split: train
path: matrixshapes/train-*
- split: validation
path: matrixshapes/validation-*
- config_name: medical_questions_russian
data_files:
- split: default
path: medical_questions_russian/default-*
- split: train
path: medical_questions_russian/train-*
- split: validation
path: medical_questions_russian/validation-*
- config_name: metaphor_boolean
data_files:
- split: default
path: metaphor_boolean/default-*
- split: train
path: metaphor_boolean/train-*
- split: validation
path: metaphor_boolean/validation-*
- config_name: metaphor_understanding
data_files:
- split: default
path: metaphor_understanding/default-*
- split: train
path: metaphor_understanding/train-*
- split: validation
path: metaphor_understanding/validation-*
- config_name: minute_mysteries_qa
data_files:
- split: default
path: minute_mysteries_qa/default-*
- split: train
path: minute_mysteries_qa/train-*
- split: validation
path: minute_mysteries_qa/validation-*
- config_name: misconceptions
data_files:
- split: default
path: misconceptions/default-*
- split: train
path: misconceptions/train-*
- split: validation
path: misconceptions/validation-*
- config_name: misconceptions_russian
data_files:
- split: default
path: misconceptions_russian/default-*
- split: train
path: misconceptions_russian/train-*
- split: validation
path: misconceptions_russian/validation-*
- config_name: mnist_ascii
data_files:
- split: default
path: mnist_ascii/default-*
- split: train
path: mnist_ascii/train-*
- split: validation
path: mnist_ascii/validation-*
- config_name: modified_arithmetic
data_files:
- split: default
path: modified_arithmetic/default-*
- split: train
path: modified_arithmetic/train-*
- split: validation
path: modified_arithmetic/validation-*
- config_name: moral_permissibility
data_files:
- split: default
path: moral_permissibility/default-*
- split: train
path: moral_permissibility/train-*
- split: validation
path: moral_permissibility/validation-*
- config_name: movie_dialog_same_or_different
data_files:
- split: default
path: movie_dialog_same_or_different/default-*
- split: train
path: movie_dialog_same_or_different/train-*
- split: validation
path: movie_dialog_same_or_different/validation-*
- config_name: movie_recommendation
data_files:
- split: default
path: movie_recommendation/default-*
- split: train
path: movie_recommendation/train-*
- split: validation
path: movie_recommendation/validation-*
- config_name: mult_data_wrangling
data_files:
- split: default
path: mult_data_wrangling/default-*
- split: train
path: mult_data_wrangling/train-*
- split: validation
path: mult_data_wrangling/validation-*
---
|
petrpan26/typescript-code | ---
dataset_info:
features:
- name: index
dtype: int64
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 2750873540
num_examples: 380000
download_size: 879130666
dataset_size: 2750873540
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jpg-mp3/image-audio | ---
license: bsd
size_categories:
- n<1K
tags:
- synthetic
pretty_name: image-to-audio
---
.wav files converted to numpy arrays using the [sampling.py](https://huggingface.co/datasets/jpg-mp3/image-audio/blob/main/sampling.py) script using [librosa](https://librosa.org/) |
ZoneTwelve/tmmluplus | ---
license: other
license_name: creative-commons-by-nc
task_categories:
- question-answering
language:
- zh
tags:
- traditional chinese
- finance
- medical
- taiwan
- benchmark
- zh-tw
- zh-hant
pretty_name: tmmlu++
size_categories:
- 100K<n<1M
configs:
- config_name: engineering_math
datafiles:
- split: train
path: "data/engineering_math_dev.csv"
- split: validation
path: "data/engineering_math_val.csv"
- split: test
path: "data/engineering_math_test.csv"
- config_name: dentistry
datafiles:
- split: train
path: "data/dentistry_dev.csv"
- split: validation
path: "data/dentistry_val.csv"
- split: test
path: "data/dentistry_test.csv"
- config_name: traditional_chinese_medicine_clinical_medicine
datafiles:
- split: train
path: "data/traditional_chinese_medicine_clinical_medicine_dev.csv"
- split: validation
path: "data/traditional_chinese_medicine_clinical_medicine_val.csv"
- split: test
path: "data/traditional_chinese_medicine_clinical_medicine_test.csv"
- config_name: clinical_psychology
datafiles:
- split: train
path: "data/clinical_psychology_dev.csv"
- split: validation
path: "data/clinical_psychology_val.csv"
- split: test
path: "data/clinical_psychology_test.csv"
- config_name: technical
datafiles:
- split: train
path: "data/technical_dev.csv"
- split: validation
path: "data/technical_val.csv"
- split: test
path: "data/technical_test.csv"
- config_name: culinary_skills
datafiles:
- split: train
path: "data/culinary_skills_dev.csv"
- split: validation
path: "data/culinary_skills_val.csv"
- split: test
path: "data/culinary_skills_test.csv"
- config_name: mechanical
datafiles:
- split: train
path: "data/mechanical_dev.csv"
- split: validation
path: "data/mechanical_val.csv"
- split: test
path: "data/mechanical_test.csv"
- config_name: logic_reasoning
datafiles:
- split: train
path: "data/logic_reasoning_dev.csv"
- split: validation
path: "data/logic_reasoning_val.csv"
- split: test
path: "data/logic_reasoning_test.csv"
- config_name: real_estate
datafiles:
- split: train
path: "data/real_estate_dev.csv"
- split: validation
path: "data/real_estate_val.csv"
- split: test
path: "data/real_estate_test.csv"
- config_name: general_principles_of_law
datafiles:
- split: train
path: "data/general_principles_of_law_dev.csv"
- split: validation
path: "data/general_principles_of_law_val.csv"
- split: test
path: "data/general_principles_of_law_test.csv"
- config_name: finance_banking
datafiles:
- split: train
path: "data/finance_banking_dev.csv"
- split: validation
path: "data/finance_banking_val.csv"
- split: test
path: "data/finance_banking_test.csv"
- config_name: anti_money_laundering
datafiles:
- split: train
path: "data/anti_money_laundering_dev.csv"
- split: validation
path: "data/anti_money_laundering_val.csv"
- split: test
path: "data/anti_money_laundering_test.csv"
- config_name: ttqav2
datafiles:
- split: train
path: "data/ttqav2_dev.csv"
- split: validation
path: "data/ttqav2_val.csv"
- split: test
path: "data/ttqav2_test.csv"
- config_name: marketing_management
datafiles:
- split: train
path: "data/marketing_management_dev.csv"
- split: validation
path: "data/marketing_management_val.csv"
- split: test
path: "data/marketing_management_test.csv"
- config_name: business_management
datafiles:
- split: train
path: "data/business_management_dev.csv"
- split: validation
path: "data/business_management_val.csv"
- split: test
path: "data/business_management_test.csv"
- config_name: organic_chemistry
datafiles:
- split: train
path: "data/organic_chemistry_dev.csv"
- split: validation
path: "data/organic_chemistry_val.csv"
- split: test
path: "data/organic_chemistry_test.csv"
- config_name: advance_chemistry
datafiles:
- split: train
path: "data/advance_chemistry_dev.csv"
- split: validation
path: "data/advance_chemistry_val.csv"
- split: test
path: "data/advance_chemistry_test.csv"
- config_name: physics
datafiles:
- split: train
path: "data/physics_dev.csv"
- split: validation
path: "data/physics_val.csv"
- split: test
path: "data/physics_test.csv"
- config_name: secondary_physics
datafiles:
- split: train
path: "data/secondary_physics_dev.csv"
- split: validation
path: "data/secondary_physics_val.csv"
- split: test
path: "data/secondary_physics_test.csv"
- config_name: human_behavior
datafiles:
- split: train
path: "data/human_behavior_dev.csv"
- split: validation
path: "data/human_behavior_val.csv"
- split: test
path: "data/human_behavior_test.csv"
- config_name: national_protection
datafiles:
- split: train
path: "data/national_protection_dev.csv"
- split: validation
path: "data/national_protection_val.csv"
