datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
atgarcia/testDataset1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: audio
struct:
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download_size: 268846964
dataset_size: 735942334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FINNUMBER/FINCH_TRAIN_SA_ESG_400_NEWFORMAT | ---
dataset_info:
features:
- name: task
dtype: string
- name: context
dtype: string
- name: question
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- name: answer
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- name: instruction
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num_examples: 400
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dataset_size: 3534599
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Zexanima/website_screenshots_image_dataset | ---
license: mit
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: url
dtype: 'null'
- name: date_captured
dtype: string
- name: objects
list:
- name: area
dtype: int64
- name: bbox
sequence: int64
- name: category_id
dtype: int64
- name: id
dtype: int64
- name: image_id
dtype: int64
- name: iscrowd
dtype: int64
- name: segmentation
sequence: 'null'
splits:
- name: test
num_bytes: 22424625
num_examples: 242
- name: train
num_bytes: 159535409.08
num_examples: 1688
- name: valid
num_bytes: 46104875
num_examples: 482
download_size: 201411511
dataset_size: 228064909.08
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: valid
path: data/valid-*
task_categories:
- object-detection
language:
- en
tags:
- web
- website
---
# Website Screenshots Image Dataset
<!-- Provide a quick summary of the dataset. -->
This dataset is obtainable [here from roboflow.](https://universe.roboflow.com/roboflow-gw7yv/website-screenshots).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Language(s) (NLP):** [English]
- **License:** [MIT]
### Dataset Sources
<!-- Provide the basic links for the dataset. -->
- **Source:** [https://universe.roboflow.com/roboflow-gw7yv/website-screenshots/dataset/1]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
From the roboflow website:
> Annotated screenshots are very useful in Robotic Process Automation. But they can be expensive to label. This dataset would cost over $4000 for humans to label on popular labeling services. We hope this dataset provides a good starting point for your project.
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The Roboflow Website Screenshots dataset is a synthetically generated dataset composed of screenshots from over 1000 of the world's top websites
### Annotations
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
- button: navigation links, tabs, etc.
- heading: text that was enclosed in \<h1> to \<h6> tags.
- link: inline, textual \<a> tags.
- label: text labeling form fields.
- text: all other text.
- image: \<img>, \<svg>, or \<video> tags, and icons.
- iframe: ads and 3rd party content.
#### label2id
```python
label2id = {
'button': 1,
'elements': 0,
'field': 2,
'heading': 3,
'iframe': 4,
'image': 5,
'label': 6,
'link': 7,
'text': 8
}
```
#### id2label
```python
id2label = {
0: 'elements',
1: 'button',
2: 'field',
3: 'heading',
4: 'iframe',
5: 'image',
6: 'label',
7: 'link',
8: 'text'
}
``` |
owanr/o1o2o3_large_r2_coedit | ---
dataset_info:
features:
- name: src
dtype: string
- name: tgt
sequence: string
splits:
- name: train
num_bytes: 18003794
num_examples: 35807
download_size: 7730296
dataset_size: 18003794
---
# Dataset Card for "o1o2o3_large_r2_coedit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Arsture/ideal-girlfriend2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 36234
num_examples: 88
download_size: 10043
dataset_size: 36234
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ideal-girlfriend2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allenai/aboutme | ---
language:
- en
tags:
- common crawl
- webtext
- social nlp
size_categories:
- 10M<n<100M
pretty_name: AboutMe
license: other
extra_gated_prompt: "Access to this dataset is automatically granted upon accepting the [**AI2 ImpACT License - Low Risk Artifacts (“LR Agreement”)**](https://allenai.org/licenses/impact-lr) and completing all fields below."
extra_gated_fields:
Your full name: text
Organization or entity you are affiliated with: text
State or country you are located in: text
Contact email: text
Please describe your intended use of the medium risk artifact(s): text
I AGREE to the terms and conditions of the MR Agreement above: checkbox
I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox
I CERTIFY that the information I have provided is true and accurate: checkbox
---
# AboutMe: Self-Descriptions in Webpages
## Dataset description
**Curated by:** Li Lucy, Suchin Gururangan, Luca Soldaini, Emma Strubell, David Bamman, Lauren Klein, Jesse Dodge
**Languages:** English
**License:** AI2 ImpACT License - Low Risk Artifacts
**Paper:** [https://arxiv.org/abs/2401.06408](https://arxiv.org/abs/2401.06408)
## Dataset sources
Common Crawl
## Uses
This dataset was originally created to document the effects of different pretraining data curation practices. It is intended for research use, e.g. AI evaluation and analysis of development pipelines or social scientific research of Internet communities and self-presentation.
## Dataset structure
This dataset consists of three parts:
- `about_pages`: webpages that are self-descriptions and profiles of website creators, or text *about* individuals and organizations on the web. These are zipped files with one json per line, with the following keys:
- `url`
- `hostname`
- `cc_segment` (for tracking where in Common Crawl the page is originally retrieved from)
- `text`
- `title` (webpage title)
- `sampled_pages`: random webpages from the same set of websites, or text created or curated *by* individuals and organizations on the web. It has the same keys as `about_pages`.
