datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
imnaveenk/necklace | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 58122109.0
num_examples: 21
download_size: 36885623
dataset_size: 58122109.0
---
# Dataset Card for "necklace"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shobhit654321-7/voice_project | ---
license: gfdl
---
|
AlekseyKorshuk/rl-bench-test-crowdsource | ---
dataset_info:
features:
- name: user_name
dtype: string
- name: bot_name
dtype: string
- name: memory
dtype: string
- name: prompt
dtype: string
- name: chat_history
list:
- name: message
dtype: string
- name: sender
dtype: string
splits:
- name: train
num_bytes: 292785
num_examples: 200
download_size: 190141
dataset_size: 292785
---
# Dataset Card for "rl-bench-test-crowdsource"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mikeriess/OpenAssistant2-NB | ---
dataset_info:
features:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: user_id
dtype: string
- name: created_date
dtype: string
- name: original_text
dtype: string
- name: role
dtype: string
- name: original_lang
dtype: string
- name: review_count
dtype: int64
- name: review_result
dtype: bool
- name: deleted
dtype: bool
- name: rank
dtype: float64
- name: synthetic
dtype: bool
- name: model_name
dtype: float64
- name: detoxify
dtype: string
- name: message_tree_id
dtype: string
- name: tree_state
dtype: string
- name: emojis
dtype: string
- name: labels
dtype: string
- name: text
dtype: string
- name: is_programming
dtype: string
- name: lang
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 103986309
num_examples: 53354
- name: valid
num_bytes: 5378439
num_examples: 2780
download_size: 44714793
dataset_size: 109364748
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
task_categories:
- question-answering
paper: https://arxiv.org/abs/2304.07327
license: apache-2.0
language:
- nb
---
# OpenAssistant2-NB
This dataset is a translated version of oassist2:
https://huggingface.co/datasets/OpenAssistant/oasst2
Please refer to the paper for a detailed description on the data:
https://arxiv.org/pdf/2304.07327.pdf
This dataset has been translated with SeamlessM4T, and subsequently filtered for conversations containing code.
__Procedure:__
1) Subset to only english quesitons (for consistency in translations)
2) Translate field 'text' with SeamlessM45-Large
3) Detect if there is code (Python, Java etc.) in each message (used Mistral-7B-instruct-v0.2 here)
4) Filter out messages (parent_id) with code in them
It is strongly suggested to do further quality assurance before using this data.
All credits goes to the __OpenAssistant__ team! |
distilled-from-one-sec-cv12/chunk_134 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1220661596
num_examples: 237853
download_size: 1247358332
dataset_size: 1220661596
---
# Dataset Card for "chunk_134"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tverous/test | ---
dataset_info:
features:
- name: uid
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: hyp_amr
dtype: string
- name: hyp_linearized_amr
dtype: string
splits:
- name: train
num_bytes: 5344233
num_examples: 14740
download_size: 1790710
dataset_size: 5344233
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LimeryJorge/Iris-Image | ---
license: apache-2.0
---
|
nbalepur/UnifiedQA_MCQA | ---
configs:
- config_name: default
data_files:
- split: full
path: data/full-*
- split: train
path: data/train-*
- split: eval_only
path: data/eval_only-*
- split: eval_subset
path: data/eval_subset-*
- split: cheat_proof
path: data/cheat_proof-*
dataset_info:
features:
- name: question
dtype: string
- name: answer_letter
dtype: string
- name: dataset
dtype: string
- name: choices
sequence: string
splits:
- name: full
num_bytes: 16696558
num_examples: 81884
- name: train
num_bytes: 12224
num_examples: 60
- name: eval_only
num_bytes: 1634360
num_examples: 7611
- name: eval_subset
num_bytes: 132241
num_examples: 820
- name: cheat_proof
num_bytes: 93406
num_examples: 820
download_size: 9297393
dataset_size: 18568789
---
# Dataset Card for "UnifiedQA_MCQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MarkusStoll/cifar10-enrichments | ---
dataset_info:
features:
- name: embedding
sequence: float32
splits:
- name: test
num_bytes: 30760000
num_examples: 10000
download_size: 36966542
dataset_size: 30760000
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "cifar10-enrichments"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Jing24/seperate_all_sub0 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 71282755
num_examples: 78391
download_size: 13012921
dataset_size: 71282755
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "seperate_all_sub0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
C10H/SBDD-model | ---
license: mit
---
|
gabriel10sr/skitchura | ---
license: openrail
---
|
breno30/BandidoFavorito | ---
license: openrail
---
|
Falah/new_photorealistic_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 1492287
num_examples: 10000
download_size: 345550
dataset_size: 1492287
---
# Dataset Card for "new_photorealistic_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FINNUMBER/FINCH_TRAIN_NQA_EXT_400_NEWFORMAT | ---
dataset_info:
features:
- name: task
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1626276
num_examples: 400
download_size: 939271
dataset_size: 1626276
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-64000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 662626
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rcugarte/font_training | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 71440845.948
num_examples: 1449
download_size: 68625549
dataset_size: 71440845.948
---
# Dataset Card for "font_training_data_new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
gigant/tib_dependency_graphs | ---
dataset_info:
features:
- name: id
dtype: int64
- name: receivers
sequence: uint16
- name: senders
sequence: uint16
- name: graph_mask
sequence: bool
splits:
- name: train
num_bytes: 1896216603
num_examples: 7251
- name: valid
num_bytes: 235053230
num_examples: 904
- name: test
num_bytes: 238694116
num_examples: 910
download_size: 2118206109
dataset_size: 2369963949
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
corbyrosset/researchy_questions | ---
license: cdla-permissive-2.0
task_categories:
- question-answering
language:
- en
---
# Introduction
[Researchy Questions](https://arxiv.org/abs/2402.17896) is a set of about 100k Bing queries that users spent the most effort on. After a labor-intensive filtering funnel from billions of queries, these "needles in the haystack" are non-factoid, multi-perspective questions that probably require a lot of sub-questions and research in order to answer adequetly. These questions are shown to be harder than other open domain QA datasets like Natural Questions.
The train dataset has about 90k samples.
# Use Cases
We provide the dataset as-is without any code or specific evaluation criteria.
For retrieval-augmented generation (RAG), the intent would to at least use the content of the clicked documents in the DocStream to ground an LLM's response to the question. Alternatively, you can issue the queries in the queries field to a search engine api and use the retrieved documents for grounding. In both cases, the intended evaluation would be a side-by-side LLM-as-a-judge to compare your candidate output to e.g. a closed-book reference output from GPT-4. This is an open project we invite the community to take on.
For ranking/retrieval evaluation, ideally, you would have access to the [Clueweb22](https://arxiv.org/abs/2211.15848) corpus and retrieve from the whole index of 10B urls and report MRR/NDCG etc. The click preferences in the DocStream are normalized to be a probability distribution and can be used as labels for relevance gain.
# Example
Each row corresponds to a user-issued question.
- **intrinsic_scores** are a set of 8 dimensions of intrinsic qualities of the question, each scored on a scale 1-10 by GPT-4
- **DocStream** is the ordered list of clicked documents from the Clueweb22 corpus, ordered by decreasing click preference. Within each Docstream entry you will find:
- **CluewebURLHash** you should be able to easily join on this key in the Clueweb22 corpus.
- **Click_Cnt** a normalized distribution of the clicks for this query aggregated across all users.
- **gpt4_decomposition** is how GPT-4 would decompose the question into sub-questions in order to provide an answer. The intent is to help retrieval-augmented answering (RAG) systems ask the right sub-questions to aid their research. This decomposition was generated "closed book" meaning GPT-4 did not know which documents were clicked on for the question.
- **queries** a list of queries that GPT-4 thought should be issued to a search engine to find more grounding documents.
- **decompositional_score** the output of our decompositional classifier, used for filtering the questions. The minimum value is 0.6
- **nonfactoid_score** output of the nonfactoid classifier, used for filtering the questions. The minimum value is 0.75.
```
{
"id": "1004841",
"question": "how does branding benefit consumers and marketers?",
"intrinsic_scores": {
"ambiguous": 0,
"incompleteness": 0,
"assumptive": 0,
"multi-faceted": 7,
"knowledge-intensive": 5,
"subjective": 3,
"reasoning-intensive": 6,
"harmful": 0
},
"DocStream": [
{
"Url": "https://chegg.com/homework-help/questions-and-answers/branding-benefit-consumers-marketers-q3328798",
"CluewebURLHash": "B592AB8F6A32E1026DE28DFF517CF1BE",
"UrlLanguage": "en",
"Title": "Solved: How Does Branding Benefit Consumers And Marketers ...",
"Snippet": "How does branding benefit consumers and marketers? Best Answer 100% (1 rating) Almost every business has a trading name, from the smallest market trader to the largest multi-national corporation. Only a minority of those businesses however, have what could be classed as a brand. view the full answer.",
"Click_Cnt": 0.625
},
{
"Url": "https://coursehero.com/tutors-problems/marketing/11098568-how-does-branding-benefit-consumers-and-marketers",
"CluewebURLHash": "D6F224DA6AAA4DF42F75BBDC6A96C44E",
"UrlLanguage": "en",
"Title": "[Solved] how does branding benefit consumers and marketers ...",
"Snippet": "How does branding benefit consumers and marketers. Benefits to consumers. 1. Saves time on shopping due to easy identification. 2. Branding is often associated with quality products hence consumers benefit from quality products. 3. Stability in prices as most branded products have fixed prices. Benefits to marketers.",
"Click_Cnt": 0.25
},
{
"Url": "https://notesmatic.com/benefits-of-branding-for-consumers-suppliers-and-the-society",
"CluewebURLHash": "8CB9FCA9B0C87659EAD15F5FB291BEC9",
"UrlLanguage": "en",
"Title": "Benefits of Branding for Consumers, Suppliers, and the ...",
"Snippet": "Benefits of branding for the buyer: It is a sign of quality and makes the selection easier for the buyer. Those who buy the same brand each time can expect to have the same quality every time they buy. It makes shopping easier for the buyer. Suppose you want to buy toothpaste and do not remember any brands.",
"Click_Cnt": 0.125
}
],
"gpt4_decomposition": {
"llm": "gpt4",
"type": "closed-book-decomposition",
"headers": [
[
"What is branding and how is it defined in marketing?"
],
[
"What are the main components or elements of branding?"
],
[
"What are the benefits of branding for consumers?",
" - How does branding help consumers identify and differentiate products or services?",
" - How does branding influence consumer perception, preference, and loyalty?",
" - How does branding provide consumers with value, satisfaction, and trust?"
],
[
"What are the benefits of branding for marketers?",
" - How does branding help marketers create and communicate a unique identity and position in the market?",
" - How does branding enhance marketer's reputation, credibility, and authority?",
" - How does branding increase marketer's competitive advantage, customer retention, and profitability?"
]
],
"subquestions": [
"- What is branding and how is it defined in marketing?",
"- What are the main components or elements of branding?",
"- What are the benefits of branding for consumers?",
" - How does branding help consumers identify and differentiate products or services?",
" - How does branding influence consumer perception, preference, and loyalty?",
" - How does branding provide consumers with value, satisfaction, and trust?",
"- What are the benefits of branding for marketers?",
" - How does branding help marketers create and communicate a unique identity and position in the market?",
" - How does branding enhance marketer's reputation, credibility, and authority?",
" - How does branding increase marketer's competitive advantage, customer retention, and profitability?"
],
"queries": [
"what is branding in marketing",
"components or elements of branding",
"benefits of branding for consumers",
"branding and consumer identification and differentiation",
"branding and consumer perception, preference, and loyalty",
"branding and consumer value, satisfaction, and trust",
"benefits of branding for marketers",
"branding and marketer's identity and position",
"branding and marketer's reputation, credibility, and authority",
"branding and marketer's competitive advantage, customer retention, and profitability"
]
},
"decompositional_score": 0.709,
"nonfactoid_score": 1.018
}
```
# Citation
If you use this dataset or find the insights from the paper to be helpful, please cite:
```
@misc{rosset2024researchy,
title={Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents},
author={Corby Rosset and Ho-Lam Chung and Guanghui Qin and Ethan C. Chau and Zhuo Feng and Ahmed Awadallah and Jennifer Neville and Nikhil Rao},
year={2024},
eprint={2402.17896},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
CyberHarem/wakaba_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of wakaba/若葉 (Kantai Collection)
This is the dataset of wakaba/若葉 (Kantai Collection), containing 204 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 204 | 128.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakaba_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 204 | 92.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakaba_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 397 | 181.43 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakaba_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 204 | 120.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakaba_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 397 | 225.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wakaba_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/wakaba_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, black_skirt, blazer, collared_shirt, long_sleeves, pleated_skirt, red_necktie, school_uniform, solo, white_shirt, black_pantyhose, looking_at_viewer, simple_background, white_background |
| 1 | 8 |  |  |  |  |  | 1girl, black_pantyhose, blazer, necktie, pleated_skirt, school_uniform, shirt, solo, machinery, turret, cannon, character_name, looking_at_viewer, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_skirt | blazer | collared_shirt | long_sleeves | pleated_skirt | red_necktie | school_uniform | solo | white_shirt | black_pantyhose | looking_at_viewer | simple_background | white_background | necktie | shirt | machinery | turret | cannon | character_name | open_mouth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:---------|:-----------------|:---------------|:----------------|:--------------|:-----------------|:-------|:--------------|:------------------|:--------------------|:--------------------|:-------------------|:----------|:--------|:------------|:---------|:---------|:-----------------|:-------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | X | | | X | | X | X | | X | X | | | X | X | X | X | X | X | X |
|
bigbio/ehr_rel |
---
language:
- en
bigbio_language:
- English
license: apache-2.0
multilinguality: monolingual
bigbio_license_shortname: APACHE_2p0
pretty_name: EHR-Rel
homepage: https://github.com/babylonhealth/EHR-Rel
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for EHR-Rel
## Dataset Description
- **Homepage:** https://github.com/babylonhealth/EHR-Rel
- **Pubmed:** False
- **Public:** True
- **Tasks:** STS
EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more
than the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work,
the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.
A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets.