- split: test
path: "data/national_protection_test.csv"
- config_name: jce_humanities
datafiles:
- split: train
path: "data/jce_humanities_dev.csv"
- split: validation
path: "data/jce_humanities_val.csv"
- split: test
path: "data/jce_humanities_test.csv"
- config_name: politic_science
datafiles:
- split: train
path: "data/politic_science_dev.csv"
- split: validation
path: "data/politic_science_val.csv"
- split: test
path: "data/politic_science_test.csv"
- config_name: agriculture
datafiles:
- split: train
path: "data/agriculture_dev.csv"
- split: validation
path: "data/agriculture_val.csv"
- split: test
path: "data/agriculture_test.csv"
- config_name: official_document_management
datafiles:
- split: train
path: "data/official_document_management_dev.csv"
- split: validation
path: "data/official_document_management_val.csv"
- split: test
path: "data/official_document_management_test.csv"
- config_name: financial_analysis
datafiles:
- split: train
path: "data/financial_analysis_dev.csv"
- split: validation
path: "data/financial_analysis_val.csv"
- split: test
path: "data/financial_analysis_test.csv"
- config_name: pharmacy
datafiles:
- split: train
path: "data/pharmacy_dev.csv"
- split: validation
path: "data/pharmacy_val.csv"
- split: test
path: "data/pharmacy_test.csv"
- config_name: educational_psychology
datafiles:
- split: train
path: "data/educational_psychology_dev.csv"
- split: validation
path: "data/educational_psychology_val.csv"
- split: test
path: "data/educational_psychology_test.csv"
- config_name: statistics_and_machine_learning
datafiles:
- split: train
path: "data/statistics_and_machine_learning_dev.csv"
- split: validation
path: "data/statistics_and_machine_learning_val.csv"
- split: test
path: "data/statistics_and_machine_learning_test.csv"
- config_name: management_accounting
datafiles:
- split: train
path: "data/management_accounting_dev.csv"
- split: validation
path: "data/management_accounting_val.csv"
- split: test
path: "data/management_accounting_test.csv"
- config_name: introduction_to_law
datafiles:
- split: train
path: "data/introduction_to_law_dev.csv"
- split: validation
path: "data/introduction_to_law_val.csv"
- split: test
path: "data/introduction_to_law_test.csv"
- config_name: computer_science
datafiles:
- split: train
path: "data/computer_science_dev.csv"
- split: validation
path: "data/computer_science_val.csv"
- split: test
path: "data/computer_science_test.csv"
- config_name: veterinary_pathology
datafiles:
- split: train
path: "data/veterinary_pathology_dev.csv"
- split: validation
path: "data/veterinary_pathology_val.csv"
- split: test
path: "data/veterinary_pathology_test.csv"
- config_name: accounting
datafiles:
- split: train
path: "data/accounting_dev.csv"
- split: validation
path: "data/accounting_val.csv"
- split: test
path: "data/accounting_test.csv"
- config_name: fire_science
datafiles:
- split: train
path: "data/fire_science_dev.csv"
- split: validation
path: "data/fire_science_val.csv"
- split: test
path: "data/fire_science_test.csv"
- config_name: optometry
datafiles:
- split: train
path: "data/optometry_dev.csv"
- split: validation
path: "data/optometry_val.csv"
- split: test
path: "data/optometry_test.csv"
- config_name: insurance_studies
datafiles:
- split: train
path: "data/insurance_studies_dev.csv"
- split: validation
path: "data/insurance_studies_val.csv"
- split: test
path: "data/insurance_studies_test.csv"
- config_name: pharmacology
datafiles:
- split: train
path: "data/pharmacology_dev.csv"
- split: validation
path: "data/pharmacology_val.csv"
- split: test
path: "data/pharmacology_test.csv"
- config_name: taxation
datafiles:
- split: train
path: "data/taxation_dev.csv"
- split: validation
path: "data/taxation_val.csv"
- split: test
path: "data/taxation_test.csv"
- config_name: trust_practice
datafiles:
- split: train
path: "data/trust_practice_dev.csv"
- split: validation
path: "data/trust_practice_val.csv"
- split: test
path: "data/trust_practice_test.csv"
- config_name: geography_of_taiwan
datafiles:
- split: train
path: "data/geography_of_taiwan_dev.csv"
- split: validation
path: "data/geography_of_taiwan_val.csv"
- split: test
path: "data/geography_of_taiwan_test.csv"
- config_name: physical_education
datafiles:
- split: train
path: "data/physical_education_dev.csv"
- split: validation
path: "data/physical_education_val.csv"
- split: test
path: "data/physical_education_test.csv"
- config_name: auditing
datafiles:
- split: train
path: "data/auditing_dev.csv"
- split: validation
path: "data/auditing_val.csv"
- split: test
path: "data/auditing_test.csv"
- config_name: administrative_law
datafiles:
- split: train
path: "data/administrative_law_dev.csv"
- split: validation
path: "data/administrative_law_val.csv"
- split: test
path: "data/administrative_law_test.csv"
- config_name: education_(profession_level)
datafiles:
- split: train
path: "data/education_(profession_level)_dev.csv"
- split: validation
path: "data/education_(profession_level)_val.csv"
- split: test
path: "data/education_(profession_level)_test.csv"
- config_name: economics
datafiles:
- split: train
path: "data/economics_dev.csv"
- split: validation
path: "data/economics_val.csv"
- split: test
path: "data/economics_test.csv"
- config_name: veterinary_pharmacology
datafiles:
- split: train
path: "data/veterinary_pharmacology_dev.csv"
- split: validation
path: "data/veterinary_pharmacology_val.csv"
- split: test
path: "data/veterinary_pharmacology_test.csv"
- config_name: nautical_science
datafiles:
- split: train
path: "data/nautical_science_dev.csv"
- split: validation
path: "data/nautical_science_val.csv"
- split: test
path: "data/nautical_science_test.csv"
- config_name: occupational_therapy_for_psychological_disorders
datafiles:
- split: train
path: "data/occupational_therapy_for_psychological_disorders_dev.csv"
- split: validation
path: "data/occupational_therapy_for_psychological_disorders_val.csv"
- split: test
path: "data/occupational_therapy_for_psychological_disorders_test.csv"
- config_name: basic_medical_science
datafiles:
- split: train
path: "data/basic_medical_science_dev.csv"
- split: validation
path: "data/basic_medical_science_val.csv"
- split: test
path: "data/basic_medical_science_test.csv"
- config_name: macroeconomics
datafiles:
- split: train
path: "data/macroeconomics_dev.csv"
- split: validation
path: "data/macroeconomics_val.csv"
- split: test
path: "data/macroeconomics_test.csv"
- config_name: trade
datafiles:
- split: train
path: "data/trade_dev.csv"
- split: validation
path: "data/trade_val.csv"
- split: test
path: "data/trade_test.csv"
- config_name: chinese_language_and_literature
datafiles:
- split: train
path: "data/chinese_language_and_literature_dev.csv"
- split: validation
path: "data/chinese_language_and_literature_val.csv"
- split: test
path: "data/chinese_language_and_literature_test.csv"
- config_name: tve_design
datafiles:
- split: train
path: "data/tve_design_dev.csv"
- split: validation
path: "data/tve_design_val.csv"
- split: test
path: "data/tve_design_test.csv"
- config_name: junior_science_exam
datafiles:
- split: train
path: "data/junior_science_exam_dev.csv"
- split: validation
path: "data/junior_science_exam_val.csv"
- split: test
path: "data/junior_science_exam_test.csv"
- config_name: junior_math_exam
datafiles:
- split: train
path: "data/junior_math_exam_dev.csv"
- split: validation
path: "data/junior_math_exam_val.csv"
- split: test
path: "data/junior_math_exam_test.csv"
- config_name: junior_chinese_exam
datafiles:
- split: train
path: "data/junior_chinese_exam_dev.csv"
- split: validation
path: "data/junior_chinese_exam_val.csv"
- split: test
path: "data/junior_chinese_exam_test.csv"
- config_name: junior_social_studies
datafiles:
- split: train