- `about_pages_meta`: algorithmically extracted information from "About" pages, including:
- `hn`: hostname of website
- `country`: the most frequent country of locations on the page, obtained using Mordecai3 geoparsing
- `roles`: social roles and occupations detected using RoBERTa based on expressions of self-identification, e.g. *I am a **dancer***. Each role is accompanied by sentence number and start/end character offsets.
- `class`: whether the page is detected to be an individual or organization
- `cluster`: one of fifty topical labels obtained via tf-idf clustering of "about" pages
Each file contains one json entry per line. Note that the entries in each file are not in a random order, but instead reflect an ordering outputted by CCNet (e.g. neighboring pages may be similar in Wikipedia-based perplexity.)
## Dataset creation
AboutMe is derived from twenty four snapshots of Common Crawl collected between 2020–05 and 2023–06. We extract text from raw Common Crawl using CCNet, and deduplicate URLs across all snapshots. We only include text that has a fastText English score > 0.5. "About" pages are identified using keywords in URLs (about, about-me, about-us, and bio), and their URLs end in `/keyword/` or `keyword.*`, e.g. `about.html`. We only include pages that have one candidate URL, to avoid ambiguity around which page is actually about the main website creator. If a webpage has both `https` and `http` versions in Common Crawl, we take the `https` version. The "sampled" pages are a single webpage randomly sampled from the website that has an "about" page.
More details on metadata creation can be found in our paper, linked above.
## Bias, Risks, and Limitations
Algorithmic measurements of textual content is scalable, but imperfect. We acknowledge that our dataset and analysis methods (e.g. classification, information retrieval) can also uphold language norms and standards that may disproportionately affect some social groups over others. We hope that future work continues to improve these content analysis pipelines, especially for long-tail or minoritized language phenomena.
We encourage future work using our dataset to minimize the extent to which they infer unlabeled or implicit information about subjects in this dataset, and to assess the risks of inferring various types of information from these pages. In addition, measurements of social identities from AboutMe pages are affected by reporting bias.
Future uses of this data should avoid incorporating personally identifiable information into generative models, report only aggregated results, and paraphrase quoted examples in papers to protect the privacy of subjects.
## Citation
```
@misc{lucy2024aboutme,
title={AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters},
author={Li Lucy and Suchin Gururangan and Luca Soldaini and Emma Strubell and David Bamman and Lauren Klein and Jesse Dodge},
year={2024},
eprint={2401.06408},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Dataset contact
lucy3_li@berkeley.edu |
result-kand2-sdxl-wuerst-karlo/a17bd262 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
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num_examples: 10
download_size: 1374
dataset_size: 201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a17bd262"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
presencesw/wmt15_fr_en | ---
dataset_info:
features:
- name: en
dtype: string
- name: fr
dtype: string
splits:
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num_bytes: 14759598012
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num_examples: 4503
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num_examples: 1500
download_size: 9665713863
dataset_size: 14761035504
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
irds/wikir_fr14k | ---
pretty_name: '`wikir/fr14k`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikir/fr14k`
The `wikir/fr14k` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikir#wikir/fr14k).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=736,616
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikir_fr14k', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Frej2020Wikir,
title={WIKIR: A Python toolkit for building a large-scale Wikipedia-based English Information Retrieval Dataset},
author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet},
booktitle={LREC},
year={2020}
}
@inproceedings{Frej2020MlWikir,
title={MLWIKIR: A Python Toolkit for Building Large-scale Wikipedia-based Information Retrieval Datasets in Chinese, English, French, Italian, Japanese, Spanish and More},
author={Jibril Frej and Didier Schwab and Jean-Pierre Chevallet},
booktitle={CIRCLE},
year={2020}
}
```
|
wolfserious/dataset1 | ---
license: apache-2.0
---
|
suthanhcong/fashion_items | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bag
'1': dress
'2': hat
'3': jacket
'4': pants
'5': shirt
'6': shoe
'7': shorts
'8': skirt
'9': sunglass
splits:
- name: train
num_bytes: 17338671.0
num_examples: 3000
download_size: 15627742
dataset_size: 17338671.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vidhikatkoria/FewShotSGD | ---
dataset_info:
features:
- name: domain
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: act
dtype: int64
- name: speaker
dtype: int64
splits:
- name: test
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num_examples: 15537
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num_examples: 83391
- name: validation
num_bytes: 6337305
num_examples: 11960
download_size: 6517762
dataset_size: 60378867
---
# Dataset Card for "FewShotSGD"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alvarobartt/evol-instruct-logging | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: evolved_instructions
sequence: string
- name: answer
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 145683
num_examples: 10
download_size: 138594
dataset_size: 145683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JaehyungKim/p2c_offensive | ---
license: other
license_name: following-original-dataset
license_link: LICENSE
---
|
income/climate-fever-top-20-gen-queries | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
---
# NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
- DocT5query model used: [BeIR/query-gen-msmarco-t5-base-v1](https://huggingface.co/BeIR/query-gen-msmarco-t5-base-v1)
- id (str): unique document id in NFCorpus in the BEIR benchmark (`corpus.jsonl`).