## Citation Information
```
@inproceedings{schulz-etal-2020-biomedical,
title = {Biomedical Concept Relatedness {--} A large {EHR}-based benchmark},
author = {Schulz, Claudia and
Levy-Kramer, Josh and
Van Assel, Camille and
Kepes, Miklos and
Hammerla, Nils},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
month = {dec},
year = {2020},
address = {Barcelona, Spain (Online)},
publisher = {International Committee on Computational Linguistics},
url = {https://aclanthology.org/2020.coling-main.577},
doi = {10.18653/v1/2020.coling-main.577},
pages = {6565--6575},
}
```
|
Nyaaneet/SynthDog-RU_EN-prepared | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
- name: lang
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 678666738.82
num_examples: 2430
- name: train
num_bytes: 2677611433.18
num_examples: 9570
download_size: 3352860165
dataset_size: 3356278172.0
---
# Dataset Card for "SynthDog-RU_EN-prepared"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shidowake/cosmopedia-japanese-subset_from_aixsatoshi_filtered-sharegpt-format-no-system-prompt_split_2 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 19834076.0
num_examples: 2495
download_size: 11996442
dataset_size: 19834076.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mfidabel/dscovr_kp_data_2016_2023 | ---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: TIMESTAMP
dtype: string
- name: KP_GRAL
dtype: float64
- name: GSE_X
dtype: float64
- name: GSE_Y
dtype: float64
- name: GSE_Z
dtype: float64
- name: CUADRADO
dtype: float64
- name: RAW_4
dtype: float64
- name: RAW_5
dtype: float64
- name: RAW_6
dtype: float64
- name: RAW_7
dtype: float64
- name: RAW_8
dtype: float64
- name: RAW_9
dtype: float64
- name: RAW_10
dtype: float64
- name: RAW_11
dtype: float64
- name: RAW_12
dtype: float64
- name: RAW_13
dtype: float64
- name: RAW_14
dtype: float64
- name: RAW_15
dtype: float64
- name: RAW_16
dtype: float64
- name: RAW_17
dtype: float64
- name: RAW_18
dtype: float64
- name: RAW_19
dtype: float64
- name: RAW_20
dtype: float64
- name: RAW_21
dtype: float64
- name: RAW_22
dtype: float64
- name: RAW_23
dtype: float64
- name: RAW_24
dtype: float64
splits:
- name: train
num_bytes: 762145515
num_examples: 3277440
download_size: 498355302
dataset_size: 762145515
---
|
111wwwww/EODIPOOWO | ---
license: isc
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/c5e1ddfc | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1330
dataset_size: 182
---
# Dataset Card for "c5e1ddfc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allandclive/Luganda_Sci-Math-Bio_Translations | ---
license: cc-by-4.0
task_categories:
- text2text-generation
- translation
language:
- lg
- en
tags:
- medical
- biology
- math
- science
size_categories:
- 1K<n<10K
---
# Luganda Sci-Math-Bio Translations
This dataset contains Luganda and English translations of biologicial, mathematical and scientific terms |
nliew/767 | ---
license: mit
---
|
joelak07/test | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 655680.0
num_examples: 80
- name: test
num_bytes: 73764.0
num_examples: 9
download_size: 293906
dataset_size: 729444.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_completive_have_done | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 295958
num_examples: 1226
- name: dev_mismatched
num_bytes: 385077
num_examples: 1509
- name: test_matched
num_bytes: 302183
num_examples: 1199
- name: test_mismatched
num_bytes: 387519
num_examples: 1541
- name: train
num_bytes: 11778885
num_examples: 48515
download_size: 8193165
dataset_size: 13149622
---
# Dataset Card for "MULTI_VALUE_mnli_completive_have_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
absr/XodifierXLLM | ---
license: c-uda
---
|
navneeth-hr/reuters_articles | ---
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: 10073414
dataset_size: 17042155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
skar01/cool_new_dataset | ---
dataset_info:
features:
- name: name
dtype: string
- name: description
dtype: string
- name: ad
dtype: string
splits:
- name: train
num_bytes: 3455
num_examples: 5
download_size: 7984
dataset_size: 3455
---
# Dataset Card for "cool_new_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pphuc25/baivanhay | ---
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 60911721
num_examples: 9913
download_size: 30468207
dataset_size: 60911721
---
# Dataset Card for "baivanhay"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mole-code/langchain-data | ---
dataset_info:
features:
- name: code
dtype: string
- name: apis
sequence: string
- name: extract_api
dtype: string
splits:
- name: train
num_bytes: 12537281
num_examples: 1228
download_size: 2888103
dataset_size: 12537281
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Sajt/dataset_horv_ger | ---
license: unknown
---
|
autoevaluate/autoeval-eval-project-samsum-61336320-1319050351 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: facebook/bart-large-xsum
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@hgoyal194](https://huggingface.co/hgoyal194) for evaluating this model. |
SEIKU/Llama-2-7b-reading-test2 | ---
license: apache-2.0
---
|
one-sec-cv12/chunk_250 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 16532262000.375
num_examples: 172125
download_size: 15176658045
dataset_size: 16532262000.375
---
# Dataset Card for "chunk_250"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aashsach/multiconer2 | ---
dataset_info:
- config_name: bn
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 3844480
num_examples: 9708
- name: validation
num_bytes: 199756
num_examples: 507
download_size: 4017205
dataset_size: 4044236
- config_name: de
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 2724923
num_examples: 9785
- name: validation
num_bytes: 137726
num_examples: 512
download_size: 2831813
dataset_size: 2862649
- config_name: en
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4448839
num_examples: 16778
- name: validation
num_bytes: 232735
num_examples: 871
download_size: 4575462
dataset_size: 4681574
- config_name: es
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4643093
num_examples: 16453
- name: validation
num_bytes: 237306
num_examples: 854
download_size: 4659064
dataset_size: 4880399
- config_name: fa
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 5861165
num_examples: 16321
- name: validation
num_bytes: 316929
num_examples: 855
download_size: 5760501
dataset_size: 6178094
- config_name: fr
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4375159
num_examples: 16548
- name: validation
num_bytes: 229499
num_examples: 857
download_size: 4492163
dataset_size: 4604658
- config_name: hi
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4039051
num_examples: 9632
- name: validation
num_bytes: 217741
num_examples: 514
download_size: 4060184
dataset_size: 4256792
- config_name: it
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4256854
num_examples: 16579
- name: validation
num_bytes: 219489
num_examples: 858
download_size: 4454712
dataset_size: 4476343
- config_name: pt
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 4587908
num_examples: 16469
- name: validation
num_bytes: 233471
num_examples: 854
download_size: 4622334
dataset_size: 4821379
- config_name: sv
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 3919442
num_examples: 16363
- name: validation
num_bytes: 205910
num_examples: 856
download_size: 4100785
dataset_size: 4125352
- config_name: uk
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 5104234
num_examples: 16429
- name: validation
num_bytes: 261125
num_examples: 851
download_size: 5245683
dataset_size: 5365359
- config_name: zh
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-AerospaceManufacturer
'2': I-AerospaceManufacturer
'3': B-AnatomicalStructure
'4': I-AnatomicalStructure
'5': B-ArtWork
'6': I-ArtWork
'7': B-Artist
'8': I-Artist
'9': B-Athlete
'10': I-Athlete
'11': B-CarManufacturer
'12': I-CarManufacturer
'13': B-Cleric
'14': I-Cleric
'15': B-Clothing
'16': I-Clothing
'17': B-Disease
'18': I-Disease
'19': B-Drink
'20': I-Drink
'21': B-Facility
'22': I-Facility
'23': B-Food
'24': I-Food
'25': B-HumanSettlement
'26': I-HumanSettlement
'27': B-MedicalProcedure
'28': I-MedicalProcedure
'29': B-Medication/Vaccine
'30': I-Medication/Vaccine
'31': B-MusicalGRP
'32': I-MusicalGRP
'33': B-MusicalWork
'34': I-MusicalWork
'35': B-ORG
'36': I-ORG
'37': B-OtherLOC
'38': I-OtherLOC
'39': B-OtherPER
'40': I-OtherPER
'41': B-OtherPROD
'42': I-OtherPROD
'43': B-Politician
'44': I-Politician
'45': B-PrivateCorp
'46': I-PrivateCorp
'47': B-PublicCorp
'48': I-PublicCorp
'49': B-Scientist
'50': I-Scientist
'51': B-Software
'52': I-Software
'53': B-SportsGRP
'54': I-SportsGRP
'55': B-SportsManager
'56': I-SportsManager
'57': B-Station
'58': I-Station
'59': B-Symptom
'60': I-Symptom
'61': B-Vehicle
'62': I-Vehicle
'63': B-VisualWork
'64': I-VisualWork
'65': B-WrittenWork
'66': I-WrittenWork
splits:
- name: train
num_bytes: 3816980
num_examples: 9759
- name: validation
num_bytes: 198669
num_examples: 506
download_size: 3935986
dataset_size: 4015649
---
|
open-llm-leaderboard/details_sethuiyer__MedleyMD | ---
pretty_name: Evaluation run of sethuiyer/MedleyMD
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [sethuiyer/MedleyMD](https://huggingface.co/sethuiyer/MedleyMD) 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_sethuiyer__MedleyMD\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-15T17:31:54.252170](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__MedleyMD/blob/main/results_2024-01-15T17-31-54.252170.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\": 0.6540922017849786,\n\
\ \"acc_stderr\": 0.03190059639989504,\n \"acc_norm\": 0.6547986122363706,\n\
\ \"acc_norm_stderr\": 0.032550018643590924,\n \"mc1\": 0.3635250917992656,\n\
\ \"mc1_stderr\": 0.016838862883965834,\n \"mc2\": 0.5246301216405119,\n\
\ \"mc2_stderr\": 0.015222279804788318\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6279863481228669,\n \"acc_stderr\": 0.014124597881844461,\n\
\ \"acc_norm\": 0.6646757679180887,\n \"acc_norm_stderr\": 0.013796182947785562\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6711810396335391,\n\
\ \"acc_stderr\": 0.004688239419302074,\n \"acc_norm\": 0.8605855407289384,\n\
\ \"acc_norm_stderr\": 0.003456706038054756\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.64,\n\
\ \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.037161774375660185,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.037161774375660185\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\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\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6042553191489362,\n \"acc_stderr\": 0.031967586978353627,\n\
\ \"acc_norm\": 0.6042553191489362,\n \"acc_norm_stderr\": 0.031967586978353627\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5350877192982456,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.5350877192982456,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\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\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.0253795249107784,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.0253795249107784\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8032258064516129,\n\
\ \"acc_stderr\": 0.022616409420742025,\n \"acc_norm\": 0.8032258064516129,\n\
\ \"acc_norm_stderr\": 0.022616409420742025\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.032876667586034906,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.032876667586034906\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.02962022787479049,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.02962022787479049\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\": 0.6743589743589744,\n \"acc_stderr\": 0.02375966576741229,\n \
\ \"acc_norm\": 0.6743589743589744,\n \"acc_norm_stderr\": 0.02375966576741229\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37777777777777777,\n \"acc_stderr\": 0.029560707392465715,\n \
\ \"acc_norm\": 0.37777777777777777,\n \"acc_norm_stderr\": 0.029560707392465715\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8513761467889909,\n \"acc_stderr\": 0.015251253773660834,\n \"\
acc_norm\": 0.8513761467889909,\n \"acc_norm_stderr\": 0.015251253773660834\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8227848101265823,\n \"acc_stderr\": 0.024856364184503217,\n \
\ \"acc_norm\": 0.8227848101265823,\n \"acc_norm_stderr\": 0.024856364184503217\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.030769352008229136,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.030769352008229136\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624734,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624734\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.03457272836917671,\n \"\
acc_norm\": 0.8264462809917356,\n \"acc_norm_stderr\": 0.03457272836917671\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993457,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993457\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7514450867052023,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.7514450867052023,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.40893854748603353,\n\
\ \"acc_stderr\": 0.016442830654715544,\n \"acc_norm\": 0.40893854748603353,\n\
\ \"acc_norm_stderr\": 0.016442830654715544\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.02495418432487991,\n\
\ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.02495418432487991\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7438271604938271,\n \"acc_stderr\": 0.024288533637726095,\n\
\ \"acc_norm\": 0.7438271604938271,\n \"acc_norm_stderr\": 0.024288533637726095\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\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\": 0.4771838331160365,\n\
\ \"acc_stderr\": 0.0127569333828237,\n \"acc_norm\": 0.4771838331160365,\n\
\ \"acc_norm_stderr\": 0.0127569333828237\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6748366013071896,\n \"acc_stderr\": 0.018950886770806315,\n \
\ \"acc_norm\": 0.6748366013071896,\n \"acc_norm_stderr\": 0.018950886770806315\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.726530612244898,\n \"acc_stderr\": 0.028535560337128438,\n\
\ \"acc_norm\": 0.726530612244898,\n \"acc_norm_stderr\": 0.028535560337128438\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\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\": 0.5240963855421686,\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\": 0.8596491228070176,\n \"acc_stderr\": 0.026640582539133196,\n\
\ \"acc_norm\": 0.8596491228070176,\n \"acc_norm_stderr\": 0.026640582539133196\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3635250917992656,\n\
\ \"mc1_stderr\": 0.016838862883965834,\n \"mc2\": 0.5246301216405119,\n\
\ \"mc2_stderr\": 0.015222279804788318\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8026835043409629,\n \"acc_stderr\": 0.011185026389050372\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6899166034874905,\n \
\ \"acc_stderr\": 0.01274030571737627\n }\n}\n```"
repo_url: https://huggingface.co/sethuiyer/MedleyMD
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|arc:challenge|25_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|gsm8k|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hellaswag|10_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-15T17-31-54.252170.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-15T17-31-54.252170.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- '**/details_harness|winogrande|5_2024-01-15T17-31-54.252170.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-15T17-31-54.252170.parquet'
- config_name: results
data_files:
- split: 2024_01_15T17_31_54.252170
path:
- results_2024-01-15T17-31-54.252170.parquet
- split: latest
path:
- results_2024-01-15T17-31-54.252170.parquet
---
# Dataset Card for Evaluation run of sethuiyer/MedleyMD
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sethuiyer/MedleyMD](https://huggingface.co/sethuiyer/MedleyMD) 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_sethuiyer__MedleyMD",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-15T17:31:54.252170](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__MedleyMD/blob/main/results_2024-01-15T17-31-54.252170.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": 0.6540922017849786,
"acc_stderr": 0.03190059639989504,
"acc_norm": 0.6547986122363706,
"acc_norm_stderr": 0.032550018643590924,
"mc1": 0.3635250917992656,
"mc1_stderr": 0.016838862883965834,
"mc2": 0.5246301216405119,
"mc2_stderr": 0.015222279804788318
},
"harness|arc:challenge|25": {
"acc": 0.6279863481228669,
"acc_stderr": 0.014124597881844461,
"acc_norm": 0.6646757679180887,
"acc_norm_stderr": 0.013796182947785562
},
"harness|hellaswag|10": {
"acc": 0.6711810396335391,
"acc_stderr": 0.004688239419302074,
"acc_norm": 0.8605855407289384,
"acc_norm_stderr": 0.003456706038054756
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.037161774375660185,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.037161774375660185
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"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": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6042553191489362,
"acc_stderr": 0.031967586978353627,
"acc_norm": 0.6042553191489362,
"acc_norm_stderr": 0.031967586978353627
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5350877192982456,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.5350877192982456,
"acc_norm_stderr": 0.046920083813689104
},
"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": 0.41534391534391535,
"acc_stderr": 0.0253795249107784,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.0253795249107784
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677172,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677172
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8032258064516129,
"acc_stderr": 0.022616409420742025,
"acc_norm": 0.8032258064516129,
"acc_norm_stderr": 0.022616409420742025
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.032876667586034906,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.032876667586034906
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.02962022787479049,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.02962022787479049
},
"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": 0.6743589743589744,
"acc_stderr": 0.02375966576741229,
"acc_norm": 0.6743589743589744,
"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37777777777777777,
"acc_stderr": 0.029560707392465715,
"acc_norm": 0.37777777777777777,
"acc_norm_stderr": 0.029560707392465715
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8513761467889909,
"acc_stderr": 0.015251253773660834,
"acc_norm": 0.8513761467889909,
"acc_norm_stderr": 0.015251253773660834
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8227848101265823,
"acc_stderr": 0.024856364184503217,
"acc_norm": 0.8227848101265823,
"acc_norm_stderr": 0.024856364184503217
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6995515695067265,
"acc_stderr": 0.030769352008229136,
"acc_norm": 0.6995515695067265,
"acc_norm_stderr": 0.030769352008229136
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624734,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624734
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8264462809917356,
"acc_stderr": 0.03457272836917671,
"acc_norm": 0.8264462809917356,
"acc_norm_stderr": 0.03457272836917671
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.03760178006026621,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.03760178006026621
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.020588491316092375,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.020588491316092375
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993457,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993457
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7514450867052023,
"acc_stderr": 0.023267528432100174,
"acc_norm": 0.7514450867052023,
"acc_norm_stderr": 0.023267528432100174
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.40893854748603353,
"acc_stderr": 0.016442830654715544,
"acc_norm": 0.40893854748603353,
"acc_norm_stderr": 0.016442830654715544
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7450980392156863,
"acc_stderr": 0.02495418432487991,
"acc_norm": 0.7450980392156863,
"acc_norm_stderr": 0.02495418432487991
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.707395498392283,
"acc_stderr": 0.02583989833487798,
"acc_norm": 0.707395498392283,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7438271604938271,
"acc_stderr": 0.024288533637726095,
"acc_norm": 0.7438271604938271,
"acc_norm_stderr": 0.024288533637726095
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4771838331160365,
"acc_stderr": 0.0127569333828237,
"acc_norm": 0.4771838331160365,
"acc_norm_stderr": 0.0127569333828237
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6838235294117647,
"acc_stderr": 0.028245687391462927,
"acc_norm": 0.6838235294117647,
"acc_norm_stderr": 0.028245687391462927
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6748366013071896,
"acc_stderr": 0.018950886770806315,
"acc_norm": 0.6748366013071896,
"acc_norm_stderr": 0.018950886770806315
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.726530612244898,
"acc_stderr": 0.028535560337128438,
"acc_norm": 0.726530612244898,
"acc_norm_stderr": 0.028535560337128438
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"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": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8596491228070176,
"acc_stderr": 0.026640582539133196,
"acc_norm": 0.8596491228070176,
"acc_norm_stderr": 0.026640582539133196
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3635250917992656,
"mc1_stderr": 0.016838862883965834,
"mc2": 0.5246301216405119,
"mc2_stderr": 0.015222279804788318
},
"harness|winogrande|5": {
"acc": 0.8026835043409629,
"acc_stderr": 0.011185026389050372
},
"harness|gsm8k|5": {
"acc": 0.6899166034874905,
"acc_stderr": 0.01274030571737627
}
}
```
## 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] |
mask-distilled-one-sec-cv12/chunk_12 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 996086856
num_examples: 195618
download_size: 1015430710
dataset_size: 996086856
---
# Dataset Card for "chunk_12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zicsx/Indus_indic-align-hindi | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12942080417
num_examples: 13310858
download_size: 2266604934
dataset_size: 12942080417
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Indus_indic-align-hindi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/gov2_trec-tb-2005_efficiency | ---
pretty_name: '`gov2/trec-tb-2005/efficiency`'
viewer: false
source_datasets: ['irds/gov2']
task_categories:
- text-retrieval
---
# Dataset Card for `gov2/trec-tb-2005/efficiency`
The `gov2/trec-tb-2005/efficiency` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/gov2#gov2/trec-tb-2005/efficiency).
# Data
This dataset provides:
- `queries` (i.e., topics); count=50,000
- `qrels`: (relevance assessments); count=45,291
- For `docs`, use [`irds/gov2`](https://huggingface.co/datasets/irds/gov2)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/gov2_trec-tb-2005_efficiency', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/gov2_trec-tb-2005_efficiency', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Clarke2005TrecTerabyte,
title={The TREC 2005 Terabyte Track},
author={Charles L. A. Clark and Falk Scholer and Ian Soboroff},
booktitle={TREC},
year={2005}
}
```
|
VanessaSchenkel/pt-all-words | ---
annotations_creators:
- no-annotation
language:
- pt
language_creators:
- expert-generated
license: []
multilinguality:
- monolingual
paperswithcode_id: sbwce
pretty_name: "Dicion\xE1rio em Portugu\xEAs"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for Dicionário Português
It is a list of portuguese words with its inflections
How to use it:
```
from datasets import load_dataset
remote_dataset = load_dataset("VanessaSchenkel/pt-all-words")
remote_dataset
```
|
yasirfaizahmed/good_tweet_bat_tweet | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- not-for-all-audiences
pretty_name: good_tweet_bad_tweet
size_categories:
- 1K<n<10K
--- |
A2H0H0R1/alpaca_data_gpt4_2 | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_mnli_a_participle | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 1053190
num_examples: 4470
- name: dev_mismatched
num_bytes: 1111149
num_examples: 4480
- name: test_matched
num_bytes: 1035879
num_examples: 4368
- name: test_mismatched
num_bytes: 1097868
num_examples: 4467
- name: train
num_bytes: 41978659
num_examples: 176336
download_size: 30217846
dataset_size: 46276745
---
# Dataset Card for "MULTI_VALUE_mnli_a_participle"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
somosnlp/RecetasDeLaAbuela | ---
license: openrail
task_categories:
- question-answering
- summarization
language:
- es
pretty_name: RecetasDeLaAbuel@
size_categories:
- 10K<n<100K
tags:
- recipes
- cooking
- recetas
- cocina
configs:
- config_name: version_inicial
data_files: "recetasdelaabuela.csv"
- config_name: version_1
data_files: "main.csv"
---
# Motivación inicial
<!-- Motivation for the creation of this dataset. -->
Este corpus ha sido creado durante el Hackathon SomosNLP Marzo 2024: #Somos600M (https://somosnlp.org/hackathon).