path: "data/junior_social_studies_dev.csv"
- split: validation
path: "data/junior_social_studies_val.csv"
- split: test
path: "data/junior_social_studies_test.csv"
- config_name: tve_mathematics
datafiles:
- split: train
path: "data/tve_mathematics_dev.csv"
- split: validation
path: "data/tve_mathematics_val.csv"
- split: test
path: "data/tve_mathematics_test.csv"
- config_name: tve_chinese_language
datafiles:
- split: train
path: "data/tve_chinese_language_dev.csv"
- split: validation
path: "data/tve_chinese_language_val.csv"
- split: test
path: "data/tve_chinese_language_test.csv"
- config_name: tve_natural_sciences
datafiles:
- split: train
path: "data/tve_natural_sciences_dev.csv"
- split: validation
path: "data/tve_natural_sciences_val.csv"
- split: test
path: "data/tve_natural_sciences_test.csv"
- config_name: junior_chemistry
datafiles:
- split: train
path: "data/junior_chemistry_dev.csv"
- split: validation
path: "data/junior_chemistry_val.csv"
- split: test
path: "data/junior_chemistry_test.csv"
- config_name: music
datafiles:
- split: train
path: "data/music_dev.csv"
- split: validation
path: "data/music_val.csv"
- split: test
path: "data/music_test.csv"
- config_name: education
datafiles:
- split: train
path: "data/education_dev.csv"
- split: validation
path: "data/education_val.csv"
- split: test
path: "data/education_test.csv"
- config_name: three_principles_of_people
datafiles:
- split: train
path: "data/three_principles_of_people_dev.csv"
- split: validation
path: "data/three_principles_of_people_val.csv"
- split: test
path: "data/three_principles_of_people_test.csv"
- config_name: taiwanese_hokkien
datafiles:
- split: train
path: "data/taiwanese_hokkien_dev.csv"
- split: validation
path: "data/taiwanese_hokkien_val.csv"
- split: test
path: "data/taiwanese_hokkien_test.csv"
- config_name: linear_algebra
datafiles:
- split: train
path: "data/linear_algebra_dev.csv"
- split: validation
path: "data/linear_algebra_val.csv"
- split: test
path: "data/linear_algebra_test.csv"
---
# TMMLU+ : Large scale traditional chinese massive multitask language understanding
<p align="center">
<img src="https://huggingface.co/datasets/ikala/tmmluplus/resolve/main/cover.png" alt="A close-up image of a neat paper note with a white background. The text 'TMMLU+' is written horizontally across the center of the note in bold, black. Join us to work in multimodal LLM : https://ikala.ai/recruit/" style="max-width: 400" width=400 />
</p>
We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level.
The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models.
```python
from datasets import load_dataset
task_list = [
'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate',
'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry',
'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management',
'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology',
'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law',
'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders',
'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam',
'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people',
'taiwanese_hokkien',
'linear_algebra'
]
for task in task_list:
val = load_dataset('ZoneTwelve/tmmluplus', task)['validation']
dev = load_dataset('ZoneTwelve/tmmluplus', task)['train']
test = load_dataset('ZoneTwelve/tmmluplus', task)['test']
```
For each dataset split
```python
for row in test:
print(row)
break
>> Dataset({
features: ['question', 'A', 'B', 'C', 'D', 'answer'],
num_rows: 11
})
```
Statistic on all four categories : STEM, Social Science, Humanities, Other
| Category | Test | Dev | Validation |
|----------------------------------|-------|------|------------|
| STEM | 3458 | 70 | 385 |
| Social Sciences | 5958 | 90 | 665 |
| Humanities | 1763 | 35 | 197 |
| Other (Business, Health, Misc.) | 8939 | 135 | 995 |
| **Total** | 20118 | 330 | 2242 |
## Benchmark on direct prompting
| model | STEM | Social Science | Humanities | Other | Average |
|------------|------------|------------|------------|------------|------------|
| [Qwen/Qwen-72B](https://huggingface.co/Qwen/Qwen-72B) | 61.12 | 71.65 | 63.00 | 61.31 |64.27|
| gpt-4-0613 | 60.36 | 67.36 | 56.03 | 57.62 |60.34|
| [Qwen/Qwen-72B-Chat](https://huggingface.co/Qwen/Qwen-72B-Chat) | 55.15 | 66.20 | 55.65 | 57.19 |58.55|
| [Qwen/Qwen-14B](https://huggingface.co/Qwen/Qwen-14B) | 46.94 | 56.69 | 49.43 | 48.81 |50.47|
| Gemini-pro | 45.38 | 57.29 | 48.80 | 48.21 |49.92|
| [01-ai/Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 40.24 | 56.77 | 53.99 | 47.58 |49.64|
| [Qwen/Qwen-14B-Chat](https://huggingface.co/Qwen/Qwen-14B-Chat) | 43.86 | 53.29 | 44.78 | 45.13 |46.77|
| [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) | 39.62 | 50.24 | 44.44 | 44.26 |44.64|
| Claude-1.3 | 42.65 | 49.33 | 42.16 | 44.14 |44.57|
| gpt-3.5-turbo-0613 | 41.56 | 46.72 | 36.73 | 42.03 |41.76|
| [CausalLM/14B](https://huggingface.co/CausalLM/14B) | 39.83 | 44.50 | 39.61 | 41.97 |41.48|
| [Skywork/Skywork-13B-base](https://huggingface.co/Skywork/Skywork-13B-base) | 36.93 | 47.27 | 41.04 | 40.10 |41.33|
| [Qwen/Qwen-7B](https://huggingface.co/Qwen/Qwen-7B) | 37.53 | 45.48 | 38.09 | 38.96 |40.01|
| [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) | 33.32 | 44.64 | 40.27 | 39.89 |39.53|
| [vivo-ai/BlueLM-7B-Base](https://huggingface.co/vivo-ai/BlueLM-7B-Base) | 33.94 | 41.52 | 37.38 | 38.74 |37.90|
| [baichuan-inc/Baichuan2-13B-Chat](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat) | 29.64 | 43.73 | 37.36 | 39.88 |37.65|
| [Qwen/Qwen-1_8B](https://huggingface.co/Qwen/Qwen-1_8B) | 32.65 | 38.95 | 38.34 | 35.27 |36.30|
| Claude-2 | 39.65 | 39.09 | 28.59 | 37.47 |36.20|
| [THUDM/chatglm3-6b](https://huggingface.co/THUDM/chatglm3-6b) | 31.05 | 39.31 | 35.64 | 35.60 |35.40|
| [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) | 29.82 | 42.29 | 34.24 | 34.31 |35.17|
| [CausalLM/7B](https://huggingface.co/CausalLM/7B) | 31.03 | 38.17 | 35.87 | 35.39 |35.11|
| [Azure99/blossom-v3_1-mistral-7b](https://huggingface.co/Azure99/blossom-v3_1-mistral-7b) | 32.80 | 36.91 | 32.36 | 34.53 |34.15|
| [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b) | 24.69 | 39.18 | 33.60 | 31.99 |32.37|
| [Qwen/Qwen-1_8B-Chat](https://huggingface.co/Qwen/Qwen-1_8B-Chat) | 26.60 | 36.36 | 31.81 | 31.96 |31.68|
| [TigerResearch/tigerbot-13b-chat-v3](https://huggingface.co/TigerResearch/tigerbot-13b-chat-v3) | 24.73 | 29.63 | 25.72 | 27.22 |26.82|
| [hongyin/mistral-7b-80k](https://huggingface.co/hongyin/mistral-7b-80k) | 24.26 | 23.76 | 22.56 | 24.57 |23.79|
| [deepseek-ai/deepseek-llm-67b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat) | 19.10 | 26.06 | 21.51 | 21.77 |22.11|
| [yentinglin/Taiwan-LLM-13B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-13B-v2.0-chat) | 18.53 | 27.65 | 17.77 | 21.49 |21.36|
| [GeneZC/MiniChat-3B](https://huggingface.co/GeneZC/MiniChat-3B) | 17.66 | 23.35 | 22.71 | 20.34 |21.02|
| [LinkSoul/Chinese-Llama-2-7b](https://huggingface.co/LinkSoul/Chinese-Llama-2-7b) | 16.55 | 18.39 | 12.97 | 16.13 |16.01|
| [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) | 14.99 | 16.23 | 15.00 | 16.22 |15.61|
| Claude-instant-1 | 12.52 | 17.13 | 15.10 | 13.57 |14.58|
| [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) | 5.60 | 13.57 | 7.71 | 11.84 |9.68|
Results via [ievals](https://github.com/iKala/ievals) ( settings : 0-shot direct answering )
# Citation
```
@article{ikala2023eval,
title={An Improved Traditional Chinese Evaluation Suite for Foundation Model},
author={Tam, Zhi-Rui and Pai, Ya-Ting},
journal={arXiv},
year={2023}
}
```
> CONTENT WARNING
> This is a modification of ikala/tmmluplus, with minor alterations made to facilitate the implementation for lm-evaluation-harness purposes.