Responde a una de las propuestas somosnlp sobre 'Recetas típicas por país/zona geográfica'.
# Nombre del Proyecto
<!-- Provide a quick summary of the dataset. -->
Este corpus o dataset se llama 'RecetasDeLaAbuel@' y es un homenaje a todas nuestr@s abuel@s que nos han enseñado a cocinar. Se trata de la mayor y más completa colección de recetas open-source en español de países hispanoamericanos.
<p align="left">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6456c6184095c967f9ace04d/h5GG5ht9r9HJCvJbuetRO.png" alt="Mi abuela cocinando" width="323">
</p>
## Corpus
## Descripción
<!-- Provide a longer summary of what this dataset is. -->
Este corpus contiene los principales elementos de una receta de cocina (título, descripción, ingredientes y preparación). Se ha completado con otros 10 atributos
hasta completar un impresionante dataset con más de 280k (20k x 14) elementos (6M palabras y 40M caracteres).
- **Curated by:** iXrst
- **Funded by:** rovi27, sbenel, GaboTuco, iXrst
- **Language(s) (NLP):** Python
- **License:** openrail
### Estructura
<!-- 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. -->
Este dataset 'RecetasDeLaAbuel@' tiene formato tabular (20k x 14). Cada fila de datos tiene los siguientes atributos:
1. Id: Identificador numérico.
2. Nombre: Nombre de la receta.
3. URL: Origen web.
4. Ingredientes: Alimentos usados.
5. Pasos: Pasos de preparación.
6. País: Código ISO_A3/país originario de la receta.
7. Duracion (HH:MM): Tiempo estimado de preparación.
8. Categoria: Tipo de receta (ej. vegetarianos, pastas, salsas, postres, cerdo, pollo etc).
9. Contexto: Entorno de uso/consumo o contexto de la receta.
10. Valoracion y Votos: Valoración 1-5 y número de votos.
11. Comensales: Número de raciones.
12. Tiempo: Tiempo del plato (ej: Desayuno, entrante, principal, acompañamiento, etc.)
13. Dificultad: Grado de dificultad (alto/medio/bajo)
14. Valor nutricional: Características básicas: 1) Nivel calorías/sodio (alto/medio/bajo), 2) Ausencia de grasas/grasas trans/colesterol/azúcar y 3) Nivel de fibra.
### Fuentes de datos
<!-- 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. -->
La información básica se ha recolectado y procesado mediante las técnicas conocidas como 'web scrapping'.
La información original se ha recopilado de diferentes páginas web:
- Recetas gratis de cocina
- Cocina peruana
- Cocina mexicana
- Cocina colombiana
Ponganse en contacto con nosotros para incluir recetas de su país, por favor!
Para más información sobre recetas de cocina dirijanse a la fuente original. Expresamos nuestro reconocimiento y agradecimiento a sus autores.
### Procesamiento de datos
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
Se utilizó scripts de Python para hacer el procesamiento del corpus, y las funciones de limpieza y curación del dataset.
** https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela/blob/main/stats.pdf
### Estadísticas
Son 20447 registros de recetas.
** https://github.com/recetasdelaabuela/somosnlp/blob/main/Docs/Stats.pdf
## Política de Uso
<!-- Address questions around how the dataset is intended to be used. -->
### Uso directo
<!-- This section describes suitable use cases for the dataset. -->
Nuestra Misión es la creación del mejor asistente de cocina inteligente específico del idioma español (corpus Recetas de la Abuel@) que agrupe recetas de países hispanoamericanos
y permita mejorar nuestra relación con la preparación y el cocinado de los alimentos.
Nuestra IA responderá a cuestiones de los sigientes tipos:
'Dime la receta del ceviche, frijoles, tortilla de patata, paella, etc'
'Qué puedo cocinar con 3 ingredientes?',
'Dime una comida de temporada para este mes de Marzo?' ,
'Propón un menú mensual para una familia'
### Fuera de alcance
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
Queda excluido cualquier uso no contemplado por la UE AI Policity (https://www.consilium.europa.eu/es/policies/artificial-intelligence/)
## Entrenamiento del modelo LLM
Consultese el informe adjunto wandb:
https://github.com/recetasdelaabuela/somosnlp/blob/e7f9796dc2c293ce923f31814de78c49c5b4e3f8/Docs/RecetasDeLaAbuel%40%20Report%20_%20Recetas19kTest20_gemma-2b-it-bnb-4bit%20%E2%80%93%20Weights%20%26%20Biases%20(3).pdf
https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela/blob/main/RecetasDeLaAbuel%40%20Report%20_%20Recetas19kTest20_gemma-2b-it-bnb-4bit%20%E2%80%93%20Weights%20%26%20Biases.pdf
# Links del proyecto
<!-- Provide the basic links for the dataset. -->
- **Repository:** https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela
- **GitHub:** https://github.com/recetasdelaabuela/somosnlp
- **Paper:** https://github.com/recetasdelaabuela/somosnlp/blob/main/Paper/LatinX_NAACL_2024-3-1.pdf
- **Corpus con formato tabular:** https://huggingface.co/datasets/somosnlp/RecetasDeLaAbuela
- **Corpus de Instrucciones Original:** https://huggingface.co/datasets/somosnlp/recetasdelaabuela_genstruct_it
- **Corpus de Instrucciones Curado:** https://huggingface.co/datasets/somosnlp/recetasdelaabuela_it
- **Modelo LLM Gemma 7b 20k RecetasDeLaAbuel@:** https://huggingface.co/somosnlp/recetasdelaabuela-0.03
- **Modelo LLM Gemma 2b 20k RecetasDeLaAbuel@:** https://huggingface.co/somosnlp/RecetasDeLaAbuela_gemma-2b-it-bnb-4bit
- **Modelo LLM Tiny Llama 1.1B RecetasDeLaAbuel@:** https://huggingface.co/somosnlp/recetasdelaabuela-0.03
- **Modelo LLM 5k RecetasDeLaAbuel@:** https://huggingface.co/somosnlp/RecetasDeLaAbuela5k_gemma-2b-bnb-4bit
- **Demo RecetasDeLaAbuel@:** https://huggingface.co/spaces/somosnlp/RecetasDeLaAbuela_Demo
- **Modelo LLM ComeBien:** https://huggingface.co/somosnlp/ComeBien_gemma-2b-it-bnb-4bit
- **Demo ComeBien:** https://huggingface.co/spaces/somosnlp/ComeBien_Demo
## Uso del modelo LLM
Los modelos LLM Gemma RecetasDeLaAbuel@ se deben usar siguiendo el formato sistema/usuario/modelo (SOT='<'start_of_turn'>'',EOT='<'end_of_turn'>')"":
<bos>SOT system\n {instruction} EOT SOT user\n {nombre} EOT SOT model\n {receta} EOT EOS_TOKEN.
Más info en https://unsloth.ai/blog/gemma-bugs
## Impacto medioambiental
Los experimentos se realizaron utilizando HuggingFace (AWS) en la región sa-east-1, que tiene una eficiencia de carbono de 0,2 kg CO2 eq/kWh. Se realizó un acumulado de 50 horas de cómputo en HW tipo T4 (TDP de 70W). Se estima que las emisiones totales son 0,7 kg eq. CO2. Las estimaciones se realizaron utilizando la web ML CO2 Impact https://mlco2.github.io/impact/#compute.
# Citaciones
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
Este trabajo se ha basado y es continuación del trabajo desarrollado en el siguiente corpus durante el Hackhaton somosnlp 2023:
https://huggingface.co/datasets/somosnlp/recetas-cocina
Debemos reconocer y agradecer públicamente la labor de su creador Fredy pues gracias a su orientación inicial hemos llegado tan lejos!
https://huggingface.co/Frorozcol
Más información del magnífico proyecto inicial 'Creación de Dataset de Recetas de Comidas' de Fredy se puede encontrar en su github:
https://github.com/Frorozcoloa/ChatCocina/tree/main
Asismismo debemos reconocer y agradecer la labor de Tiago en la recopilación de diversas fuentes de recetas:
- 37 comidas saludables para cuidarse durante todo el mes
- 101 recetas sanas para tener un menú saludable de lunes a domingo
- 50 recetas Fáciles, Sanas, Rápidas y Económicas - Antojo en tu cocina
- 54 recetas saludables para niños, comidas sanas y fáciles de hacer
# Autores
https://huggingface.co/rovi27 <br>
https://huggingface.co/sbenel <br>
https://huggingface.co/GabTuco <br>
https://huggingface.co/iXrst <br>
# Asesoría Académica
Modelización de temática mediante BERTopic
https://huggingface.co/andreamorgar
# Contacto
mailto: recetasdelaabuela.comebien@gmail.com |
baber/cce-renewals | ---
configs:
- config_name: full
data_files: "renewals_full.parquet"
- config_name: unmatched
data_files: "ren_unmatched.parquet"
- config_name: matched
data_files: "matched.parquet"
license: cc0-1.0
---
The `oreg` and the `odat` refer to the original registration number and date respectively of the copyright entry and thats what we try to match with the Copyright Catalog. `rdat` is the renewal date. The other columns are pretty self-explanatory. |
DialogueCharacter/chinese_moss_unfiltered | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: response
sequence: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 3264688861
num_examples: 550415
download_size: 1534910020
dataset_size: 3264688861
---
# Dataset Card for "chinese_moss_unfiltered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
odunola/sentimenttweets | ---
license: apache-2.0
---
|
Carlisle/msmarco-passage-non-abs | ---
license: mit
---
|
quac | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
- extractive-qa
paperswithcode_id: quac
pretty_name: Question Answering in Context
dataset_info:
features:
- name: dialogue_id
dtype: string
- name: wikipedia_page_title
dtype: string
- name: background
dtype: string
- name: section_title
dtype: string
- name: context
dtype: string
- name: turn_ids
sequence: string
- name: questions
sequence: string
- name: followups
sequence:
class_label:
names:
'0': y
'1': n
'2': m
- name: yesnos
sequence:
class_label:
names:
'0': y
'1': n
'2': x
- name: answers
sequence:
- name: texts
sequence: string
- name: answer_starts
sequence: int32
- name: orig_answers
struct:
- name: texts
sequence: string
- name: answer_starts
sequence: int32
config_name: plain_text
splits:
- name: train
num_bytes: 58174754
num_examples: 11567
- name: validation
num_bytes: 7375938
num_examples: 1000
download_size: 77043986
dataset_size: 65550692
---
# Dataset Card for Question Answering in Context
## 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:** [QuAC](https://quac.ai/)
- **Paper:** [QuAC: Question Answering in Context](https://arxiv.org/abs/1808.07036)
- **Leaderboard:** [QuAC's leaderboard](https://quac.ai/)
- **Point of Contact:** [Google group](https://groups.google.com/forum/#!forum/quac_ai)
### Dataset Summary
Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context.
### Supported Tasks and Leaderboards
The core problem involves predicting a text span to answer a question about a Wikipedia section (extractive question answering). Since QuAC questions include a dialog component, each instance includes a “dialog history” of questions and answers asked in the dialog prior to the given question, along with some additional metadata.
Authors provided [an official evaluation script](https://s3.amazonaws.com/my89public/quac/scorer.py) for evaluation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A validation examples looks like this (one entry per dialogue):
```
{
'dialogue_id': 'C_6abd2040a75d47168a9e4cca9ca3fed5_0',
'wikipedia_page_title': 'Satchel Paige',
'background': 'Leroy Robert "Satchel" Paige (July 7, 1906 - June 8, 1982) was an American Negro league baseball and Major League Baseball (MLB) pitcher who became a legend in his own lifetime by being known as perhaps the best pitcher in baseball history, by his longevity in the game, and by attracting record crowds wherever he pitched. Paige was a right-handed pitcher, and at age 42 in 1948, he was the oldest major league rookie while playing for the Cleveland Indians. He played with the St. Louis Browns until age 47, and represented them in the All-Star Game in 1952 and 1953.',
'section_title': 'Chattanooga and Birmingham: 1926-29',
'context': 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month, of which Paige would collect $50 with the rest going to his mother. He also agreed to pay Lula Paige a $200 advance, and she agreed to the contract. The local newspapers--the Chattanooga News and Chattanooga Times--recognized from the beginning that Paige was special. In April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers. Part way through the 1927 season, Paige\'s contract was sold to the Birmingham Black Barons of the major Negro National League (NNL). According to Paige\'s first memoir, his contract was for $450 per month, but in his second he said it was for $275. Pitching for the Black Barons, Paige threw hard but was wild and awkward. In his first big game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray. Murray then charged the mound and Paige raced for the dugout, but Murray flung his bat and struck Paige above the hip. The police were summoned, and the headline of the Birmingham Reporter proclaimed a "Near Riot." Paige improved and matured as a pitcher with help from his teammates, Sam Streeter and Harry Salmon, and his manager, Bill Gatewood. He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings. Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (Several sources credit his 1929 strikeout total as the all-time single-season record for the Negro leagues, though there is variation among the sources about the exact number of strikeouts.) On April 29 of that season he recorded 17 strikeouts in a game against the Cuban Stars, which exceeded what was then the major league record of 16 held by Noodles Hahn and Rube Waddell. Six days later he struck out 18 Nashville Elite Giants, a number that was tied in the white majors by Bob Feller in 1938. Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut. CANNOTANSWER',
'turn_ids': ['C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#0', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#1', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#2', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#3', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#4', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#5', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#6', 'C_6abd2040a75d47168a9e4cca9ca3fed5_0_q#7'],
'questions': ['what did he do in Chattanooga', 'how did he discover him', 'what position did he play', 'how did they help him', 'when did he go to Birmingham', 'how did he feel about this', 'how did he do with this team', 'What made him leave the team'],
'followups': [0, 2, 0, 1, 0, 1, 0, 1],
'yesnos': [2, 2, 2, 2, 2, 2, 2, 2]
'answers': {
'answer_starts': [
[480, 39, 0, 67, 39],
[2300, 2300, 2300],
[848, 1023, 848, 848, 1298],
[2300, 2300, 2300, 2300, 2300],
[600, 600, 600, 634, 600],
[2300, 2300, 2300],
[939, 1431, 848, 848, 1514],
[2106, 2106, 2165]
],
'texts': [
['April 1926, shortly after his arrival, he recorded nine strikeouts over six innings against the Atlanta Black Crackers.', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige', 'A former friend from the Mobile slums, Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League.', 'manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,'],
['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
['Pitching for the Black Barons,', 'fastball', 'Pitching for', 'Pitching', 'Paige improved and matured as a pitcher with help from his teammates,'], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
["Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", "Paige's contract was sold to the Birmingham Black Barons of the major Negro National League (NNL", "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons"], ['CANNOTANSWER', 'CANNOTANSWER', 'CANNOTANSWER'],
['game in late June 1927, against the St. Louis Stars, Paige incited a brawl when his fastball hit the hand of St. Louis catcher Mitchell Murray.', 'He finished the 1927 season 7-1 with 69 strikeouts and 26 walks in 89 1/3 innings.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Pitching for the Black Barons, Paige threw hard but was wild and awkward.', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. ('],
['Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs', 'Due to his increased earning potential, Barons owner R. T. Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd,', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.']
]
},
'orig_answers': {
'answer_starts': [39, 2300, 1298, 2300, 600, 2300, 1514, 2165],
'texts': ['Alex Herman, was the player/manager for the Chattanooga White Sox of the minor Negro Southern League. In 1926 he discovered Paige and offered to pay him $250 per month,', 'CANNOTANSWER', 'Paige improved and matured as a pitcher with help from his teammates,', 'CANNOTANSWER', "Part way through the 1927 season, Paige's contract was sold to the Birmingham Black Barons", 'CANNOTANSWER', 'Over the next two seasons, Paige went 12-5 and 10-9 while recording 176 strikeouts in 1929. (', 'Jackson would "rent" Paige out to other ball clubs for a game or two to draw a decent crowd, with both Jackson and Paige taking a cut.']
},
}
```
### Data Fields
- `dialogue_id`: ID of the dialogue.
- `wikipedia_page_title`: title of the Wikipedia page.
- `background`: first paragraph of the main Wikipedia article.
- `section_tile`: Wikipedia section title.
- `context`: Wikipedia section text.
- `turn_ids`: list of identification of dialogue turns. One list of ids per dialogue.
- `questions`: list of questions in the dialogue. One list of questions per dialogue.
- `followups`: list of followup actions in the dialogue. One list of followups per dialogue. `y`: follow, `m`: maybe follow yp, `n`: don't follow up.
- `yesnos`: list of yes/no in the dialogue. One list of yes/nos per dialogue. `y`: yes, `n`: no, `x`: neither.
- `answers`: dictionary of answers to the questions (validation step of data collection)
- `answer_starts`: list of list of starting offsets. For training, list of single element lists (one answer per question).
- `texts`: list of list of span texts answering questions. For training, list of single element lists (one answer per question).
- `orig_answers`: dictionary of original answers (the ones provided by the teacher in the dialogue)
- `answer_starts`: list of starting offsets
- `texts`: list of span texts answering questions.
### Data Splits
QuAC contains 98,407 QA pairs from 13,594 dialogs. The dialogs were conducted on 8,854 unique sections from 3,611 unique Wikipedia articles, and every dialog contains between four and twelve questions.
The dataset comes with a train/dev split such that there is no overlap in sections across splits. Furthermore, the dev and test sets only include one
dialog per section, in contrast to the training set which can have multiple dialogs per section. Dev and test instances come with five reference answers instead of just one as in the training set; we obtain the extra references to improve the reliability of our evaluations, as questions can have multiple valid answer spans. The test set is not publicly available; instead, researchers must submit their models to the [leaderboard](http://quac.ai), which will run the model on our hidden test set.
The training set contains 83,568 questions (11,567 dialogues), while 7,354 (1,000) and 7,353 (1,002) separate questions are reserved for the dev and test set respectively.
## Dataset Creation
### Curation Rationale
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Source Data
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Initial Data Collection and Normalization
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Who are the source language producers?
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Annotations
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Annotation process
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
#### Who are the annotators?
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Personal and Sensitive Information
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Discussion of Biases
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Other Known Limitations
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
## Additional Information
### Dataset Curators
Please refer to the [Datasheet](https://quac.ai/datasheet.pdf) from the authors of the dataset.
### Licensing Information
The dataset is distributed under the MIT license.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{choi-etal-2018-quac,
title = "{Q}u{AC}: Question Answering in Context",
author = "Choi, Eunsol and
He, He and
Iyyer, Mohit and
Yatskar, Mark and
Yih, Wen-tau and
Choi, Yejin and
Liang, Percy and
Zettlemoyer, Luke",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D18-1241",
doi = "10.18653/v1/D18-1241",
pages = "2174--2184",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at \url{http://quac.ai}.",
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
oshanam/mydata | ---
license: gpl-2.0
---
|
CyberHarem/intrepid_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of intrepid/イントレピッド/无畏 (Azur Lane)
This is the dataset of intrepid/イントレピッド/无畏 (Azur Lane), containing 120 images and their tags.
The core tags of this character are `long_hair, breasts, bangs, blue_eyes, hair_between_eyes, twintails, grey_hair, very_long_hair, hair_ornament, large_breasts, sidelocks, mole, mole_on_breast`, 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 | 120 | 206.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/intrepid_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 120 | 98.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/intrepid_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 317 | 222.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/intrepid_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 120 | 171.22 MiB | [Download](https://huggingface.co/datasets/CyberHarem/intrepid_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 317 | 346.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/intrepid_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/intrepid_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 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, paizuri, solo_focus, looking_at_viewer, penis, upper_body, black_gloves, blue_cloak, censored, cum_on_breasts, high_collar, huge_breasts, nipples, simple_background, white_background, ejaculation, nude, smile, strapless_dress |
| 1 | 6 |  |  |  |  |  | 1girl, blue_cloak, cleavage, simple_background, solo, closed_mouth, looking_at_viewer, upper_body, white_background, black_dress, black_gloves, two_side_up, blush, high_collar, strapless_dress |
| 2 | 9 |  |  |  |  |  | 1girl, black_thighhighs, cleavage, looking_at_viewer, solo, standing, black_dress, black_gloves, blue_cloak, cowboy_shot, simple_background, zettai_ryouiki, short_dress, high_collar, skindentation, blue_cape, floating_hair, strapless_dress, two_side_up |
| 3 | 9 |  |  |  |  |  | 1girl, baseball_cap, baseball_uniform, ponytail, white_shirt, baseball_mitt, short_sleeves, thighs, holding, simple_background, solo, white_shorts, black_socks, blue_headwear, hair_through_headwear, kneehighs, purple_hair, standing, white_background, belt, looking_at_viewer, short_shorts, white_footwear, layered_sleeves, mole_on_ass, mole_on_thigh |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1boy | 1girl | blush | hetero | paizuri | solo_focus | looking_at_viewer | penis | upper_body | black_gloves | blue_cloak | censored | cum_on_breasts | high_collar | huge_breasts | nipples | simple_background | white_background | ejaculation | nude | smile | strapless_dress | cleavage | solo | closed_mouth | black_dress | two_side_up | black_thighhighs | standing | cowboy_shot | zettai_ryouiki | short_dress | skindentation | blue_cape | floating_hair | baseball_cap | baseball_uniform | ponytail | white_shirt | baseball_mitt | short_sleeves | thighs | holding | white_shorts | black_socks | blue_headwear | hair_through_headwear | kneehighs | purple_hair | belt | short_shorts | white_footwear | layered_sleeves | mole_on_ass | mole_on_thigh |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------|:--------|:--------|:---------|:----------|:-------------|:--------------------|:--------|:-------------|:---------------|:-------------|:-----------|:-----------------|:--------------|:---------------|:----------|:--------------------|:-------------------|:--------------|:-------|:--------|:------------------|:-----------|:-------|:---------------|:--------------|:--------------|:-------------------|:-----------|:--------------|:-----------------|:--------------|:----------------|:------------|:----------------|:---------------|:-------------------|:-----------|:--------------|:----------------|:----------------|:---------|:----------|:---------------|:--------------|:----------------|:------------------------|:------------|:--------------|:-------|:---------------|:-----------------|:------------------|:--------------|:----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | | X | X | | | | X | | X | X | X | | | X | | | X | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | | X | | | | | X | | | X | X | | | X | | | X | | | | | X | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | | X | | | | | X | | | | | | | | | | X | X | | | | | | X | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
nzh324/twinkle | ---
license: mit
---
|
marcelomoreno26/recipe_nlg_text_generation | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 994479980
num_examples: 1150168
- name: test
num_bytes: 213221091
num_examples: 246466
- name: validation
num_bytes: 213079274
num_examples: 246464
download_size: 709333003
dataset_size: 1420780345
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
male-2/dataset-public-v2 | ---
dataset_info:
features:
- name: conversation
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 842
num_examples: 1
download_size: 6839
dataset_size: 842
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset-public-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Greg3d/test | ---
license: afl-3.0
---
|
biglam/yalta_ai_tabular_dataset | ---
annotations_creators:
- expert-generated
language: []
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality: []
pretty_name: YALTAi Tabular Dataset
size_categories:
- n<1K
source_datasets: []
tags:
- manuscripts
- LAM
task_categories:
- object-detection
task_ids: []
---
# YALTAi Tabular Dataset
## Table of Contents
- [YALTAi Tabular Dataset](#YALTAi-Tabular-Dataset)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://doi.org/10.5281/zenodo.6827706](https://doi.org/10.5281/zenodo.6827706)
- **Paper:** [https://arxiv.org/abs/2207.11230](https://arxiv.org/abs/2207.11230)
### Dataset Summary
This dataset contains a subset of data used in the paper [You Actually Look Twice At it (YALTAi): using an object detectionapproach instead of region segmentation within the Kraken engine](https://arxiv.org/abs/2207.11230). This paper proposes treating page layout recognition on historical documents as an object detection task (compared to the usual pixel segmentation approach). This dataset covers pages with tabular information with the following objects "Header", "Col", "Marginal", "text".
### Supported Tasks and Leaderboards
- `object-detection`: This dataset can be used to train a model for object-detection on historic document images.
## Dataset Structure
This dataset has two configurations. These configurations both cover the same data and annotations but provide these annotations in different forms to make it easier to integrate the data with existing processing pipelines.
- The first configuration, `YOLO`, uses the data's original format.
- The second configuration converts the YOLO format into a format which is closer to the `COCO` annotation format. This is done to make it easier to work with the `feature_extractor`s from the `Transformers` models for object detection, which expect data to be in a COCO style format.
### Data Instances
An example instance from the COCO config:
```
{'height': 2944,
'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FA413CDA210>,
'image_id': 0,
'objects': [{'area': 435956,
'bbox': [0.0, 244.0, 1493.0, 292.0],
'category_id': 0,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 88234,
'bbox': [305.0, 127.0, 562.0, 157.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5244,
'bbox': [1416.0, 196.0, 92.0, 57.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 5720,
'bbox': [1681.0, 182.0, 88.0, 65.0],
'category_id': 2,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 374085,
'bbox': [0.0, 540.0, 163.0, 2295.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 577599,
'bbox': [104.0, 537.0, 253.0, 2283.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 598670,
'bbox': [304.0, 533.0, 262.0, 2285.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 56,
'bbox': [284.0, 539.0, 8.0, 7.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 1868412,
'bbox': [498.0, 513.0, 812.0, 2301.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 307800,
'bbox': [1250.0, 512.0, 135.0, 2280.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 494109,
'bbox': [1330.0, 503.0, 217.0, 2277.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 52,
'bbox': [1734.0, 1013.0, 4.0, 13.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []},
{'area': 90666,
'bbox': [0.0, 1151.0, 54.0, 1679.0],
'category_id': 1,
'id': 0,
'image_id': '0',
'iscrowd': False,
'segmentation': []}],
'width': 2064}
```
An example instance from the YOLO config:
``` python
{'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=2064x2944 at 0x7FAA140F2450>,
'objects': {'bbox': [[747, 390, 1493, 292],
[586, 206, 562, 157],
[1463, 225, 92, 57],
[1725, 215, 88, 65],
[80, 1688, 163, 2295],
[231, 1678, 253, 2283],
[435, 1675, 262, 2285],
[288, 543, 8, 7],
[905, 1663, 812, 2301],
[1318, 1653, 135, 2280],
[1439, 1642, 217, 2277],
[1737, 1019, 4, 13],
[26, 1991, 54, 1679]],
'label': [0, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]}}
```
### Data Fields
The fields for the YOLO config:
- `image`: the image
- `objects`: the annotations which consist of:
- `bbox`: a list of bounding boxes for the image
- `label`: a list of labels for this image
The fields for the COCO config:
- `height`: height of the image
- `width`: width of the image
- `image`: image
- `image_id`: id for the image
- `objects`: annotations in COCO format, consisting of a list containing dictionaries with the following keys:
- `bbox`: bounding boxes for the images
- `category_id`: a label for the image
- `image_id`: id for the image
- `iscrowd`: COCO `iscrowd` flag
- `segmentation`: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
### Data Splits
The dataset contains a train, validation and test split with the following numbers per split:
| | train | validation | test |
|----------|-------|------------|------|
| examples | 196 | 22 | 135 |
## Dataset Creation
> [this] dataset was produced using a single source, the Lectaurep Repertoires dataset [Rostaing et al., 2021], which served as a basis for only the training and development split. The testset is composed of original data, from various documents, from the 17th century up to the early 20th with a single soldier war report. The test set is voluntarily very different and out of domain with column borders that are not drawn nor printed in certain cases, layout in some kind of masonry layout. p.8
.
### Curation Rationale
This dataset was created to produce a simplified version of the [Lectaurep Repertoires dataset](https://github.com/HTR-United/lectaurep-repertoires), which was found to contain:
> around 16 different ways to describe columns, from Col1 to Col7, the case-different col1-col7 and finally ColPair and ColOdd, which we all reduced to Col p.8
### Source Data
#### Initial Data Collection and Normalization
The LECTAUREP (LECTure Automatique de REPertoires) project, which began in 2018, is a joint initiative of the Minutier central des notaires de Paris, the National Archives and the
Minutier central des notaires de Paris of the National Archives, the [ALMAnaCH (Automatic Language Modeling and Analysis & Computational Humanities)](https://www.inria.fr/en/almanach) team at Inria and the EPHE (Ecole Pratique des Hautes Etudes), in partnership with the Ministry of Culture.
> The lectaurep-bronod corpus brings together 100 pages from the repertoire of Maître Louis Bronod (1719-1765), notary in Paris from December 13, 1719 to July 23, 1765. The pages concerned were written during the years 1742 to 1745.
#### Who are the source language producers?
[More information needed]
### Annotations
| | Train | Dev | Test | Total | Average area | Median area |
|----------|-------|-----|------|-------|--------------|-------------|
| Col | 724 | 105 | 829 | 1658 | 9.32 | 6.33 |
| Header | 103 | 15 | 42 | 160 | 6.78 | 7.10 |
| Marginal | 60 | 8 | 0 | 68 | 0.70 | 0.71 |
| Text | 13 | 5 | 0 | 18 | 0.01 | 0.00 |
| | | | - | | | |
#### Annotation process
[More information needed]
#### Who are the annotators?
[More information needed]
### Personal and Sensitive Information
This data does not contain information relating to living individuals.
## Considerations for Using the Data
### Social Impact of Dataset
A growing number of datasets are related to page layout for historical documents. This dataset offers a different approach to annotating these datasets (focusing on object detection rather than pixel-level annotations). Improving document layout recognition can have a positive impact on downstream tasks, in particular Optical Character Recognition.
### Discussion of Biases
Historical documents contain a wide variety of page layouts. This means that the ability of models trained on this dataset to transfer to documents with very different layouts is not guaranteed.
### Other Known Limitations
[More information needed]
## Additional Information
### Dataset Curators
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@dataset{clerice_thibault_2022_6827706,
author = {Clérice, Thibault},
title = {YALTAi: Tabular Dataset},
month = jul,
year = 2022,
publisher = {Zenodo},
version = {1.0.0},
doi = {10.5281/zenodo.6827706},
url = {https://doi.org/10.5281/zenodo.6827706}
}
```
[](https://doi.org/10.5281/zenodo.6827706)
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset.
|
Bellaaazzzzz/X_ray | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: additional_feature
dtype: int64
- name: conditioning_image
dtype: image
splits:
- name: train
num_bytes: 625886838.168
num_examples: 4218
- name: test
num_bytes: 86326556.0
num_examples: 762
download_size: 697216425
dataset_size: 712213394.168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "X_ray"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
itllm52/testloi | ---
license: afl-3.0
---
|
bigscience-data/roots_indic-pa_wiktionary | ---
language: pa
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
|
huggingartists/big-russian-boss | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/big-russian-boss"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.52183 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d66eeeef006738708df1e52b84c34c14.403x403x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/big-russian-boss">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Big Russian Boss</div>
<a href="https://genius.com/artists/big-russian-boss">
<div style="text-align: center; font-size: 14px;">@big-russian-boss</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/big-russian-boss).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/big-russian-boss")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|151| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/big-russian-boss")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
chemNLP/eur-lex-europa-merged | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2027022
num_examples: 75
download_size: 1035116
dataset_size: 2027022
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "eur-lex-europa-merged"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_ewqr2130__alignment-handbook-zephyr-7b_ppostep_100 | ---
pretty_name: Evaluation run of ewqr2130/alignment-handbook-zephyr-7b_ppostep_100
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ewqr2130/alignment-handbook-zephyr-7b_ppostep_100](https://huggingface.co/ewqr2130/alignment-handbook-zephyr-7b_ppostep_100)\
\ 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_ewqr2130__alignment-handbook-zephyr-7b_ppostep_100\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-18T13:32:11.965537](https://huggingface.co/datasets/open-llm-leaderboard/details_ewqr2130__alignment-handbook-zephyr-7b_ppostep_100/blob/main/results_2024-01-18T13-32-11.965537.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\": 0.23774405306004376,\n\
\ \"acc_stderr\": 0.030137212407426187,\n \"acc_norm\": 0.23884860895654994,\n\
\ \"acc_norm_stderr\": 0.030942263352296245,\n \"mc1\": 0.23133414932680538,\n\
\ \"mc1_stderr\": 0.014761945174862661,\n \"mc2\": NaN,\n \"\
mc2_stderr\": NaN\n },\n \"harness|arc:challenge|25\": {\n \"acc\"\
: 0.2167235494880546,\n \"acc_stderr\": 0.012040156713481189,\n \"\
acc_norm\": 0.29266211604095566,\n \"acc_norm_stderr\": 0.013295916103619397\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25562636924915355,\n\
\ \"acc_stderr\": 0.004353212146198442,\n \"acc_norm\": 0.25871340370444135,\n\
\ \"acc_norm_stderr\": 0.004370328224831795\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.24444444444444444,\n\
\ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.24444444444444444,\n\
\ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.18421052631578946,\n \"acc_stderr\": 0.0315469804508223,\n\
\ \"acc_norm\": 0.18421052631578946,\n \"acc_norm_stderr\": 0.0315469804508223\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.22641509433962265,\n \"acc_stderr\": 0.02575755989310678,\n\
\ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.02575755989310678\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\"\
: 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.18497109826589594,\n\
\ \"acc_stderr\": 0.02960562398177122,\n \"acc_norm\": 0.18497109826589594,\n\
\ \"acc_norm_stderr\": 0.02960562398177122\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.03873958714149351,\n\
\ \"acc_norm\": 0.18627450980392157,\n \"acc_norm_stderr\": 0.03873958714149351\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n\
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.31063829787234043,\n \"acc_stderr\": 0.03025123757921317,\n\
\ \"acc_norm\": 0.31063829787234043,\n \"acc_norm_stderr\": 0.03025123757921317\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n\
\ \"acc_stderr\": 0.041857744240220575,\n \"acc_norm\": 0.2719298245614035,\n\
\ \"acc_norm_stderr\": 0.041857744240220575\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.23448275862068965,\n \"acc_stderr\": 0.035306258743465914,\n\
\ \"acc_norm\": 0.23448275862068965,\n \"acc_norm_stderr\": 0.035306258743465914\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\
acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.21428571428571427,\n\
\ \"acc_stderr\": 0.03670066451047181,\n \"acc_norm\": 0.21428571428571427,\n\
\ \"acc_norm_stderr\": 0.03670066451047181\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25483870967741934,\n\
\ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.25483870967741934,\n\
\ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2019704433497537,\n \"acc_stderr\": 0.028247350122180267,\n\
\ \"acc_norm\": 0.2019704433497537,\n \"acc_norm_stderr\": 0.028247350122180267\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\"\
: 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.24242424242424243,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.24242424242424243,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.1919191919191919,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.1919191919191919,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.18652849740932642,\n \"acc_stderr\": 0.02811209121011745,\n\
\ \"acc_norm\": 0.18652849740932642,\n \"acc_norm_stderr\": 0.02811209121011745\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.24615384615384617,\n \"acc_stderr\": 0.021840866990423095,\n\
\ \"acc_norm\": 0.24615384615384617,\n \"acc_norm_stderr\": 0.021840866990423095\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507385,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507385\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.23949579831932774,\n \"acc_stderr\": 0.027722065493361266,\n\
\ \"acc_norm\": 0.23949579831932774,\n \"acc_norm_stderr\": 0.027722065493361266\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436775,\n \"\
acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436775\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.22935779816513763,\n \"acc_stderr\": 0.018025349724618684,\n \"\
acc_norm\": 0.22935779816513763,\n \"acc_norm_stderr\": 0.018025349724618684\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.16666666666666666,\n \"acc_stderr\": 0.025416428388767478,\n \"\
acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.025416428388767478\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.23529411764705882,\n \"acc_stderr\": 0.029771775228145628,\n \"\
acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.029771775228145628\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2616033755274262,\n \"acc_stderr\": 0.028609516716994934,\n \
\ \"acc_norm\": 0.2616033755274262,\n \"acc_norm_stderr\": 0.028609516716994934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3811659192825112,\n\
\ \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.3811659192825112,\n\
\ \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.03941897526516303,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.03941897526516303\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3148148148148148,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.3148148148148148,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.032910995786157686,\n\