> [More details on Discussions](https://huggingface.co/datasets/ZoneTwelve/tmmluplus/discussions/1) |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_3_tp_0.1 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
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dtype: string
- name: input
dtype: string
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- name: gen_kwargs
struct:
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dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
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- name: top_p
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- name: reward_1
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- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
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download_size: 679025547
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configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
- split: epoch_12
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
- split: epoch_13
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
- split: epoch_14
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
- split: epoch_21
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
- split: epoch_22
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
- split: epoch_23
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
Svenni551/toxic-full-uncensored-v3.0 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
- name: response
dtype: string
splits:
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num_examples: 34
download_size: 44379
dataset_size: 82572
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/81882391 | ---
dataset_info:
features:
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dtype: string
- name: id
dtype: int64
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num_examples: 10
download_size: 1331
dataset_size: 182
---
# Dataset Card for "81882391"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atmallen/quirky_sciq_pythia-410m_bob_hard | ---
dataset_info:
features:
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dtype: string
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sequence: string
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- name: bob_label
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configs:
- config_name: default
data_files:
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path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
ArthurFischel/AA_taster_mon_hum_1_game_raw | ---
dataset_info:
features:
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dtype: string
- name: timestamp
dtype: int64
- name: done
dtype: bool
- name: gameid
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dtype: string
splits:
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download_size: 6136102
dataset_size: 86802495
---
# Dataset Card for "AA_taster_mon_hum_1_game_raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rinabuoy/Eng-Khmer | ---
dataset_info:
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configs:
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data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
freshpearYoon/vr_train_free_35 | ---
dataset_info:
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struct:
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sequence: float64
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dtype: string
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dtype: string
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dataset_size: 6973918443
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Helsinki-NLP/opus_paracrawl | ---
annotations_creators:
- found
language_creators:
- found
language:
- bg
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- cs
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- el
- en
- es
- et
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- it
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- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: OpusParaCrawl
config_names:
- de-pl
- el-en
- en-ha
- en-ig
- en-km
- en-so
- en-sw
- en-tl
- es-gl
- fr-nl
dataset_info:
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data_files:
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data_files:
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path: en-sw/train-*
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data_files:
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path: en-tl/train-*
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data_files:
- split: train
path: es-gl/train-*
- config_name: fr-nl
data_files:
- split: train
path: fr-nl/train-*
---
# Dataset Card for OpusParaCrawl
## 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:** http://opus.nlpl.eu/ParaCrawl.php
- **Repository:** None
- **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Parallel corpora from Web Crawls collected in the ParaCrawl project.
Tha dataset contains:
- 42 languages, 43 bitexts
- total number of files: 59,996
- total number of tokens: 56.11G
- total number of sentence fragments: 3.13G
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?",
"en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
- Creative commons CC0 (no rights reserved)
### Citation Information
```bibtex
@inproceedings{banon-etal-2020-paracrawl,
title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
author = "Ba{\~n}{\'o}n, Marta and
Chen, Pinzhen and
Haddow, Barry and
Heafield, Kenneth and
Hoang, Hieu and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Kamran, Amir and
Kirefu, Faheem and
Koehn, Philipp and
Ortiz Rojas, Sergio and
Pla Sempere, Leopoldo and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Sarr{\'\i}as, Elsa and
Strelec, Marek and
Thompson, Brian and
Waites, William and
Wiggins, Dion and
Zaragoza, Jaume",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.417",
doi = "10.18653/v1/2020.acl-main.417",
pages = "4555--4567",
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {Jörg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
HoarfrostRaven/fibres_64 | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: uint8
splits:
- name: train
num_bytes: 17359200
num_examples: 600
download_size: 7443781
dataset_size: 17359200
---
# Dataset Card for "fibre"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lucyd/deepgen_eval | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 4463
num_examples: 43
download_size: 3837
dataset_size: 4463
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Replete-AI__Mistral-11b-v0.1 | ---
pretty_name: Evaluation run of Replete-AI/Mistral-11b-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Replete-AI/Mistral-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-11b-v0.1)\
\ 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_Replete-AI__Mistral-11b-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T12:30:39.267532](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-11b-v0.1/blob/main/results_2024-03-21T12-30-39.267532.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\": 0.6309829529704917,\n\
\ \"acc_stderr\": 0.03249502226073932,\n \"acc_norm\": 0.6345615860197364,\n\
\ \"acc_norm_stderr\": 0.033137530512338635,\n \"mc1\": 0.4320685434516524,\n\
\ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\
\ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5989761092150171,\n \"acc_stderr\": 0.014322255790719867,\n\
\ \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.014169664520303101\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6646086436964748,\n\
\ \"acc_stderr\": 0.004711622011148463,\n \"acc_norm\": 0.8465445130452102,\n\
\ \"acc_norm_stderr\": 0.0035968938961909113\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.042446332383532265,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.042446332383532265\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\": 0.55,\n\
\ \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.55,\n \
\ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6415094339622641,\n \"acc_stderr\": 0.02951470358398176,\n\
\ \"acc_norm\": 0.6415094339622641,\n \"acc_norm_stderr\": 0.02951470358398176\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\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.54,\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\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n\
\ \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4444444444444444,\n \"acc_stderr\": 0.025591857761382182,\n \"\
acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.025591857761382182\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7612903225806451,\n\
\ \"acc_stderr\": 0.024251071262208837,\n \"acc_norm\": 0.7612903225806451,\n\
\ \"acc_norm_stderr\": 0.024251071262208837\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.031234752377721164,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.031234752377721164\n \
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.02805779167298901,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.02805779167298901\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131154,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977934,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977934\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8385321100917431,\n \"acc_stderr\": 0.015776239256163224,\n \"\
acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.015776239256163224\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5509259259259259,\n \"acc_stderr\": 0.03392238405321617,\n \"\
acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.03392238405321617\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437395,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437395\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.027652153144159274,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.027652153144159274\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.03050028317654585,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.03050028317654585\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.034089978868575295,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.034089978868575295\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\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077816,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077816\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8007662835249042,\n\