\ \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.032910995786157686\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n\
\ \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2606837606837607,\n\
\ \"acc_stderr\": 0.028760348956523414,\n \"acc_norm\": 0.2606837606837607,\n\
\ \"acc_norm_stderr\": 0.028760348956523414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2848020434227331,\n\
\ \"acc_stderr\": 0.016139174096522574,\n \"acc_norm\": 0.2848020434227331,\n\
\ \"acc_norm_stderr\": 0.016139174096522574\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.21965317919075145,\n \"acc_stderr\": 0.022289638852617904,\n\
\ \"acc_norm\": 0.21965317919075145,\n \"acc_norm_stderr\": 0.022289638852617904\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808855,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808855\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22875816993464052,\n \"acc_stderr\": 0.024051029739912258,\n\
\ \"acc_norm\": 0.22875816993464052,\n \"acc_norm_stderr\": 0.024051029739912258\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2379421221864952,\n\
\ \"acc_stderr\": 0.024185150647818704,\n \"acc_norm\": 0.2379421221864952,\n\
\ \"acc_norm_stderr\": 0.024185150647818704\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2623456790123457,\n \"acc_stderr\": 0.02447722285613511,\n\
\ \"acc_norm\": 0.2623456790123457,\n \"acc_norm_stderr\": 0.02447722285613511\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.25886524822695034,\n \"acc_stderr\": 0.026129572527180844,\n \
\ \"acc_norm\": 0.25886524822695034,\n \"acc_norm_stderr\": 0.026129572527180844\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2392438070404172,\n\
\ \"acc_stderr\": 0.010896123652676651,\n \"acc_norm\": 0.2392438070404172,\n\
\ \"acc_norm_stderr\": 0.010896123652676651\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.02439819298665492,\n\
\ \"acc_norm\": 0.20220588235294118,\n \"acc_norm_stderr\": 0.02439819298665492\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2565359477124183,\n \"acc_stderr\": 0.01766784161237899,\n \
\ \"acc_norm\": 0.2565359477124183,\n \"acc_norm_stderr\": 0.01766784161237899\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.23636363636363636,\n\
\ \"acc_stderr\": 0.04069306319721377,\n \"acc_norm\": 0.23636363636363636,\n\
\ \"acc_norm_stderr\": 0.04069306319721377\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.17142857142857143,\n \"acc_stderr\": 0.02412746346265015,\n\
\ \"acc_norm\": 0.17142857142857143,\n \"acc_norm_stderr\": 0.02412746346265015\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.19402985074626866,\n\
\ \"acc_stderr\": 0.027962677604768924,\n \"acc_norm\": 0.19402985074626866,\n\
\ \"acc_norm_stderr\": 0.027962677604768924\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.26506024096385544,\n\
\ \"acc_stderr\": 0.03436024037944967,\n \"acc_norm\": 0.26506024096385544,\n\
\ \"acc_norm_stderr\": 0.03436024037944967\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.21052631578947367,\n \"acc_stderr\": 0.0312678171466318,\n\
\ \"acc_norm\": 0.21052631578947367,\n \"acc_norm_stderr\": 0.0312678171466318\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23133414932680538,\n\
\ \"mc1_stderr\": 0.014761945174862661,\n \"mc2\": NaN,\n \"\
mc2_stderr\": NaN\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.489344909234412,\n\
\ \"acc_stderr\": 0.014049294536290403\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/ewqr2130/alignment-handbook-zephyr-7b_ppostep_100
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|arc:challenge|25_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|gsm8k|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hellaswag|10_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-32-11.965537.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T13-32-11.965537.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- '**/details_harness|winogrande|5_2024-01-18T13-32-11.965537.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-18T13-32-11.965537.parquet'
- config_name: results
data_files:
- split: 2024_01_18T13_32_11.965537
path:
- results_2024-01-18T13-32-11.965537.parquet
- split: latest
path:
- results_2024-01-18T13-32-11.965537.parquet
---
# Dataset Card for Evaluation run of ewqr2130/alignment-handbook-zephyr-7b_ppostep_100
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ewqr2130/alignment-handbook-zephyr-7b_ppostep_100](https://huggingface.co/ewqr2130/alignment-handbook-zephyr-7b_ppostep_100) 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_ewqr2130__alignment-handbook-zephyr-7b_ppostep_100",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-18T13:32:11.965537](https://huggingface.co/datasets/open-llm-leaderboard/details_ewqr2130__alignment-handbook-zephyr-7b_ppostep_100/blob/main/results_2024-01-18T13-32-11.965537.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": 0.23774405306004376,
"acc_stderr": 0.030137212407426187,
"acc_norm": 0.23884860895654994,
"acc_norm_stderr": 0.030942263352296245,
"mc1": 0.23133414932680538,
"mc1_stderr": 0.014761945174862661,
"mc2": NaN,
"mc2_stderr": NaN
},
"harness|arc:challenge|25": {
"acc": 0.2167235494880546,
"acc_stderr": 0.012040156713481189,
"acc_norm": 0.29266211604095566,
"acc_norm_stderr": 0.013295916103619397
},
"harness|hellaswag|10": {
"acc": 0.25562636924915355,
"acc_stderr": 0.004353212146198442,
"acc_norm": 0.25871340370444135,
"acc_norm_stderr": 0.004370328224831795
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.24,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.24444444444444444,
"acc_stderr": 0.03712537833614866,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.03712537833614866
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.18421052631578946,
"acc_stderr": 0.0315469804508223,
"acc_norm": 0.18421052631578946,
"acc_norm_stderr": 0.0315469804508223
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.22641509433962265,
"acc_stderr": 0.02575755989310678,
"acc_norm": 0.22641509433962265,
"acc_norm_stderr": 0.02575755989310678
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2361111111111111,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.2361111111111111,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.18497109826589594,
"acc_stderr": 0.02960562398177122,
"acc_norm": 0.18497109826589594,
"acc_norm_stderr": 0.02960562398177122
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.18627450980392157,
"acc_stderr": 0.03873958714149351,
"acc_norm": 0.18627450980392157,
"acc_norm_stderr": 0.03873958714149351
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.31063829787234043,
"acc_stderr": 0.03025123757921317,
"acc_norm": 0.31063829787234043,
"acc_norm_stderr": 0.03025123757921317
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2719298245614035,
"acc_stderr": 0.041857744240220575,
"acc_norm": 0.2719298245614035,
"acc_norm_stderr": 0.041857744240220575
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.23448275862068965,
"acc_stderr": 0.035306258743465914,
"acc_norm": 0.23448275862068965,
"acc_norm_stderr": 0.035306258743465914
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2619047619047619,
"acc_stderr": 0.022644212615525218,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.022644212615525218
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.21428571428571427,
"acc_stderr": 0.03670066451047181,
"acc_norm": 0.21428571428571427,
"acc_norm_stderr": 0.03670066451047181
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.25483870967741934,
"acc_stderr": 0.024790118459332208,
"acc_norm": 0.25483870967741934,
"acc_norm_stderr": 0.024790118459332208
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2019704433497537,
"acc_stderr": 0.028247350122180267,
"acc_norm": 0.2019704433497537,
"acc_norm_stderr": 0.028247350122180267
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.24242424242424243,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.24242424242424243,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.1919191919191919,
"acc_stderr": 0.028057791672989017,
"acc_norm": 0.1919191919191919,
"acc_norm_stderr": 0.028057791672989017
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.18652849740932642,
"acc_stderr": 0.02811209121011745,
"acc_norm": 0.18652849740932642,
"acc_norm_stderr": 0.02811209121011745
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.24615384615384617,
"acc_stderr": 0.021840866990423095,
"acc_norm": 0.24615384615384617,
"acc_norm_stderr": 0.021840866990423095
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26666666666666666,
"acc_stderr": 0.02696242432507385,
"acc_norm": 0.26666666666666666,
"acc_norm_stderr": 0.02696242432507385
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.23949579831932774,
"acc_stderr": 0.027722065493361266,
"acc_norm": 0.23949579831932774,
"acc_norm_stderr": 0.027722065493361266
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.1986754966887417,
"acc_stderr": 0.03257847384436775,
"acc_norm": 0.1986754966887417,
"acc_norm_stderr": 0.03257847384436775
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.22935779816513763,
"acc_stderr": 0.018025349724618684,
"acc_norm": 0.22935779816513763,
"acc_norm_stderr": 0.018025349724618684
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.16666666666666666,
"acc_stderr": 0.025416428388767478,
"acc_norm": 0.16666666666666666,
"acc_norm_stderr": 0.025416428388767478
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.029771775228145628,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.029771775228145628
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2616033755274262,
"acc_stderr": 0.028609516716994934,
"acc_norm": 0.2616033755274262,
"acc_norm_stderr": 0.028609516716994934
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.3811659192825112,
"acc_stderr": 0.03259625118416827,
"acc_norm": 0.3811659192825112,
"acc_norm_stderr": 0.03259625118416827
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.22900763358778625,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.22900763358778625,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.24793388429752067,
"acc_stderr": 0.03941897526516303,
"acc_norm": 0.24793388429752067,
"acc_norm_stderr": 0.03941897526516303
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.04489931073591312,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.04489931073591312
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.22699386503067484,
"acc_stderr": 0.032910995786157686,
"acc_norm": 0.22699386503067484,
"acc_norm_stderr": 0.032910995786157686
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04287858751340456,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04287858751340456
},
"harness|hendrycksTest-management|5": {
"acc": 0.2524271844660194,
"acc_stderr": 0.04301250399690877,
"acc_norm": 0.2524271844660194,
"acc_norm_stderr": 0.04301250399690877
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2606837606837607,
"acc_stderr": 0.028760348956523414,
"acc_norm": 0.2606837606837607,
"acc_norm_stderr": 0.028760348956523414
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2848020434227331,
"acc_stderr": 0.016139174096522574,
"acc_norm": 0.2848020434227331,
"acc_norm_stderr": 0.016139174096522574
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.21965317919075145,
"acc_stderr": 0.022289638852617904,
"acc_norm": 0.21965317919075145,
"acc_norm_stderr": 0.022289638852617904
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24692737430167597,
"acc_stderr": 0.014422292204808855,
"acc_norm": 0.24692737430167597,
"acc_norm_stderr": 0.014422292204808855
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.22875816993464052,
"acc_stderr": 0.024051029739912258,
"acc_norm": 0.22875816993464052,
"acc_norm_stderr": 0.024051029739912258
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2379421221864952,
"acc_stderr": 0.024185150647818704,
"acc_norm": 0.2379421221864952,
"acc_norm_stderr": 0.024185150647818704
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2623456790123457,
"acc_stderr": 0.02447722285613511,
"acc_norm": 0.2623456790123457,
"acc_norm_stderr": 0.02447722285613511
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.25886524822695034,
"acc_stderr": 0.026129572527180844,
"acc_norm": 0.25886524822695034,
"acc_norm_stderr": 0.026129572527180844
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2392438070404172,
"acc_stderr": 0.010896123652676651,
"acc_norm": 0.2392438070404172,
"acc_norm_stderr": 0.010896123652676651
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.20220588235294118,
"acc_stderr": 0.02439819298665492,
"acc_norm": 0.20220588235294118,
"acc_norm_stderr": 0.02439819298665492
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2565359477124183,
"acc_stderr": 0.01766784161237899,
"acc_norm": 0.2565359477124183,
"acc_norm_stderr": 0.01766784161237899
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.23636363636363636,
"acc_stderr": 0.04069306319721377,
"acc_norm": 0.23636363636363636,
"acc_norm_stderr": 0.04069306319721377
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.17142857142857143,
"acc_stderr": 0.02412746346265015,
"acc_norm": 0.17142857142857143,
"acc_norm_stderr": 0.02412746346265015
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.19402985074626866,
"acc_stderr": 0.027962677604768924,
"acc_norm": 0.19402985074626866,
"acc_norm_stderr": 0.027962677604768924
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.26506024096385544,
"acc_stderr": 0.03436024037944967,
"acc_norm": 0.26506024096385544,
"acc_norm_stderr": 0.03436024037944967
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.21052631578947367,
"acc_stderr": 0.0312678171466318,
"acc_norm": 0.21052631578947367,
"acc_norm_stderr": 0.0312678171466318
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23133414932680538,
"mc1_stderr": 0.014761945174862661,
"mc2": NaN,
"mc2_stderr": NaN
},
"harness|winogrande|5": {
"acc": 0.489344909234412,
"acc_stderr": 0.014049294536290403
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
bairongz/hh_rlhf_cn_60k | ---
license: mit
dataset_info:
features:
- name: context
list:
- name: role
dtype: string
- name: text
dtype: string
- name: chosen
struct:
- name: role
dtype: string
- name: text
dtype: string
- name: rejected
struct:
- name: role
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 52692150.66348743
num_examples: 60000
- name: test
num_bytes: 2896304.5223752153
num_examples: 3240
download_size: 35477794
dataset_size: 55588455.185862646
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
CyberHarem/willow_neuralcloud | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of willow/ウィロウ/薇洛儿 (Neural Cloud)
This is the dataset of willow/ウィロウ/薇洛儿 (Neural Cloud), containing 22 images and their tags.
The core tags of this character are `pink_hair, breasts, red_eyes, short_hair, large_breasts, hat, eyewear_on_head, sunglasses, bangs, bow, hair_ornament, pink_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 22 | 44.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/willow_neuralcloud/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 22 | 19.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/willow_neuralcloud/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 52 | 43.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/willow_neuralcloud/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 22 | 36.47 MiB | [Download](https://huggingface.co/datasets/CyberHarem/willow_neuralcloud/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 52 | 71.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/willow_neuralcloud/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/willow_neuralcloud',
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 | 22 |  |  |  |  |  | 1girl, solo, looking_at_viewer, gloves, open_mouth, black_thighhighs, jacket, smile, holding, bowtie, skirt, white_shirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | gloves | open_mouth | black_thighhighs | jacket | smile | holding | bowtie | skirt | white_shirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:---------|:-------------|:-------------------|:---------|:--------|:----------|:---------|:--------|:--------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X |
|
SandeepKanao/HL7-FHIR-Synthetic-Dataset | ---
license: bsd
task_categories:
- text-classification
- token-classification
- summarization
language:
- en
tags:
- medical
pretty_name: PatientData
size_categories:
- 1K<n<10K
--- |
huggingartists/little-big | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/little-big"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.151956 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/32e68b9d7093213fd4c06984ee3ff6aa.900x900x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/little-big">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Little Big</div>
<a href="https://genius.com/artists/little-big">
<div style="text-align: center; font-size: 14px;">@little-big</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/little-big).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/little-big")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|86| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/little-big")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
distilabel-internal-testing/Capybara-Preferences-Remaining-Final | ---
size_categories: n<1K
tags:
- synthetic
- distilabel
- rlaif
dataset_info:
config_name: keep_columns
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: generations
sequence: string
- name: generation_models
sequence: string
- name: ratings
sequence: int64
- name: rationales
sequence: string
- name: ultrafeedback_model
dtype: string
splits:
- name: train
num_bytes: 54027722
num_examples: 8375
download_size: 27570869
dataset_size: 54027722
configs:
- config_name: keep_columns
data_files:
- split: train
path: keep_columns/train-*
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for Capybara-Preferences-Remaining-Final
This dataset has been created with [Distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/Capybara-Preferences-Remaining-Final/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/Capybara-Preferences-Remaining-Final/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: keep_columns </summary><hr>
```json
{
"generation_models": [
"teknium/OpenHermes-2.5-Mistral-7B",
"argilla/notus-7b-v1",
"mlabonne/NeuralBeagle14-7B",
"ask_ldj"
],
"generations": [
"Certainly, there are several strategies that can help prevent the creation of a hostile environment during a debate or discussion:\n\n1. Encourage Respectful Communication: Set a tone of respect and promote civil discourse by emphasizing the importance of listening, asking questions, and acknowledging different perspectives.\n\n2. Establish Clear Rules: Clearly outline the ground rules for the discussion or debate, emphasizing that personal attacks or ad hominem remarks will not be tolerated.\n\n3. Promote Active Listening: Encourage participants to actively listen to each other, rather than just waiting for",
"Certainly! Here are some strategies that can help prevent the creation of a hostile environment during a debate or discussion:\n\n1. Encourage a Safe and Respectful Space: Create an environment that makes participants feel safe and respected by reminding everyone to avoid ad hominem attacks, to listen to each other, and to respect each other\u0027s viewpoints. Establishing clear ground rules can help everyone focus on the topic at hand and avoid distractions. 2. Encourage Active Listening: Encourage participants to listen actively to each other by letting everyone know that they have been heard,",
"Here are some strategies to prevent the creation of a hostile environment during a debate or discussion:\n\n1. Establish ground rules: At the beginning of a debate or discussion, establish clear ground rules or guidelines that promote civility and respect among participants. This may include prohibiting personal attacks, ad hominem arguments, interruptions, or negative body language.\n\n2. Encourage active listening: Encourage all participants to listen actively and attentively to each other\u0027s perspectives. This promotes understanding and can help reduce misunderstandings or emotional escalation.\n\n3. Focus on the issue",
"Absolutely, here are some strategies to prevent the creation of a hostile environment during a debate or discussion:\n\n1. Establish Ground Rules: Before starting the discussion, set clear expectations for respectful and constructive dialogue. This might include rules against personal attacks, interruptions, or disrespectful language.\n\n2. Encourage Active Listening: Participants should be encouraged to listen to others\u0027 viewpoints with an open mind, rather than formulating their own responses while others are speaking.\n\n3. Promote Empathy: Encourage participants to try to understand the perspectives and feelings of others, even if they disagree. This can help to foster a sense of mutual respect and understanding.\n\n4. Focus on the Issue, Not the Person: Remind participants to focus on the topic at hand, rather than resorting to personal attacks or criticisms. \n\n5. Use Neutral Language: Avoid inflammatory or loaded language that could escalate tensions. Instead, use neutral, objective language to express your points.\n\n6. Facilitate Fairness: If you\u0027re leading the discussion, ensure that all participants have an equal opportunity to speak and that their viewpoints are given fair consideration.\n\n7. Encourage Questions: Encourage participants to ask clarifying questions if they don\u0027t understand a point, rather than making assumptions or jumping to conclusions.\n\n8. Model Respectful Behavior: If you\u0027re leading the discussion, model the kind of behavior you want to see from participants. This includes listening attentively, responding respectfully, and admitting when you\u0027re wrong.\n\n9. Manage Conflict: If tensions do arise, address them directly and calmly. This might involve taking a short break, reminding participants of the ground rules, or facilitating a resolution between conflicting parties.\n\n10. Provide Positive Feedback: Recognize and reinforce positive behaviors, such as when participants express their points respectfully, listen attentively, or show empathy towards others. This can help to create a more positive and collaborative environment."
],
"messages": [
{
"content": "Identify the type of logical fallacy in this argument: \u0027You can\u0027t trust Peter because he didn\u0027t go to college.\u0027\n",
"role": "user"
},
{
"content": "The logical fallacy in this argument is ad hominem, because it attacks Peter\u0027s personal characteristics or circumstances (not going to college) rather than addressing the merits of his arguments or ideas.",
"role": "assistant"
},
{
"content": "Given that ad hominem is a type of logical fallacy, how might this bias affect the quality of a debate or discussion?",
"role": "user"
},
{
"content": "Ad hominem fallacies can significantly lower the quality of a debate or discussion. Instead of focusing on the merits of the arguments, ideas, or evidence at hand, participants might get distracted by personal attacks or irrelevant criticisms. This can lead to a hostile environment, where the focus is on winning the argument rather than seeking truth or understanding. Furthermore, it can prevent a fair and balanced evaluation of the actual topic being discussed, as it diverts attention away from the substantive issues.",
"role": "assistant"
},
{
"content": "Discuss the potential consequences of a hostile environment in a debate or discussion.",
"role": "user"
},
{
"content": "A hostile environment in a debate or discussion can have several negative consequences:\n\n1. Deterioration of Communication: When the environment becomes hostile, it can lead to breakdowns in communication. Participants may become defensive or aggressive, which can hinder effective exchange of ideas.\n\n2. Reduced Participation: Some individuals may feel intimidated or uncomfortable in a hostile environment, leading them to withdraw or refrain from contributing their ideas or perspectives.\n\n3. Bias and Prejudice: Hostility can exacerbate biases and prejudices, as participants may resort to stereotypes or discriminatory remarks, further escalating the conflict.\n\n4. Loss of Objectivity: In a hostile environment, participants may lose sight of the objective of the discussion or debate. Instead of seeking to understand different viewpoints or find common ground, they may focus on winning the argument at all costs.\n\n5. Damage to Relationships: If the debate or discussion involves individuals who have ongoing relationships (like colleagues, friends, or family members), a hostile environment can cause long-term damage to these relationships.\n\n6. Misinformation: In a hostile debate, the focus often shifts from facts and logical reasoning to personal attacks. This can lead to the spread of misinformation, as participants may overlook or dismiss valid points or evidence.\n\nOverall, a hostile environment in a debate or discussion can inhibit productive dialogue, discourage participation, and potentially lead to misinformation or misunderstanding.",
"role": "assistant"
},
{
"content": "Could you elaborate on strategies to prevent the creation of a hostile environment during a debate or discussion?",
"role": "user"
}
],
"ratings": [
3,
3
],
"rationales": [
"The response provides practical strategies that are related to maintaining a respectful debate environment, satisfying correctness and informativeness. However, the text is cut off, likely due to a character limit, which affects the completeness of the information and does not entirely align with the instruction\u0027s intention to provide elaborated strategies. The truncated nature of the text causes uncertainty and incompleteness, but what is provided before the cut-off is accurate.",
"Text 2 begins with strategies to create a safe space for discussion, which"
],
"ultrafeedback_model": "gpt-4-1106-preview"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distilabel-internal-testing/Capybara-Preferences-Remaining-Final", "keep_columns")
```
</details>
|
Pablao0948/Chiro | ---
license: openrail
---
|
CyberHarem/little_prinz_eugen_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of little_prinz_eugen/プリンツ・オイゲンちゃん/小欧根 (Azur Lane)
This is the dataset of little_prinz_eugen/プリンツ・オイゲンちゃん/小欧根 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `long_hair, multicolored_hair, streaked_hair, breasts, red_hair, two_side_up, bangs, large_breasts, white_hair, very_long_hair, antenna_hair, mole, mole_on_breast, hair_between_eyes, headgear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-----------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 947.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/little_prinz_eugen_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 456.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/little_prinz_eugen_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1373 | 1.05 GiB | [Download](https://huggingface.co/datasets/CyberHarem/little_prinz_eugen_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 801.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/little_prinz_eugen_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1373 | 1.62 GiB | [Download](https://huggingface.co/datasets/CyberHarem/little_prinz_eugen_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/little_prinz_eugen_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 | 16 |  |  |  |  |  | 1girl, iron_cross, long_sleeves, looking_at_viewer, solo, black_gloves, sideboob, simple_background, thighhighs, white_background, garter_straps, orange_eyes, grey_hair, smile, closed_mouth, rigging, swept_bangs |
| 1 | 6 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, looking_at_viewer, solo, black_gloves, blush, brown_eyes, hair_bow, simple_background, thighs, white_background |
| 2 | 6 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, cleavage, looking_at_viewer, maid_headdress, official_alternate_costume, solo, black_thighhighs, blush, elbow_gloves, black_dress, navel, smile, cross, red_eyes, thigh_strap |
| 3 | 9 |  |  |  |  |  | 1girl, black_thighhighs, blush, iron_cross, kimono, looking_at_viewer, solo, wide_sleeves, black_panties, sideboob, thighs, long_sleeves, sakazuki, choker, holding, obi, open_mouth, side-tie_panties, smile, alcohol, simple_background, swept_bangs, white_background, brown_eyes, collarbone, nail_polish, red_nails |
| 4 | 5 |  |  |  |  |  | 1girl, bare_shoulders, bridal_veil, cleavage, looking_at_viewer, solo, wedding_dress, white_dress, bridal_gauntlets, garter_straps, smile, white_thighhighs, blush, finger_to_mouth, see-through, simple_background, white_background, white_gloves, official_alternate_costume |
| 5 | 8 |  |  |  |  |  | 1girl, cropped_jacket, cropped_shirt, looking_at_viewer, navel, official_alternate_costume, open_jacket, race_queen, red_panties, solo, yellow_eyes, black_gloves, half_gloves, panty_straps, purple_jacket, thighhighs, two-tone_skirt, white_skirt, underboob_cutout, white_belt, checkered_flag, miniskirt, black_jacket, earpiece, standing, twintails, two-tone_hair, blue_sky, cowboy_shot, day, headset, holding_flag, short_sleeves, simple_background, smile, stomach, white_background |
| 6 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nurse_cap, short_sleeves, solo, cross, open_mouth, simple_background, white_background, white_dress, white_gloves, white_thighhighs, sideboob, white_headwear, cleavage, holding_syringe, orange_eyes, smile, swept_bangs, thighs |
| 7 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, bare_shoulders, black_ribbon, hair_ribbon, simple_background, underwear_only, white_background, black_bra, black_panties, cleavage, navel, thighs, ass, black_thighhighs, brown_eyes, collarbone, garter_belt, lingerie, looking_back, parted_lips, red_bra, red_panties, side-tie_panties, swept_bangs |
| 8 | 73 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, hair_ribbon, black_bikini, black_ribbon, cleavage, navel, side-tie_bikini_bottom, black_choker, german_flag_bikini, collarbone, bare_shoulders, thigh_strap, official_alternate_costume, smile, brown_eyes, grey_hair, thighs, day, outdoors, cross, earrings, simple_background, tongue_out |
| 9 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, playboy_bunny, solo, bare_shoulders, blush, detached_collar, strapless_leotard, black_leotard, cleavage, pantyhose, rabbit_ears, wrist_cuffs, bowtie, fake_animal_ears, simple_background, iron_cross, ribbon, smile, white_background, covered_navel, holding |
| 10 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, black_skirt, blush, holding, pleated_skirt, school_uniform, simple_background, white_shirt, white_background, full_body, hair_ribbon, alternate_costume, collared_shirt, cross, earrings, grey_hair, long_sleeves, school_bag, smile, standing |
| 11 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, white_apron, blush, enmaided, puffy_short_sleeves, cup, frilled_apron, maid_headdress, orange_eyes, waist_apron, animal_ears, black_dress, black_thighhighs, bow, brown_eyes, closed_mouth, food, holding_tray, maid_apron, open_mouth, parted_bangs, standing, wrist_cuffs |
| 12 | 7 |  |  |  |  |  | blush, 1girl, hetero, solo_focus, tongue_out, 1boy, open_mouth, facial, heart-shaped_pupils, yellow_eyes, cum_in_mouth, cum_on_breasts, cum_on_hair, erection, horns, licking_penis, uncensored |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | iron_cross | long_sleeves | looking_at_viewer | solo | black_gloves | sideboob | simple_background | thighhighs | white_background | garter_straps | orange_eyes | grey_hair | smile | closed_mouth | rigging | swept_bangs | bare_shoulders | black_dress | blush | brown_eyes | hair_bow | thighs | cleavage | maid_headdress | official_alternate_costume | black_thighhighs | elbow_gloves | navel | cross | red_eyes | thigh_strap | kimono | wide_sleeves | black_panties | sakazuki | choker | holding | obi | open_mouth | side-tie_panties | alcohol | collarbone | nail_polish | red_nails | bridal_veil | wedding_dress | white_dress | bridal_gauntlets | white_thighhighs | finger_to_mouth | see-through | white_gloves | cropped_jacket | cropped_shirt | open_jacket | race_queen | red_panties | yellow_eyes | half_gloves | panty_straps | purple_jacket | two-tone_skirt | white_skirt | underboob_cutout | white_belt | checkered_flag | miniskirt | black_jacket | earpiece | standing | twintails | two-tone_hair | blue_sky | cowboy_shot | day | headset | holding_flag | short_sleeves | stomach | nurse_cap | white_headwear | holding_syringe | black_ribbon | hair_ribbon | underwear_only | black_bra | ass | garter_belt | lingerie | looking_back | parted_lips | red_bra | black_bikini | side-tie_bikini_bottom | black_choker | german_flag_bikini | outdoors | earrings | tongue_out | playboy_bunny | detached_collar | strapless_leotard | black_leotard | pantyhose | rabbit_ears | wrist_cuffs | bowtie | fake_animal_ears | ribbon | covered_navel | black_skirt | pleated_skirt | school_uniform | white_shirt | full_body | alternate_costume | collared_shirt | school_bag | white_apron | enmaided | puffy_short_sleeves | cup | frilled_apron | waist_apron | animal_ears | bow | food | holding_tray | maid_apron | parted_bangs | hetero | solo_focus | 1boy | facial | heart-shaped_pupils | cum_in_mouth | cum_on_breasts | cum_on_hair | erection | horns | licking_penis | uncensored |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------|:---------------|:--------------------|:-------|:---------------|:-----------|:--------------------|:-------------|:-------------------|:----------------|:--------------|:------------|:--------|:---------------|:----------|:--------------|:-----------------|:--------------|:--------|:-------------|:-----------|:---------|:-----------|:-----------------|:-----------------------------|:-------------------|:---------------|:--------|:--------|:-----------|:--------------|:---------|:---------------|:----------------|:-----------|:---------|:----------|:------|:-------------|:-------------------|:----------|:-------------|:--------------|:------------|:--------------|:----------------|:--------------|:-------------------|:-------------------|:------------------|:--------------|:---------------|:-----------------|:----------------|:--------------|:-------------|:--------------|:--------------|:--------------|:---------------|:----------------|:-----------------|:--------------|:-------------------|:-------------|:-----------------|:------------|:---------------|:-----------|:-----------|:------------|:----------------|:-----------|:--------------|:------|:----------|:---------------|:----------------|:----------|:------------|:-----------------|:------------------|:---------------|:--------------|:-----------------|:------------|:------|:--------------|:-----------|:---------------|:--------------|:----------|:---------------|:-------------------------|:---------------|:---------------------|:-----------|:-----------|:-------------|:----------------|:------------------|:--------------------|:----------------|:------------|:--------------|:--------------|:---------|:-------------------|:---------|:----------------|:--------------|:----------------|:-----------------|:--------------|:------------|:--------------------|:-----------------|:-------------|:--------------|:-----------|:----------------------|:------|:----------------|:--------------|:--------------|:------|:-------|:---------------|:-------------|:---------------|:---------|:-------------|:-------|:---------|:----------------------|:---------------|:-----------------|:--------------|:-----------|:--------|:----------------|:-------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | X | X | X | | X | | X | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | X | X | X | | | | | | | | X | | | | X | X | X | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | X | X | X | X | | X | X | | X | | | | X | | | X | | | X | X | | X | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | X | | | X | | X | X | | | X | | | | X | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | X | X | X | | X | X | X | | | | X | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | X | X | | X | X | | X | | X | | X | | | X | | | X | | | X | X | | | | | | X | | | | | | | | | | X | | | | | | | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | X | | | X | | X | | | | | | | X | X | | X | X | | X | X | | | X | | X | | | | | | X | | | | | | X | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 73 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 9 |  |  |  |  |  | X | X | | X | X | | | X | | X | | | | X | | | | X | | X | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 8 |  |  |  |  |  | X | | X | X | X | | | X | | X | | | X | X | | | | | | X | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | X | | | X | X | | | | | | | X | | | X | | | | X | X | X | | | | X | | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 12 | 7 |  |  |  |  |  | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
ouvic215/Soldering-Data-Annotation-boarding | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 61916409.75
num_examples: 1522
download_size: 61887418
dataset_size: 61916409.75
---
# Dataset Card for "Soldering-Data-Annotation-boarding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
paoloitaliani/privacyqa | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: full
path: data/full-*
dataset_info:
features:
- name: document
dtype: string
- name: qa_pair
dtype: string
splits:
- name: train
num_bytes: 335038.44083526684
num_examples: 344
- name: validation
num_bytes: 42853.75406032483
num_examples: 44
- name: test
num_bytes: 41879.805104408355
num_examples: 43
- name: full
num_bytes: 419772
num_examples: 431
download_size: 233651
dataset_size: 839544.0
---
# Dataset Card for "privacyqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KaioSan/Lucas | ---
license: openrail
---
|
alancooney/sae-monology-pile-uncopyrighted-tokenizer-EleutherAI-gpt-neox-20b | ---
license: mit
---
|
tyqiangz/multilingual-sentiments | ---
language:
- de
- en
- es
- fr
- ja
- zh
- id
- ar
- hi
- it
- ms
- pt
license: apache-2.0
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
task_categories:
- text-classification
task_ids:
- sentiment-analysis
- sentiment-classification
---
# Multilingual Sentiments Dataset
A collection of multilingual sentiments datasets grouped into 3 classes -- positive, neutral, negative.
Most multilingual sentiment datasets are either 2-class positive or negative, 5-class ratings of products reviews (e.g. Amazon multilingual dataset) or multiple classes of emotions. However, to an average person, sometimes positive, negative and neutral classes suffice and are more straightforward to perceive and annotate. Also, a positive/negative classification is too naive, most of the text in the world is actually neutral in sentiment. Furthermore, most multilingual sentiment datasets don't include Asian languages (e.g. Malay, Indonesian) and are dominated by Western languages (e.g. English, German).
Git repo: https://github.com/tyqiangz/multilingual-sentiment-datasets
## Dataset Description
- **Webpage:** https://github.com/tyqiangz/multilingual-sentiment-datasets
|
kingbri/PIPPA-shareGPT | ---
license: agpl-3.0
task_categories:
- conversational
language:
- en
tags:
- conversational
- roleplay
- custom-format
- a.
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card: PIPPA-ShareGPT
This is a conversion of [PygmalionAI's PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) deduped dataset to ShareGPT format for finetuning with Axolotl.
The reformat was completed via the following [TypeScript project](https://github.com/bdashore3/ShareGPT-Reformat) called ShareGPT-Reformat.
# Files and explanations
- pippa_sharegpt_raw.jsonl: The raw deduped dataset file converted to shareGPT. Roles will be defaulted to your finetuning software.
- pippa_sharegpt.jsonl: A shareGPT dataset with the roles as USER: and CHARACTER: for finetuning with axolotl
- pippa_sharegpt_trimmed.jsonl: A shareGPT dataset that has trimmed newlines, randomized system prompts, removes empty messages, and removes examples without a character description. Roles are USER and CHARACTER.
The best file to use is `pippa_sharegpt_trimmed.jsonl` if you want a finetune without bugs or inconsistencies. The best dataset to modify is either the original PIPPA deduped dataset with the ShareGPT reformat project or `pippa_sharegpt.jsonl`.
# Required Axolotl patches
To make this dataset usable in its entirety, some axolotl patches are needed:
- [This patch](https://github.com/bdashore3/axolotl/commit/995557bdf3c6c8b3e839b224ef9513fc2b097f30) allows the ability to use custom system prompts with ShareGPT format.
- [This patch](https://github.com/bdashore3/axolotl/commit/8970280de2ea01e41c044406051922715f4086cb) allows for custom roles for the USER and ASSISTANT and allows for GPT prompts to come before human ones without cutoff.
You WILL experience unideal results with base axolotl at the time of publishing this README.