\ \"acc_stderr\": 0.014283378044296417,\n \"acc_norm\": 0.8007662835249042,\n\
\ \"acc_norm_stderr\": 0.014283378044296417\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.20446927374301677,\n\
\ \"acc_stderr\": 0.01348881340471193,\n \"acc_norm\": 0.20446927374301677,\n\
\ \"acc_norm_stderr\": 0.01348881340471193\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.02625605383571896,\n\
\ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.02625605383571896\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694905,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694905\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7067901234567902,\n \"acc_stderr\": 0.02532988817190092,\n\
\ \"acc_norm\": 0.7067901234567902,\n \"acc_norm_stderr\": 0.02532988817190092\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4680573663624511,\n\
\ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\
\ \"acc_norm_stderr\": 0.012744149704869649\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\": 0.6372549019607843,\n \"acc_stderr\": 0.01945076843250552,\n \
\ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.01945076843250552\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.04631381319425465\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\": 0.8159203980099502,\n\
\ \"acc_stderr\": 0.02740385941078684,\n \"acc_norm\": 0.8159203980099502,\n\
\ \"acc_norm_stderr\": 0.02740385941078684\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4320685434516524,\n\
\ \"mc1_stderr\": 0.017341202394988257,\n \"mc2\": 0.5923114451952954,\n\
\ \"mc2_stderr\": 0.016045963776594944\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174789\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4981046247156937,\n \
\ \"acc_stderr\": 0.013772385765569753\n }\n}\n```"
repo_url: https://huggingface.co/Replete-AI/Mistral-11b-v0.1
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_21T12_30_39.267532
path:
- '**/details_harness|arc:challenge|25_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|gsm8k|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hellaswag|10_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-30-39.267532.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T12-30-39.267532.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- '**/details_harness|winogrande|5_2024-03-21T12-30-39.267532.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T12-30-39.267532.parquet'
- config_name: results
data_files:
- split: 2024_03_21T12_30_39.267532
path:
- results_2024-03-21T12-30-39.267532.parquet
- split: latest
path:
- results_2024-03-21T12-30-39.267532.parquet
---
# Dataset Card for Evaluation run of Replete-AI/Mistral-11b-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Replete-AI/Mistral-11b-v0.1](https://huggingface.co/Replete-AI/Mistral-11b-v0.1) 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_Replete-AI__Mistral-11b-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T12:30:39.267532](https://huggingface.co/datasets/open-llm-leaderboard/details_Replete-AI__Mistral-11b-v0.1/blob/main/results_2024-03-21T12-30-39.267532.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": 0.6309829529704917,
"acc_stderr": 0.03249502226073932,
"acc_norm": 0.6345615860197364,
"acc_norm_stderr": 0.033137530512338635,
"mc1": 0.4320685434516524,
"mc1_stderr": 0.017341202394988257,
"mc2": 0.5923114451952954,
"mc2_stderr": 0.016045963776594944
},
"harness|arc:challenge|25": {
"acc": 0.5989761092150171,
"acc_stderr": 0.014322255790719867,
"acc_norm": 0.6220136518771331,
"acc_norm_stderr": 0.014169664520303101
},
"harness|hellaswag|10": {
"acc": 0.6646086436964748,
"acc_stderr": 0.004711622011148463,
"acc_norm": 0.8465445130452102,
"acc_norm_stderr": 0.0035968938961909113
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.042446332383532265,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.042446332383532265
},
"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": 0.55,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.55,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6415094339622641,
"acc_stderr": 0.02951470358398176,
"acc_norm": 0.6415094339622641,
"acc_norm_stderr": 0.02951470358398176
},
"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": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"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": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.03643037168958548,
"acc_norm": 0.6473988439306358,
"acc_norm_stderr": 0.03643037168958548
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5531914893617021,
"acc_stderr": 0.0325005368436584,
"acc_norm": 0.5531914893617021,
"acc_norm_stderr": 0.0325005368436584
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4298245614035088,
"acc_stderr": 0.04657047260594963,
"acc_norm": 0.4298245614035088,
"acc_norm_stderr": 0.04657047260594963
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.025591857761382182,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.025591857761382182
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7612903225806451,
"acc_stderr": 0.024251071262208837,
"acc_norm": 0.7612903225806451,
"acc_norm_stderr": 0.024251071262208837
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8,
"acc_stderr": 0.031234752377721164,
"acc_norm": 0.8,
"acc_norm_stderr": 0.031234752377721164
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
"acc_stderr": 0.02805779167298901,
"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.02805779167298901
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8652849740932642,
"acc_stderr": 0.02463978909770944,
"acc_norm": 0.8652849740932642,
"acc_norm_stderr": 0.02463978909770944
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.028897748741131154,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.028897748741131154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.030066761582977934,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977934
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
"acc_stderr": 0.015776239256163224,
"acc_norm": 0.8385321100917431,
"acc_norm_stderr": 0.015776239256163224
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5509259259259259,
"acc_stderr": 0.03392238405321617,
"acc_norm": 0.5509259259259259,
"acc_norm_stderr": 0.03392238405321617
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437395,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437395
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7637130801687764,
"acc_stderr": 0.027652153144159274,
"acc_norm": 0.7637130801687764,
"acc_norm_stderr": 0.027652153144159274
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7085201793721974,
"acc_stderr": 0.03050028317654585,
"acc_norm": 0.7085201793721974,
"acc_norm_stderr": 0.03050028317654585
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.034089978868575295,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.034089978868575295
},
"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": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.022509033937077816,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077816
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8007662835249042,
"acc_stderr": 0.014283378044296417,
"acc_norm": 0.8007662835249042,
"acc_norm_stderr": 0.014283378044296417
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7167630057803468,
"acc_stderr": 0.024257901705323378,
"acc_norm": 0.7167630057803468,
"acc_norm_stderr": 0.024257901705323378
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.20446927374301677,
"acc_stderr": 0.01348881340471193,
"acc_norm": 0.20446927374301677,
"acc_norm_stderr": 0.01348881340471193
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6993464052287581,
"acc_stderr": 0.02625605383571896,
"acc_norm": 0.6993464052287581,
"acc_norm_stderr": 0.02625605383571896
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694905,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694905
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7067901234567902,
"acc_stderr": 0.02532988817190092,
"acc_norm": 0.7067901234567902,
"acc_norm_stderr": 0.02532988817190092
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4680573663624511,
"acc_stderr": 0.012744149704869649,
"acc_norm": 0.4680573663624511,
"acc_norm_stderr": 0.012744149704869649
},
"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": 0.6372549019607843,
"acc_stderr": 0.01945076843250552,
"acc_norm": 0.6372549019607843,
"acc_norm_stderr": 0.01945076843250552
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
"acc_stderr": 0.04631381319425465,
"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
},
"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": 0.8159203980099502,
"acc_stderr": 0.02740385941078684,
"acc_norm": 0.8159203980099502,
"acc_norm_stderr": 0.02740385941078684
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.783625730994152,
"acc_stderr": 0.03158149539338734,
"acc_norm": 0.783625730994152,
"acc_norm_stderr": 0.03158149539338734
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4320685434516524,
"mc1_stderr": 0.017341202394988257,
"mc2": 0.5923114451952954,
"mc2_stderr": 0.016045963776594944
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174789
},
"harness|gsm8k|5": {
"acc": 0.4981046247156937,
"acc_stderr": 0.013772385765569753
}
}
```
## 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] |
commaai/commaSteeringControl | ---
license: mit
size_categories:
- 100K<n<1M
---
# commaSteeringControl
`commaSteeringControl` is a dataset of car steering measurements from ~12500 hours of driving with openpilot engaged. We control steering on most cars in openpilot using `steeringTorque`. This results in some lateral acceleration depending on both the car's internal vehicle dynamics and external factors (car speed, road roll, etc). Learning this relationship is essential to having accurate steering control in openpilot. `commaSteeringControl` is the largest controls dataset of its kind, spanning
hundreds of car models across 10+ brands.