# Citations
Paper for the original dataset:
```bibtex
@misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Saviourscs/Review_status | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 554317
num_examples: 460
download_size: 315594
dataset_size: 554317
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lavita/medical-qa-shared-task-v1-all | ---
dataset_info:
features:
- name: id
dtype: int64
- name: ending0
dtype: string
- name: ending1
dtype: string
- name: ending2
dtype: string
- name: ending3
dtype: string
- name: ending4
dtype: string
- name: label
dtype: int64
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: startphrase
dtype: string
splits:
- name: train
num_bytes: 16691926
num_examples: 10178
- name: dev
num_bytes: 2086503
num_examples: 1272
download_size: 10556685
dataset_size: 18778429
---
# Dataset Card for "medical-qa-shared-task-v1-all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JonasGeiping/the_pile_WordPiecex32768_e9f3c90fb38fb46185ad86ed3b69b9d5 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 281031236364
num_examples: 544634179
download_size: 154235023266
dataset_size: 281031236364
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JacobLinCool/NTNU-Course | ---
language:
- zh
---
# National Taiwan Normal University Course Catalog
Range: 109-1 ~ 112-1
## Data
Each dataset is an array of objects, each object represents a course:
```ts
interface Course {
serial: number
code: string
name: string
ename: string
type: string
department: string
form: string
credit: number
duration: string
gu_domain: string
description: string
goals: [goal: string, capability: string][]
teacher: string[]
schedule: string
methods: [method: string, description: string][]
evaluations: [weight: number, type: string, description: string][]
material: string
enrollment: number
limit: number
schedules: {
day: number
period: [from: number, to: number]
location: string
classroom: string
}[]
programs: string[]
comment: string
restriction: string
}
```
## Copyright
The copyright is owned by National Taiwan Normal University.
Source: <https://courseap2.itc.ntnu.edu.tw/acadmOpenCourse/index.jsp>
|
Tristan/olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-suffix-array-dedup | ---
dataset_info:
features:
- name: text
dtype: string
- name: url
dtype: string
- name: crawl_timestamp
dtype: float64
splits:
- name: train
num_bytes: 33979057509.213223
num_examples: 7520438
download_size: 8573685687
dataset_size: 33979057509.213223
---
# Dataset Card for "olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-suffix-array-dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chathuranga-jayanath/selfapr-half-train-data | ---
dataset_info:
features:
- name: fix
dtype: string
- name: ctx
dtype: string
splits:
- name: train
num_bytes: 375022976
num_examples: 332097
- name: validation
num_bytes: 46798340
num_examples: 41511
- name: test
num_bytes: 46866360
num_examples: 41511
download_size: 215621510
dataset_size: 468687676
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
minoruskore/numbers | ---
license: other
configs:
- config_name: default
data_files:
- split: train1kk
path: data/train1kk-*
- split: test1kk
path: data/test1kk-*
- split: train10kk
path: data/train10kk-*
- split: test10kk
path: data/test10kk-*
- split: train100k
path: data/train100k-*
- split: test100k
path: data/test100k-*
dataset_info:
features:
- name: number
dtype: int64
- name: text
dtype: string
splits:
- name: train1kk
num_bytes: 51110729
num_examples: 800000
- name: test1kk
num_bytes: 12780276
num_examples: 200000
- name: train10kk
num_bytes: 604734899
num_examples: 8000000
- name: test10kk
num_bytes: 151175106
num_examples: 2000000
- name: train100k
num_bytes: 4170428
num_examples: 80000
- name: test100k
num_bytes: 1040577
num_examples: 20000
download_size: 193519290
dataset_size: 825012015
---
|
BangumiBase/onepiece | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of One Piece
This is the image base of bangumi One Piece, we detected 303 characters, 35000 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 | 125 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 22 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 100 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 78 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 366 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 190 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 5255 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 213 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 556 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 291 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 343 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 1155 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 53 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 129 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 1204 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 512 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 779 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 124 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 161 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 142 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 234 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 104 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 79 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 53 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 23 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 105 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 190 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 223 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 2107 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 389 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 153 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 128 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 1893 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 122 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 77 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 30 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 101 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 196 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 267 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 89 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 83 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 244 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 29 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 124 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 261 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 52 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 71 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 25 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 36 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 50 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 138 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 35 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 46 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 44 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 190 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 116 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 49 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 88 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 41 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 74 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 73 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 32 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 80 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 74 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 645 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 104 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 69 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 68 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 554 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 199 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 50 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 36 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 59 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 124 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 138 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 53 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 65 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 33 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 48 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 41 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 35 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 74 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 74 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 49 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 45 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 57 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 94 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 111 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 54 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 28 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 30 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 68 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 113 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 17 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 143 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 88 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 49 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 50 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 20 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 41 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 30 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 30 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 47 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 14 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 45 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 20 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 57 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 23 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 54 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 38 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 73 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 34 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 78 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 62 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 138 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 24 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 46 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 23 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 46 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 83 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 29 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 79 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 26 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 40 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 2694 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 120 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 103 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 33 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 59 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 54 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 22 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 23 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 54 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 40 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 23 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| 136 | 23 | [Download](136/dataset.zip) |  |  |  |  |  |  |  |  |
| 137 | 21 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 35 | [Download](138/dataset.zip) |  |  |  |  |  |  |  |  |
| 139 | 39 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 52 | [Download](140/dataset.zip) |  |  |  |  |  |  |  |  |
| 141 | 42 | [Download](141/dataset.zip) |  |  |  |  |  |  |  |  |
| 142 | 42 | [Download](142/dataset.zip) |  |  |  |  |  |  |  |  |
| 143 | 20 | [Download](143/dataset.zip) |  |  |  |  |  |  |  |  |
| 144 | 41 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| 145 | 43 | [Download](145/dataset.zip) |  |  |  |  |  |  |  |  |
| 146 | 424 | [Download](146/dataset.zip) |  |  |  |  |  |  |  |  |
| 147 | 50 | [Download](147/dataset.zip) |  |  |  |  |  |  |  |  |
| 148 | 24 | [Download](148/dataset.zip) |  |  |  |  |  |  |  |  |
| 149 | 33 | [Download](149/dataset.zip) |  |  |  |  |  |  |  |  |
| 150 | 30 | [Download](150/dataset.zip) |  |  |  |  |  |  |  |  |
| 151 | 20 | [Download](151/dataset.zip) |  |  |  |  |  |  |  |  |
| 152 | 21 | [Download](152/dataset.zip) |  |  |  |  |  |  |  |  |
| 153 | 68 | [Download](153/dataset.zip) |  |  |  |  |  |  |  |  |
| 154 | 24 | [Download](154/dataset.zip) |  |  |  |  |  |  |  |  |
| 155 | 26 | [Download](155/dataset.zip) |  |  |  |  |  |  |  |  |
| 156 | 36 | [Download](156/dataset.zip) |  |  |  |  |  |  |  |  |
| 157 | 26 | [Download](157/dataset.zip) |  |  |  |  |  |  |  |  |
| 158 | 67 | [Download](158/dataset.zip) |  |  |  |  |  |  |  |  |
| 159 | 30 | [Download](159/dataset.zip) |  |  |  |  |  |  |  |  |
| 160 | 23 | [Download](160/dataset.zip) |  |  |  |  |  |  |  |  |
| 161 | 39 | [Download](161/dataset.zip) |  |  |  |  |  |  |  |  |
| 162 | 18 | [Download](162/dataset.zip) |  |  |  |  |  |  |  |  |
| 163 | 48 | [Download](163/dataset.zip) |  |  |  |  |  |  |  |  |
| 164 | 109 | [Download](164/dataset.zip) |  |  |  |  |  |  |  |  |
| 165 | 16 | [Download](165/dataset.zip) |  |  |  |  |  |  |  |  |
| 166 | 54 | [Download](166/dataset.zip) |  |  |  |  |  |  |  |  |
| 167 | 18 | [Download](167/dataset.zip) |  |  |  |  |  |  |  |  |
| 168 | 23 | [Download](168/dataset.zip) |  |  |  |  |  |  |  |  |
| 169 | 25 | [Download](169/dataset.zip) |  |  |  |  |  |  |  |  |
| 170 | 108 | [Download](170/dataset.zip) |  |  |  |  |  |  |  |  |
| 171 | 37 | [Download](171/dataset.zip) |  |  |  |  |  |  |  |  |
| 172 | 58 | [Download](172/dataset.zip) |  |  |  |  |  |  |  |  |
| 173 | 24 | [Download](173/dataset.zip) |  |  |  |  |  |  |  |  |
| 174 | 37 | [Download](174/dataset.zip) |  |  |  |  |  |  |  |  |
| 175 | 41 | [Download](175/dataset.zip) |  |  |  |  |  |  |  |  |
| 176 | 18 | [Download](176/dataset.zip) |  |  |  |  |  |  |  |  |
| 177 | 21 | [Download](177/dataset.zip) |  |  |  |  |  |  |  |  |
| 178 | 157 | [Download](178/dataset.zip) |  |  |  |  |  |  |  |  |
| 179 | 32 | [Download](179/dataset.zip) |  |  |  |  |  |  |  |  |
| 180 | 42 | [Download](180/dataset.zip) |  |  |  |  |  |  |  |  |
| 181 | 40 | [Download](181/dataset.zip) |  |  |  |  |  |  |  |  |
| 182 | 29 | [Download](182/dataset.zip) |  |  |  |  |  |  |  |  |
| 183 | 16 | [Download](183/dataset.zip) |  |  |  |  |  |  |  |  |
| 184 | 22 | [Download](184/dataset.zip) |  |  |  |  |  |  |  |  |
| 185 | 20 | [Download](185/dataset.zip) |  |  |  |  |  |  |  |  |
| 186 | 16 | [Download](186/dataset.zip) |  |  |  |  |  |  |  |  |
| 187 | 21 | [Download](187/dataset.zip) |  |  |  |  |  |  |  |  |
| 188 | 51 | [Download](188/dataset.zip) |  |  |  |  |  |  |  |  |
| 189 | 36 | [Download](189/dataset.zip) |  |  |  |  |  |  |  |  |
| 190 | 95 | [Download](190/dataset.zip) |  |  |  |  |  |  |  |  |
| 191 | 31 | [Download](191/dataset.zip) |  |  |  |  |  |  |  |  |
| 192 | 14 | [Download](192/dataset.zip) |  |  |  |  |  |  |  |  |
| 193 | 18 | [Download](193/dataset.zip) |  |  |  |  |  |  |  |  |
| 194 | 26 | [Download](194/dataset.zip) |  |  |  |  |  |  |  |  |
| 195 | 68 | [Download](195/dataset.zip) |  |  |  |  |  |  |  |  |
| 196 | 44 | [Download](196/dataset.zip) |  |  |  |  |  |  |  |  |
| 197 | 29 | [Download](197/dataset.zip) |  |  |  |  |  |  |  |  |
| 198 | 34 | [Download](198/dataset.zip) |  |  |  |  |  |  |  |  |
| 199 | 10 | [Download](199/dataset.zip) |  |  |  |  |  |  |  |  |
| 200 | 38 | [Download](200/dataset.zip) |  |  |  |  |  |  |  |  |
| 201 | 32 | [Download](201/dataset.zip) |  |  |  |  |  |  |  |  |
| 202 | 31 | [Download](202/dataset.zip) |  |  |  |  |  |  |  |  |
| 203 | 19 | [Download](203/dataset.zip) |  |  |  |  |  |  |  |  |
| 204 | 39 | [Download](204/dataset.zip) |  |  |  |  |  |  |  |  |
| 205 | 25 | [Download](205/dataset.zip) |  |  |  |  |  |  |  |  |
| 206 | 37 | [Download](206/dataset.zip) |  |  |  |  |  |  |  |  |
| 207 | 42 | [Download](207/dataset.zip) |  |  |  |  |  |  |  |  |
| 208 | 62 | [Download](208/dataset.zip) |  |  |  |  |  |  |  |  |
| 209 | 45 | [Download](209/dataset.zip) |  |  |  |  |  |  |  |  |
| 210 | 49 | [Download](210/dataset.zip) |  |  |  |  |  |  |  |  |
| 211 | 22 | [Download](211/dataset.zip) |  |  |  |  |  |  |  |  |
| 212 | 44 | [Download](212/dataset.zip) |  |  |  |  |  |  |  |  |
| 213 | 17 | [Download](213/dataset.zip) |  |  |  |  |  |  |  |  |
| 214 | 48 | [Download](214/dataset.zip) |  |  |  |  |  |  |  |  |
| 215 | 11 | [Download](215/dataset.zip) |  |  |  |  |  |  |  |  |
| 216 | 29 | [Download](216/dataset.zip) |  |  |  |  |  |  |  |  |
| 217 | 25 | [Download](217/dataset.zip) |  |  |  |  |  |  |  |  |
| 218 | 15 | [Download](218/dataset.zip) |  |  |  |  |  |  |  |  |
| 219 | 17 | [Download](219/dataset.zip) |  |  |  |  |  |  |  |  |
| 220 | 25 | [Download](220/dataset.zip) |  |  |  |  |  |  |  |  |
| 221 | 72 | [Download](221/dataset.zip) |  |  |  |  |  |  |  |  |
| 222 | 65 | [Download](222/dataset.zip) |  |  |  |  |  |  |  |  |
| 223 | 45 | [Download](223/dataset.zip) |  |  |  |  |  |  |  |  |
| 224 | 33 | [Download](224/dataset.zip) |  |  |  |  |  |  |  |  |
| 225 | 19 | [Download](225/dataset.zip) |  |  |  |  |  |  |  |  |
| 226 | 30 | [Download](226/dataset.zip) |  |  |  |  |  |  |  |  |
| 227 | 21 | [Download](227/dataset.zip) |  |  |  |  |  |  |  |  |
| 228 | 12 | [Download](228/dataset.zip) |  |  |  |  |  |  |  |  |
| 229 | 11 | [Download](229/dataset.zip) |  |  |  |  |  |  |  |  |
| 230 | 34 | [Download](230/dataset.zip) |  |  |  |  |  |  |  |  |
| 231 | 13 | [Download](231/dataset.zip) |  |  |  |  |  |  |  |  |
| 232 | 101 | [Download](232/dataset.zip) |  |  |  |  |  |  |  |  |
| 233 | 147 | [Download](233/dataset.zip) |  |  |  |  |  |  |  |  |
| 234 | 28 | [Download](234/dataset.zip) |  |  |  |  |  |  |  |  |
| 235 | 34 | [Download](235/dataset.zip) |  |  |  |  |  |  |  |  |
| 236 | 24 | [Download](236/dataset.zip) |  |  |  |  |  |  |  |  |
| 237 | 37 | [Download](237/dataset.zip) |  |  |  |  |  |  |  |  |
| 238 | 62 | [Download](238/dataset.zip) |  |  |  |  |  |  |  |  |
| 239 | 30 | [Download](239/dataset.zip) |  |  |  |  |  |  |  |  |
| 240 | 64 | [Download](240/dataset.zip) |  |  |  |  |  |  |  |  |
| 241 | 27 | [Download](241/dataset.zip) |  |  |  |  |  |  |  |  |
| 242 | 13 | [Download](242/dataset.zip) |  |  |  |  |  |  |  |  |
| 243 | 27 | [Download](243/dataset.zip) |  |  |  |  |  |  |  |  |
| 244 | 20 | [Download](244/dataset.zip) |  |  |  |  |  |  |  |  |
| 245 | 13 | [Download](245/dataset.zip) |  |  |  |  |  |  |  |  |
| 246 | 19 | [Download](246/dataset.zip) |  |  |  |  |  |  |  |  |
| 247 | 40 | [Download](247/dataset.zip) |  |  |  |  |  |  |  |  |
| 248 | 18 | [Download](248/dataset.zip) |  |  |  |  |  |  |  |  |
| 249 | 20 | [Download](249/dataset.zip) |  |  |  |  |  |  |  |  |
| 250 | 63 | [Download](250/dataset.zip) |  |  |  |  |  |  |  |  |
| 251 | 15 | [Download](251/dataset.zip) |  |  |  |  |  |  |  |  |
| 252 | 16 | [Download](252/dataset.zip) |  |  |  |  |  |  |  |  |
| 253 | 5 | [Download](253/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 254 | 10 | [Download](254/dataset.zip) |  |  |  |  |  |  |  |  |
| 255 | 14 | [Download](255/dataset.zip) |  |  |  |  |  |  |  |  |
| 256 | 16 | [Download](256/dataset.zip) |  |  |  |  |  |  |  |  |
| 257 | 24 | [Download](257/dataset.zip) |  |  |  |  |  |  |  |  |
| 258 | 32 | [Download](258/dataset.zip) |  |  |  |  |  |  |  |  |
| 259 | 17 | [Download](259/dataset.zip) |  |  |  |  |  |  |  |  |
| 260 | 19 | [Download](260/dataset.zip) |  |  |  |  |  |  |  |  |
| 261 | 40 | [Download](261/dataset.zip) |  |  |  |  |  |  |  |  |
| 262 | 70 | [Download](262/dataset.zip) |  |  |  |  |  |  |  |  |
| 263 | 44 | [Download](263/dataset.zip) |  |  |  |  |  |  |  |  |
| 264 | 37 | [Download](264/dataset.zip) |  |  |  |  |  |  |  |  |
| 265 | 18 | [Download](265/dataset.zip) |  |  |  |  |  |  |  |  |
| 266 | 17 | [Download](266/dataset.zip) |  |  |  |  |  |  |  |  |
| 267 | 152 | [Download](267/dataset.zip) |  |  |  |  |  |  |  |  |
| 268 | 14 | [Download](268/dataset.zip) |  |  |  |  |  |  |  |  |
| 269 | 26 | [Download](269/dataset.zip) |  |  |  |  |  |  |  |  |
| 270 | 129 | [Download](270/dataset.zip) |  |  |  |  |  |  |  |  |
| 271 | 29 | [Download](271/dataset.zip) |  |  |  |  |  |  |  |  |
| 272 | 42 | [Download](272/dataset.zip) |  |  |  |  |  |  |  |  |
| 273 | 15 | [Download](273/dataset.zip) |  |  |  |  |  |  |  |  |
| 274 | 21 | [Download](274/dataset.zip) |  |  |  |  |  |  |  |  |
| 275 | 40 | [Download](275/dataset.zip) |  |  |  |  |  |  |  |  |
| 276 | 12 | [Download](276/dataset.zip) |  |  |  |  |  |  |  |  |
| 277 | 20 | [Download](277/dataset.zip) |  |  |  |  |  |  |  |  |
| 278 | 15 | [Download](278/dataset.zip) |  |  |  |  |  |  |  |  |
| 279 | 11 | [Download](279/dataset.zip) |  |  |  |  |  |  |  |  |
| 280 | 29 | [Download](280/dataset.zip) |  |  |  |  |  |  |  |  |
| 281 | 17 | [Download](281/dataset.zip) |  |  |  |  |  |  |  |  |
| 282 | 9 | [Download](282/dataset.zip) |  |  |  |  |  |  |  |  |
| 283 | 19 | [Download](283/dataset.zip) |  |  |  |  |  |  |  |  |
| 284 | 28 | [Download](284/dataset.zip) |  |  |  |  |  |  |  |  |
| 285 | 14 | [Download](285/dataset.zip) |  |  |  |  |  |  |  |  |
| 286 | 6 | [Download](286/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 287 | 6 | [Download](287/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 288 | 12 | [Download](288/dataset.zip) |  |  |  |  |  |  |  |  |
| 289 | 10 | [Download](289/dataset.zip) |  |  |  |  |  |  |  |  |
| 290 | 98 | [Download](290/dataset.zip) |  |  |  |  |  |  |  |  |
| 291 | 8 | [Download](291/dataset.zip) |  |  |  |  |  |  |  |  |
| 292 | 10 | [Download](292/dataset.zip) |  |  |  |  |  |  |  |  |
| 293 | 14 | [Download](293/dataset.zip) |  |  |  |  |  |  |  |  |
| 294 | 54 | [Download](294/dataset.zip) |  |  |  |  |  |  |  |  |
| 295 | 35 | [Download](295/dataset.zip) |  |  |  |  |  |  |  |  |
| 296 | 12 | [Download](296/dataset.zip) |  |  |  |  |  |  |  |  |
| 297 | 25 | [Download](297/dataset.zip) |  |  |  |  |  |  |  |  |
| 298 | 20 | [Download](298/dataset.zip) |  |  |  |  |  |  |  |  |
| 299 | 6 | [Download](299/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 300 | 25 | [Download](300/dataset.zip) |  |  |  |  |  |  |  |  |
| 301 | 14 | [Download](301/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 661 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
vldsavelyev/guitar_tab | ---
task_categories:
- text-generation
tags:
- music
- music-notation
- guitar
- tablature
size_categories:
- 10K<n<100K
dataset_info:
- config_name: default
features:
- name: text
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 961930414
num_examples: 47299
download_size: 75369951
dataset_size: 961930414
- config_name: all
features:
- name: file
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: track_name
dtype: string
- name: track_number
dtype: int32
- name: instrument_name
dtype: string
- name: instrument_number
dtype: int32
- name: tempo
dtype: int32
- name: tuning
dtype: string
- name: frets
dtype: int32
splits:
- name: train
num_bytes: 951605310
num_examples: 188297
download_size: 75369951
dataset_size: 951605310
---
# Dataset Card for Dataset Name
## Dataset Description
- **Repository:** https://github.com/vladsavelyev/guitart
### Dataset Summary
GuitarPro files with song tablatures, converted into [alphaTex](https://alphatab.net/docs/alphatex/introduction) format using this Python [converter](https://github.com/vladsavelyev/guitartab/blob/main/gp_to_tex.py).