The main purpose of this dataset is to give the community access to the data needed to model the steering of their car, and with that make a more accurate steering controller in openpilot to improve openpilot's performance on that car.
This is the largest dataset of vehicle dynamics ever released. It can also be used to develop or verify practical vehicle dynamics models for lateral acceleration, tire slip, road roll, understeer/oversteer, etc. We may add more fields for this goal in the future.
## Dataset
- Download the dataset from [HuggingFace](https://huggingface.co/datasets/commaai/commaSteeringControl/tree/main/data)
- Checkout the example notebook at [`visualize.ipynb`](https://github.com/commaai/comma-steering-control/blob/master/visualize.ipynb)
```
# Data Structure
data/
├── Platform 1
| ├── Segment 1
| ├── ...
| └── Segment N
└── Platform M
├── Segment 1
└── ...
| | Fields | Description | Value Range |
|---:|:----------------------|:---------------------------------------------------------------------------------|:----------------|
| 0 | t | Time | [0, 60] |
| 1 | latActive | Is openpilot engaged? | {True, False} |
| 2 | steeringPressed | Is steering wheel pressed by the user? | {True, False} |
| 3 | vEgo | Forward velocity of the car (m/s) | [0, ∞] |
| 4 | aEgo | Forward acceleration of the car (m/s^2) | [-∞, ∞] |
| 5 | steeringAngleDeg | Steering Angle (Deg) | [-∞, ∞] |
| 6 | steer | Normalized steer torque | [-1, 1] |
| 7 | steerFiltered | Normalized, rate limited steer torque | [-1, 1] |
| 8 | roll | Road roll (rad) | [-0.174, 0.174] |
| 9 | latAccelDesired | Lateral acceleration requested from the planner | [-∞, ∞] |
| 10 | latAccelSteeringAngle | Lateral acceleration computed from the steering wheel angle and vehicle dynamics | [-∞, ∞] |
| 11 | latAccelLocalizer | Lateral acceleration from the localizer | [-∞, ∞] |
| 12 | epsFwVersion | EPS firmware version | str |
```
## Dataset Notes
- All values from different messages are interpolated and synced to time `t`
- Steering torque is normalized in openpilot (to get `steer`), and further rate limits are applied (to get `steerFiltered`). `steerFiltered` is the best input signal.
- The `latAccelSteeringAngle` is computed from steering angle and roll using the vehicle model from openpilot. This is the best signal to predict as `latAccelLocalizer`, which comes from a sensor fusion localizer on the comma three device, can be quite noisy.
- In reality (especially for some cars), the relationship is non-linear depending on vehicle speed, and has temporal dynamics. On many cars the steering command is processed and smoothed inside the EPS causing non-linearities and temporal effects. There are also temporal effects in the physics (like in a mass-spring-damper model).
- There may be a lag in openpilot fully regaining steering control after `steeringPressed` which may have to be accounted for.
- In some platforms, cars with different `epsFwVersion` have dramatically different steering behaviour, although this is not common.
- Any algorithm that could be upstreamed to openpilot needs to be simple, fast, and reliable - similar to `torqued`, simple non-linear functions, or simple MLPs etc.
## Timeline of lateral control modeling in openpilot
- In [0.8.15](https://blog.comma.ai/0815release/#torque-controller),
we introduced a [new controller](https://github.com/commaai/openpilot/blob/master/selfdrive/controls/lib/latcontrol_torque.py) that leveraged the relationship between steering torque and lateral acceleration.
- In [0.9.0](https://blog.comma.ai/090release/#torqued-an-auto-tuner-for-lateral-control), we introduced
[torqued](https://github.com/commaai/openpilot/blob/master/selfdrive/locationd/torqued.py), which learns the relationship online. Here we assume that the gravity adjusted lateral acceleration has a linear dependence wrt. the steer command. We fit a Total-Least-Squares solution to obtain the factor. We also assume an error-dependant friction value (causes the hysteresis).
- In [0.9.2](https://blog.comma.ai/092release/#chevrolet-bolt-euv), we introduced a non-linear feed-forward function.
- There has been [extensive community effort](https://github.com/twilsonco/openpilot/tree/log-info) to improve the controller (speed-based relationships, using neural networks, etc).
- We are working on further improvements for future releases.
|
jiuyuan/mind_prompts | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 106750384
num_examples: 48976
download_size: 38303071
dataset_size: 106750384
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mind_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/istella22_test | ---
pretty_name: '`istella22/test`'
viewer: false
source_datasets: ['irds/istella22']
task_categories:
- text-retrieval
---
# Dataset Card for `istella22/test`
The `istella22/test` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/istella22#istella22/test).
# Data
This dataset provides:
- `queries` (i.e., topics); count=2,198
- `qrels`: (relevance assessments); count=10,693
- For `docs`, use [`irds/istella22`](https://huggingface.co/datasets/irds/istella22)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/istella22_test', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/istella22_test', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
diwank/good_joke-dataset | ---
dataset_info:
features:
- name: source
dtype: string
- name: body
dtype: string
- name: title
dtype: string
- name: category
dtype: string
- name: rating
dtype: float64
splits:
- name: train
num_bytes: 2525722
num_examples: 20045
download_size: 1436839
dataset_size: 2525722
---
# Dataset Card for "good_joke-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-from-one-sec-cv12/chunk_132 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1113484908
num_examples: 216969
download_size: 1138209420
dataset_size: 1113484908
---
# Dataset Card for "chunk_132"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DavidLanz/medical_instruction | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
**Supervisory Fine-Tuning Dataset (SFT and RLHF)**
- Dataset Name: medical_finetune_tw.json
- Description: This dataset comprises a total of 2.06 million entries and is sourced from various sources, including:
1. Six medical department medical inquiry datasets from the [Chinese Medical Dialogue Dataset](https://github.com/Toyhom/Chinese-medical-dialogue-data), totaling 790,000 entries.
2. An online medical encyclopedia dataset, [huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa), with 360,000 entries.
3. A medical knowledge graph dataset, [huatuo_knowledge_graph_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa), with 790,000 entries. These three parts are merged, resulting in a dataset with a total of 1.95 million entries.
4. English medical inquiry dialogue data from [Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor), which includes data from HealthCareMagic-100k and GenMedGPT-5k datasets, totaling 110,000 entries.
|
Eternalenv/aaaaaa | ---
license: openrail
---
|
sixf0ur/GuanacoDataset-de | ---
license: gpl-3.0
task_categories:
- text-generation
- question-answering
language:
- de
pretty_name: German Guanaco Dataset
size_categories:
- 1K<n<10K
---
This dataset was taken from JosephusCheung/GuanacoDataset and filtered to German entries. |
AMead10/Universal-Pure-Dove | ---
dataset_info:
features:
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 11565500
num_examples: 3857
download_size: 5954760
dataset_size: 11565500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-Pure-Dove"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/Open_Platypus_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: 6181135
num_examples: 15444
download_size: 0
dataset_size: 6181135
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Open_Platypus_standardized_cluster_2_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fredmo/vertexai-qna-500 | ---
license: unknown
---
|
sagnikrayc/snli-bt | ---
license: afl-3.0
---
### Dataset Card for SNLI Back Translation
back translation of SNLI dataset: only use the test version
|
open-source-metrics/tokenizers-dependents | ---
license: apache-2.0
pretty_name: tokenizers metrics
tags:
- github-stars
dataset_info:
features:
- name: name
dtype: string
- name: stars
dtype: int64
- name: forks
dtype: int64
splits:
- name: package
num_bytes: 95
num_examples: 2
- name: repository
num_bytes: 1893
num_examples: 42
download_size: 5046
dataset_size: 1988
---
# tokenizers metrics
This dataset contains metrics about the huggingface/tokenizers package.
Number of repositories in the dataset: 11460
Number of packages in the dataset: 124
## Package dependents
This contains the data available in the [used-by](https://github.com/huggingface/tokenizers/network/dependents)
tab on GitHub.
### Package & Repository star count
This section shows the package and repository star count, individually.
Package | Repository
:-------------------------:|:-------------------------:
 | 
There are 14 packages that have more than 1000 stars.
There are 41 repositories that have more than 1000 stars.
The top 10 in each category are the following:
*Package*
[huggingface/transformers](https://github.com/huggingface/transformers): 70475
[hankcs/HanLP](https://github.com/hankcs/HanLP): 26958
[facebookresearch/ParlAI](https://github.com/facebookresearch/ParlAI): 9439
[UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers): 8461
[lucidrains/DALLE-pytorch](https://github.com/lucidrains/DALLE-pytorch): 4816
[ThilinaRajapakse/simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers): 3303
[neuml/txtai](https://github.com/neuml/txtai): 2530
[QData/TextAttack](https://github.com/QData/TextAttack): 2087
[lukas-blecher/LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR): 1981
[utterworks/fast-bert](https://github.com/utterworks/fast-bert): 1760
*Repository*
[huggingface/transformers](https://github.com/huggingface/transformers): 70480
[hankcs/HanLP](https://github.com/hankcs/HanLP): 26958
[RasaHQ/rasa](https://github.com/RasaHQ/rasa): 14842
[facebookresearch/ParlAI](https://github.com/facebookresearch/ParlAI): 9440
[gradio-app/gradio](https://github.com/gradio-app/gradio): 9169
[UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers): 8462
[microsoft/unilm](https://github.com/microsoft/unilm): 6650
[EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo): 6431
[moyix/fauxpilot](https://github.com/moyix/fauxpilot): 6300
[lucidrains/DALLE-pytorch](https://github.com/lucidrains/DALLE-pytorch): 4816
### Package & Repository fork count
This section shows the package and repository fork count, individually.
Package | Repository
:-------------------------:|:-------------------------:
 | 
There are 11 packages that have more than 200 forks.
There are 39 repositories that have more than 200 forks.
The top 10 in each category are the following:
*Package*
[huggingface/transformers](https://github.com/huggingface/transformers): 16158
[hankcs/HanLP](https://github.com/hankcs/HanLP): 7388
[facebookresearch/ParlAI](https://github.com/facebookresearch/ParlAI): 1920
[UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers): 1695
[ThilinaRajapakse/simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers): 658
[lucidrains/DALLE-pytorch](https://github.com/lucidrains/DALLE-pytorch): 543
[utterworks/fast-bert](https://github.com/utterworks/fast-bert): 336
[nyu-mll/jiant](https://github.com/nyu-mll/jiant): 273
[QData/TextAttack](https://github.com/QData/TextAttack): 269
[lukas-blecher/LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR): 245
*Repository*
[huggingface/transformers](https://github.com/huggingface/transformers): 16157
[hankcs/HanLP](https://github.com/hankcs/HanLP): 7388
[RasaHQ/rasa](https://github.com/RasaHQ/rasa): 4105
[plotly/dash-sample-apps](https://github.com/plotly/dash-sample-apps): 2795
[facebookresearch/ParlAI](https://github.com/facebookresearch/ParlAI): 1920
[UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers): 1695
[microsoft/unilm](https://github.com/microsoft/unilm): 1223
[openvinotoolkit/open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo): 1207
[bhaveshlohana/HacktoberFest2020-Contributions](https://github.com/bhaveshlohana/HacktoberFest2020-Contributions): 1020
[data-science-on-aws/data-science-on-aws](https://github.com/data-science-on-aws/data-science-on-aws): 884
|
heliosprime/twitter_dataset_1713215597 | ---
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: 18044
num_examples: 48
download_size: 17663
dataset_size: 18044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713215597"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_OCR_rices_ns_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
sequence: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0
num_bytes: 725000
num_examples: 1000
download_size: 118057
dataset_size: 725000
---
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_T_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Daniel357/Feh | ---
license: openrail
---
|
mask-distilled-one-sec-cv12/chunk_181 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1049298256
num_examples: 206068
download_size: 1071056555
dataset_size: 1049298256
---
# Dataset Card for "chunk_181"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jxm/trec-covid__gtr_base__dpr | ---
dataset_info:
features:
- name: text
dtype: string
- name: embeddings_A
sequence: float32
- name: embeddings_B
sequence: float32
splits:
- name: train
num_bytes: 719847373
num_examples: 100000
download_size: 732191015
dataset_size: 719847373
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sunlab/patch_db | ---
license: apache-2.0
task_categories:
- feature-extraction
- text-classification
- summarization
- text-generation
tags:
- code
- commit
- patch
language:
- en
pretty_name: PatchDB
size_categories:
- 10K<n<100K
---
# PatchDB: A Large-Scale Security Patch Dataset
## Description
To foster large-scale research on vulnerability mitigation and to enable a comparison of different detection approaches, we make our dataset ***PatchDB*** from our DSN'21 paper publicly available.
PatchDB is a large-scale security patch dataset that contains around 12,073 security patches and 23,742 non-security patches from the real world.
You can find more details on the dataset in the paper *"[PatchDB: A Large-Scale Security Patch Dataset](https://csis.gmu.edu/ksun/publications/dsn21_PatchDB.pdf)"*. You can also visit our [PatchDB official website](https://sunlab-gmu.github.io/PatchDB) for more information.
<font color="red">Please use your work emails to request for the dataset.</font> Typically, it takes no longer than 24 hours to get approval.
## Data Structure
PatchDB is stored in `json` format, where each sample contains 9 keys and has the following format.
```json
{
"category": the type of patch, value:"security" or "non-security",
"source": the source of patch, value: "cve" or "wild",
"CVE_ID": the CVE ID if it exists, value: "CVE-XXXX-XXXXX" or "NA",
"CWE_ID": the CWE ID if it exists, value: "cwe_id" or "NA"
"commit_id": the hash value of the commit, type: str,
"owner": the owner id of the repository, type: str,
"repo": the repository id, type: str,
"commit_message": the commit message part of the patch, type: str,
"diff_code": the diff code part of the patch, type: str
}
```
## Disclaimer & Download Agreement<span id="jump"></span>
To download the PatchDB dataset, you must agree with the items of the succeeding Disclaimer & Download Agreement. You should carefully read the following terms before submitting the PatchDB request form.
- PatchDB is constructed and cross-checked by 3 experts that work in security patch research.
Due to the potential misclassification led by subjective factors, the Sun Security Laboratory (SunLab) cannot guarantee a 100% accuracy for samples in the dataset.
- The copyright of the PatchDB dataset is owned by SunLab.
- The purpose of using PatchDB should be non-commercial research and/or personal use. The dataset should not be used for commercial use and any profitable purpose.
- The PatchDB dataset should not be re-selled or re-distributed. Anyone who has obtained PatchDB should not share the dataset with others without the permission from SunLab.