### Supported Tasks and Leaderboards
Supported are NLP tasks, and potentially could be augmented with audio and used to auto-generate tabs from music.
### Source Data
GuitarPro tabs archive. |
Multimodal-Fatima/OxfordPets_embeddings | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: vision_embeddings
sequence: float32
splits:
- name: test
num_bytes: 420471647.375
num_examples: 3669
download_size: 0
dataset_size: 420471647.375
---
# Dataset Card for "OxfordPets_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lyccyl/en-zh-lexicon | ---
license: cc-by-nc-sa-4.0
---
|
camenduru/plushies | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 42942055.0
num_examples: 730
download_size: 42653871
dataset_size: 42942055.0
models:
- camenduru/plushies
---
|
Smith42/galaxies | ---
license: cc-by-sa-4.0
size_categories:
- 1M<n<10M
dataset_info:
features:
- name: image
dtype: image
- name: dr8_id
dtype: string
splits:
- name: train
num_bytes: 959387460144.3469
num_examples: 8474566
- name: test
num_bytes: 9785671701.822557
num_examples: 86471
- name: validation
num_bytes: 9798204502.80013
num_examples: 86499
download_size: 982501453040
dataset_size: 978971336348.9696
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
tags:
- astronomy
---
# Galaxies for training astroPT
Here we have ~8.5 million galaxy cutouts from the [DESI legacy survey DR8](https://www.legacysurvey.org/dr8/description/).
The cut outs are 512x512 pixel jpg images centred on the galaxy source.
I've split away 1% of the images into a test set, and 1% into a validation set.
The remaining 98% of the images comprise the training set.
There is also accompanying metadata!
The metadata is in parquet format in the root dir of this repo.
You can link the metadata with the galaxies via their dr8_id.
## Useful links
Models here: [https://huggingface.co/Smith42/astroPT](https://huggingface.co/Smith42/astroPT)
Code here: [https://github.com/smith42/astroPT](https://github.com/smith42/astroPT)
Upstream catalogue is [on Zenodo](https://zenodo.org/records/8360385) and paper describing the catalogue is available as [Walmsley+2023](https://doi.org/10.1093/mnras/stad2919).
If you find this dataset useful please consider citing the upstream sources below 🚀🚀:
```
@article{ref_dey2019,
author = {Dey, A. and Schlegel, D. J. and Lang, D. and Blum, R. and Burleigh, K. and Fan, X. and Findlay, J. R. and Finkbeiner, D. and Herrera, D. and Juneau, S. and others},
title = {{Overview of the DESI Legacy Imaging Surveys}},
journal = {Astronomical Journal},
volume = {157},
number = {5},
pages = {168},
year = {2019},
issn = {1538-3881},
publisher = {The American Astronomical Society},
doi = {10.3847/1538-3881/ab089d}
}
```
```
@article{ref_walmsley2023,
author = {Walmsley, M. and G{\ifmmode\acute{e}\else\'{e}\fi}ron, T. and Kruk, S. and Scaife, A. M. M. and Lintott, C. and Masters, K. L. and Dawson, J. M. and Dickinson, H. and Fortson, L. and Garland, I. L. and others},
title = {{Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys}},
journal = {Monthly Notices of the Royal Astronomical Society},
volume = {526},
number = {3},
pages = {4768--4786},
year = {2023},
issn = {0035-8711},
publisher = {Oxford Academic},
doi = {10.1093/mnras/stad2919}
}
``` |
open-llm-leaderboard/details_Nitral-AI__Stanta-Lelemon-Maid-7B | ---
pretty_name: Evaluation run of Nitral-AI/Stanta-Lelemon-Maid-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Nitral-AI/Stanta-Lelemon-Maid-7B](https://huggingface.co/Nitral-AI/Stanta-Lelemon-Maid-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Nitral-AI__Stanta-Lelemon-Maid-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-02T21:55:29.755449](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__Stanta-Lelemon-Maid-7B/blob/main/results_2024-04-02T21-55-29.755449.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\": 0.6502673274605925,\n\
\ \"acc_stderr\": 0.03212466853426241,\n \"acc_norm\": 0.6519697657151704,\n\
\ \"acc_norm_stderr\": 0.032769330700968775,\n \"mc1\": 0.42717258261933905,\n\
\ \"mc1_stderr\": 0.01731683441096393,\n \"mc2\": 0.5957979284348149,\n\
\ \"mc2_stderr\": 0.015352861274064818\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6484641638225256,\n \"acc_stderr\": 0.013952413699600938,\n\
\ \"acc_norm\": 0.6757679180887372,\n \"acc_norm_stderr\": 0.013678810399518824\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6802429794861581,\n\
\ \"acc_stderr\": 0.004654291661255905,\n \"acc_norm\": 0.8602867954590719,\n\
\ \"acc_norm_stderr\": 0.003459806991389837\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.02794321998933714,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.02794321998933714\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.035676037996391706,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.035676037996391706\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\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\":\
\ 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.042295258468165065\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137285,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137285\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7580645161290323,\n\
\ \"acc_stderr\": 0.024362599693031086,\n \"acc_norm\": 0.7580645161290323,\n\
\ \"acc_norm_stderr\": 0.024362599693031086\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.0351760354036101,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.0351760354036101\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218974,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218974\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \
\ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206858,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206858\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6722689075630253,\n \"acc_stderr\": 0.03048991141767323,\n \
\ \"acc_norm\": 0.6722689075630253,\n \"acc_norm_stderr\": 0.03048991141767323\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.039439666991836285,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.039439666991836285\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8311926605504587,\n \"acc_stderr\": 0.01606005626853034,\n \"\
acc_norm\": 0.8311926605504587,\n \"acc_norm_stderr\": 0.01606005626853034\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931792,\n \"\
acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931792\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579658,\n \
\ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579658\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\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\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.032262193772867744,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.032262193772867744\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.021901905115073325,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.021901905115073325\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993459,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993459\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7312138728323699,\n \"acc_stderr\": 0.023868003262500104,\n\
\ \"acc_norm\": 0.7312138728323699,\n \"acc_norm_stderr\": 0.023868003262500104\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3474860335195531,\n\
\ \"acc_stderr\": 0.01592556406020815,\n \"acc_norm\": 0.3474860335195531,\n\
\ \"acc_norm_stderr\": 0.01592556406020815\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.0256468630971379,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.0256468630971379\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7376543209876543,\n \"acc_stderr\": 0.024477222856135114,\n\
\ \"acc_norm\": 0.7376543209876543,\n \"acc_norm_stderr\": 0.024477222856135114\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47783572359843546,\n\
\ \"acc_stderr\": 0.01275768304771618,\n \"acc_norm\": 0.47783572359843546,\n\
\ \"acc_norm_stderr\": 0.01275768304771618\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.027778298701545443,\n\
\ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.027778298701545443\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \
\ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7428571428571429,\n \"acc_stderr\": 0.027979823538744546,\n\
\ \"acc_norm\": 0.7428571428571429,\n \"acc_norm_stderr\": 0.027979823538744546\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.42717258261933905,\n\
\ \"mc1_stderr\": 0.01731683441096393,\n \"mc2\": 0.5957979284348149,\n\
\ \"mc2_stderr\": 0.015352861274064818\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7963693764798737,\n \"acc_stderr\": 0.011317798781626918\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6110689916603488,\n \
\ \"acc_stderr\": 0.01342838248127424\n }\n}\n```"
repo_url: https://huggingface.co/Nitral-AI/Stanta-Lelemon-Maid-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|arc:challenge|25_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|gsm8k|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hellaswag|10_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T21-55-29.755449.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T21-55-29.755449.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- '**/details_harness|winogrande|5_2024-04-02T21-55-29.755449.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-02T21-55-29.755449.parquet'
- config_name: results
data_files:
- split: 2024_04_02T21_55_29.755449
path:
- results_2024-04-02T21-55-29.755449.parquet
- split: latest
path:
- results_2024-04-02T21-55-29.755449.parquet
---
# Dataset Card for Evaluation run of Nitral-AI/Stanta-Lelemon-Maid-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Nitral-AI/Stanta-Lelemon-Maid-7B](https://huggingface.co/Nitral-AI/Stanta-Lelemon-Maid-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Nitral-AI__Stanta-Lelemon-Maid-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-02T21:55:29.755449](https://huggingface.co/datasets/open-llm-leaderboard/details_Nitral-AI__Stanta-Lelemon-Maid-7B/blob/main/results_2024-04-02T21-55-29.755449.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": 0.6502673274605925,
"acc_stderr": 0.03212466853426241,
"acc_norm": 0.6519697657151704,
"acc_norm_stderr": 0.032769330700968775,
"mc1": 0.42717258261933905,
"mc1_stderr": 0.01731683441096393,
"mc2": 0.5957979284348149,
"mc2_stderr": 0.015352861274064818
},
"harness|arc:challenge|25": {
"acc": 0.6484641638225256,
"acc_stderr": 0.013952413699600938,
"acc_norm": 0.6757679180887372,
"acc_norm_stderr": 0.013678810399518824
},
"harness|hellaswag|10": {
"acc": 0.6802429794861581,
"acc_stderr": 0.004654291661255905,
"acc_norm": 0.8602867954590719,
"acc_norm_stderr": 0.003459806991389837
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.02794321998933714,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.02794321998933714
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.035676037996391706,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.035676037996391706
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.025107425481137285,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.025107425481137285
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7580645161290323,
"acc_stderr": 0.024362599693031086,
"acc_norm": 0.7580645161290323,
"acc_norm_stderr": 0.024362599693031086
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.0351760354036101,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.0351760354036101
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.032568666616811015,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.032568666616811015
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
"acc_stderr": 0.027772533334218974,
"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218974
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.02150024957603348,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.02150024957603348
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.658974358974359,
"acc_stderr": 0.02403548967633508,
"acc_norm": 0.658974358974359,
"acc_norm_stderr": 0.02403548967633508
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.362962962962963,
"acc_stderr": 0.029318203645206858,
"acc_norm": 0.362962962962963,
"acc_norm_stderr": 0.029318203645206858
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6722689075630253,
"acc_stderr": 0.03048991141767323,
"acc_norm": 0.6722689075630253,
"acc_norm_stderr": 0.03048991141767323
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.039439666991836285,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.039439666991836285
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8311926605504587,
"acc_stderr": 0.01606005626853034,
"acc_norm": 0.8311926605504587,
"acc_norm_stderr": 0.01606005626853034
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8480392156862745,
"acc_stderr": 0.025195658428931792,
"acc_norm": 0.8480392156862745,
"acc_norm_stderr": 0.025195658428931792
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8185654008438819,
"acc_stderr": 0.025085961144579658,
"acc_norm": 0.8185654008438819,
"acc_norm_stderr": 0.025085961144579658
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"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": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.032262193772867744,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.032262193772867744
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5267857142857143,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.5267857142857143,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.021901905115073325,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.021901905115073325
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993459,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993459
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7312138728323699,
"acc_stderr": 0.023868003262500104,
"acc_norm": 0.7312138728323699,
"acc_norm_stderr": 0.023868003262500104
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3474860335195531,
"acc_stderr": 0.01592556406020815,
"acc_norm": 0.3474860335195531,
"acc_norm_stderr": 0.01592556406020815
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.0256468630971379,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.0256468630971379
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.02608270069539966,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.02608270069539966
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7376543209876543,
"acc_stderr": 0.024477222856135114,
"acc_norm": 0.7376543209876543,
"acc_norm_stderr": 0.024477222856135114
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47783572359843546,
"acc_stderr": 0.01275768304771618,
"acc_norm": 0.47783572359843546,
"acc_norm_stderr": 0.01275768304771618
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7022058823529411,
"acc_stderr": 0.027778298701545443,
"acc_norm": 0.7022058823529411,
"acc_norm_stderr": 0.027778298701545443
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6699346405228758,
"acc_stderr": 0.019023726160724553,
"acc_norm": 0.6699346405228758,
"acc_norm_stderr": 0.019023726160724553
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7428571428571429,
"acc_stderr": 0.027979823538744546,
"acc_norm": 0.7428571428571429,
"acc_norm_stderr": 0.027979823538744546
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454125,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454125
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.0358870281282637,
"acc_norm": 0.85,
"acc_norm_stderr": 0.0358870281282637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.42717258261933905,
"mc1_stderr": 0.01731683441096393,
"mc2": 0.5957979284348149,
"mc2_stderr": 0.015352861274064818
},
"harness|winogrande|5": {
"acc": 0.7963693764798737,
"acc_stderr": 0.011317798781626918
},
"harness|gsm8k|5": {
"acc": 0.6110689916603488,
"acc_stderr": 0.01342838248127424
}
}
```
## 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] |
freshpearYoon/vr_train_free_42 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: filename
dtype: string
- name: NumOfUtterance
dtype: int64
- name: text
dtype: string
- name: samplingrate
dtype: int64
- name: begin_time
dtype: float64
- name: end_time
dtype: float64
- name: speaker_id
dtype: string
- name: directory
dtype: string
splits:
- name: train
num_bytes: 7766409210
num_examples: 10000
download_size: 1296341413
dataset_size: 7766409210
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chiranshamilka/TestIpay | ---
license: openrail++
---
|
nongiga/nt_multispecies_1048576 | ---
dataset_info:
features:
- name: sequence
dtype: string
- name: description
dtype: string
- name: start_pos
dtype: int32
- name: end_pos
dtype: int32
- name: fasta_url
dtype: string
splits:
- name: train
num_bytes: 133253293053
num_examples: 127050
- name: validation
num_bytes: 53488531
num_examples: 51
- name: test
num_bytes: 84951962
num_examples: 81
download_size: 60060196924
dataset_size: 133391733546
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
ovior/twitter_dataset_1713017767 | ---
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: 2329218
num_examples: 7037
download_size: 1327166
dataset_size: 2329218
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yzhuang/metatree_fri_c3_1000_10 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: X
sequence: float64
- name: y
dtype: int64
splits:
- name: train
num_bytes: 71600
num_examples: 716
- name: validation
num_bytes: 28400
num_examples: 284
download_size: 105272
dataset_size: 100000
---
# Dataset Card for "metatree_fri_c3_1000_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dog/actlearn_to_label_samples | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1393.5387535882953
num_examples: 5
download_size: 3840
dataset_size: 1393.5387535882953
---
# Dataset Card for "actlearn_to_label_samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.