## Team
The PatchDB dataset is built by [Sun Security Laboratory](https://sunlab-gmu.github.io/) (SunLab) at [George Mason University](https://www2.gmu.edu/), Fairfax, VA.
## Citations
```bibtex
@inproceedings{wang2021PatchDB,
author={Wang, Xinda and Wang, Shu and Feng, Pengbin and Sun, Kun and Jajodia, Sushil},
booktitle={2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)},
title={PatchDB: A Large-Scale Security Patch Dataset},
year={2021},
volume={},
number={},
pages={149-160},
doi={10.1109/DSN48987.2021.00030}
}
``` |
mariakmurphy55/empty | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- legal
pretty_name: prettyname!
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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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## Dataset Card Contact
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MichaelJH/Ryu-AI_ryu-standardized_untokenized.datadict | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1107440
num_examples: 4107
download_size: 499332
dataset_size: 1107440
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
jorisdelorme/work_focus_website | ---
license: mit
---
|
Multimodal-Fatima/VQAv2_sample_validation_benchmarks | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 200179
num_examples: 10
download_size: 87070
dataset_size: 200179
---
# Dataset Card for "VQAv2_sample_validation_benchmarks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Data-Lab/classification_dialogue_search_v0.2 | ---
dataset_info:
features:
- name: query
dtype: string
- name: ner
dtype: string
- name: gold
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 924613
num_examples: 5742
download_size: 338455
dataset_size: 924613
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "classification_dialogue_search_v0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
seedboxai/gsm8k_de | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 151625
num_examples: 250
download_size: 90903
dataset_size: 151625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ZhangYuanhan/OmniBenchmark | ---
license: cc-by-nc-nd-4.0
---
|
Kal1510/gemma | ---
license: apache-2.0
---
|
byeonghwikim/hssd-hab | ---
language:
- en
pretty_name: HSSD
tags:
- 3D scenes
- Embodied AI
license: cc-by-nc-4.0
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: "You agree to use this dataset under the [CC BY-NC 4.0 license](https://creativecommons.org/licenses/by-nc/4.0/) terms"
viewer: false
---
HSSD: Habitat Synthetic Scenes Dataset
==================================
The [Habitat Synthetic Scenes Dataset (HSSD)](https://3dlg-hcvc.github.io/hssd/) is a human-authored 3D scene dataset that more closely mirrors real scenes than prior datasets.
Our dataset represents real interiors and contains a diverse set of 211 scenes and more than 18000 models of real-world objects.
<img src="https://i.imgur.com/XEkLxNs.png" width=50%>
This repository provides a Habitat consumption-ready compressed version of HSSD.
See [this repository](https://huggingface.co/datasets/hssd/hssd-models) for corresponding uncompressed assets.
## Dataset Structure
```
├── objects
│ ├── */*.glb
│ ├── */*.collider.glb
│ ├── */*.filteredSupportSurface(.ply|.glb)
│ ├── */*.object_config.json
├── stages
│ ├── *.glb
│ ├── *.stage_config.json
├── scenes
│ ├── *.scene_instance.json
├── scenes_uncluttered
│ ├── *.scene_instance.json
├── scene_filter_files
│ ├── *.rec_filter.json
└── hssd-hab.scene_dataset_config.json
└── hssd-hab-uncluttered.scene_dataset_config.json
```
- `hssd-hab.scene_dataset_config.json`: This SceneDataset config file aggregates the assets and metadata necessary to fully describe the set of stages, objects, and scenes constituting the dataset.
- `objects`: 3D models representing distinct objects that are used to compose scenes. Contains configuration files, render assets, collider assets, and Receptacle mesh assets.
- `stages`: A stage in Habitat is the set of static mesh components which make up the backdrop of a scene (e.g. floor, walls, stairs, etc.).
- `scenes`: A scene is a single 3D world composed of a static stage and a variable number of objects.
### Rearrange-ready assets:
Supporting Habitat 3.0 embodied rearrangement tasks with updated colliders, adjusted and de-cluttered scene contents, receptacle meshes, and receptacle filter files. See [aihabitat.org/habitat3/](aihabitat.org/habitat3/) for more details.
- `hssd-hab-uncluttered.scene_dataset_config.json`: This SceneDataset config file aggregates adds the adjusted and uncluttered scenes for rearrangement tasks.
- `scenes_uncluttered`: Contains the adjusted scene instance configuration files.
- `scene_filter_files`: A scene filter file organizes available Receptacle instances in a scene into active and inactive groups based on simualtion heuristics and manual edits. It is consumed by the RearrangeEpisodeGenerator to construct valid RearrangeEpisodeDatasets.
## Getting Started
To load HSSD scenes into the Habitat simulator, you can start by installing [habitat-sim](https://github.com/facebookresearch/habitat-sim) using instructions specified [here](https://github.com/facebookresearch/habitat-sim#installation).
Once installed, you can run the interactive Habitat viewer to load a scene:
```
habitat-viewer --dataset /path/to/hssd-hab/hssd-hab.scene_dataset_config.json -- 102344280
# or ./build/viewer if compiling from source
```
You can find more information about using the interactive viewer [here](https://github.com/facebookresearch/habitat-sim#testing:~:text=path/to/data/-,Interactive%20testing,-%3A%20Use%20the%20interactive).
Habitat-Sim is typically used with [Habitat-Lab](https://github.com/facebookresearch/habitat-lab), a modular high-level library for end-to-end experiments in embodied AI.
To define embodied AI tasks (e.g. navigation, instruction following, question answering), train agents, and benchmark their performance using standard metrics, you can download habitat-lab using the instructions provided [here](https://github.com/facebookresearch/habitat-lab#installation).
## Changelog
- `v0.2.5` (work in progress): **Rearrange-ready HSSD**
- Note: this is a checkpoint. Known issues exist and continued polish is ongoing.
- Adds Receptacle meshes describing support surfaces for small objects (e.g. table or shelf surfaces).
- Adds collider meshes (.collider.glb) for assets with Receptacle meshes to support simulation.
- Adds new scenes 'scenes_uncluttered' and new SceneDataset 'hssd-hab-uncluttered' containing adjusted and de-cluttered versions of the scenes for use in embodied rearrangement tasks.
- Adds 'scene_filter_files' which sort Receptacles in each scene into active and inactive groups for RearrangeEpisode generation.
- `v0.2.4`:
- Recompresses several object GLBs to preserve PBR material status.
- Adds CSV with object metadata and semantic lexicon files for Habitat.
- Adds train/val scene splits file.
- `v0.2.3`: First release.
|
stemsai/vocalset | ---
license: cc-by-4.0
---
|
joey234/affixal_negation_polarity_tmp | ---
dataset_info:
features:
- name: word
dtype: string
- name: neg_score
dtype: float64
- name: pos_score
dtype: float64
- name: label
dtype: int64
- name: checked
dtype: string
- name: thinh
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 273442
num_examples: 4144
download_size: 84067
dataset_size: 273442
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "affixal_negation_polarity_tmp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_california_sgosdt_l256_dim8_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 215960000
num_examples: 10000
- name: validation
num_bytes: 215960000
num_examples: 10000
download_size: 151409122
dataset_size: 431920000
---
# Dataset Card for "autotree_automl_10000_california_sgosdt_l256_dim8_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rntc/hal_sdv_fulltext | ---
dataset_info:
features:
- name: halid
dtype: string
- name: lang
dtype: string
- name: domain
sequence: string
- name: timestamp
dtype: string
- name: year
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6880552994.651032
num_examples: 44300
download_size: 3398418818
dataset_size: 6880552994.651032
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
result-kand2-sdxl-wuerst-karlo/76e05263 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 197
num_examples: 10
download_size: 1361
dataset_size: 197
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "76e05263"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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