- Questions generated: 20
- Code used for generation: [evaluate_anserini_docT5query_parallel.py](https://github.com/beir-cellar/beir/blob/main/examples/retrieval/evaluation/sparse/evaluate_anserini_docT5query_parallel.py)
Below contains the old dataset card for the BEIR benchmark.
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.Top-20 generated queries for every passage in NFCorpus
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
serge-wilson/wolof_speech_transcription | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1746401219.7180312
num_examples: 12599
- name: test
num_bytes: 309529899.3475478
num_examples: 2245
download_size: 2043272901
dataset_size: 2055931119.065579
---
# Dataset Card for "wolof_speech_transcription"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
siswati_ner_corpus | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ss
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Siswati NER Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: siswati_ner_corpus
splits:
- name: train
num_bytes: 3517151
num_examples: 10798
download_size: 21882224
dataset_size: 3517151
---
# Dataset Card for Siswati NER Corpus
## 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:** [Siswati Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/346)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The Siswati Ner Corpus is a Siswati dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Siswati language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Siswati.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
```
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Tinsita', 'tebantfu', ':', 'tinsita', 'tetakhamiti']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - siswati.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{siswati_ner_corpus,
author = {B.B. Malangwane and
M.N. Kekana and
S.S. Sedibe and
B.C. Ndhlovu and
Roald Eiselen},
title = {NCHLT Siswati Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/346},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. |
caojianjian/LAMM | ---
license: apache-2.0
---
### LAMM-Dataset Directory Structure
├── 2D_Benchmark
│ ├── ai2d_images.zip
│ ├── celeba_images.zip
│ ├── cifar10_images.zip
│ ├── flickr30k_images.zip
│ ├── fsc147_images.zip
│ ├── lsp_images.zip
│ ├── sqaimage_images.zip
│ ├── svt_images.zip
│ ├── ucmerced_images.zip
│ ├── voc2012_images.zip
│ ├── meta_file
│ │ ├── Caption_flickr30k.json
│ │ ├── Classification_CIFAR10.json
│ │ ├── Counting_FSC147.json
│ │ ├── Detection_VOC2012.json
│ │ ├── Facial_Classification_CelebA(Hair).json
│ │ ├── Facial_Classification_CelebA(Smile).json
│ │ ├── Fine-grained_Classification_UCMerced.json
│ │ ├── Keypoints_Dectection_LSP.json
│ │ ├── Locating_FSC147.json
│ │ ├── Locating_LSP.json
│ │ ├── Locating_VOC2012.json
│ │ ├── OCR_SVT.json
│ │ ├── VQA_AI2D.json
│ │ └── VQA_SQAimage.json
├── 2D_Instruct
│ ├── bamboo_images.zip
│ ├── coco_images.zip
│ ├── locount_images.zip
│ ├── textvqa_images.zip
│ ├── meta_file
│ │ ├── daily_dialogue_49k.json
│ │ ├── detailed_description_49k.json
│ │ ├── factual_knowledge_dialogue_42k.json
│ │ ├── LAMM_instruct_140k.json
│ │ ├── LAMM_instruct_186k.json
│ │ ├── LAMM_instruct_98k.json
│ │ └── vision_task_dialogue_46k.json
├── 3D_Benchmark
│ ├── scannet_pcls.zip
│ ├── meta_file
│ │ ├── Detection_ScanNet.json
│ │ ├── VG_ScanRefer.json
│ │ └── VQA_ScanQA_multiplechoice.json
└── 3D_Instruct
├── 3rscan_pcls.zip
├── shapenet_pcls.zip
├── meta_file
│ └── LAMM_3dinstruct_10k.json
|
akadhim-ai/martin_valen_dataset_10 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 82775.0
num_examples: 10
download_size: 82229
dataset_size: 82775.0
---
# Dataset Card for "martin_valen_dataset_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
asdasdsadsaweqweeasdsad/skywolfdata | ---
license: apache-2.0
---
|
autoevaluate/autoeval-eval-jeffdshen__neqa0_8shot-jeffdshen__neqa0_8shot-5a61bc-1852963394 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/neqa0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: jeffdshen/neqa0_8shot
dataset_config: jeffdshen--neqa0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: jeffdshen/neqa0_8shot
* Config: jeffdshen--neqa0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. |
alvations/c4p0-x1-de-en | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: target_backto_source
dtype: string
- name: raw_target
list:
- name: generated_text
dtype: string
- name: raw_target_backto_source
list:
- name: generated_text
dtype: string
- name: prompt
dtype: string
- name: reverse_prompt
dtype: string
- name: source_langid
dtype: string
- name: target_langid
dtype: string
- name: target_backto_source_langid
dtype: string
- name: doc_id
dtype: int64
- name: sent_id
dtype: int64
- name: timestamp
dtype: timestamp[us]
- name: url
dtype: string
- name: doc_hash
dtype: string
splits:
- name: train
num_bytes: 32165
num_examples: 31
download_size: 22249
dataset_size: 32165
configs:
- config_name: default
data_files:
- split: train
path: c0d4dc8660289947/train-*
---
|
universalner/uner_llm_inst_croatian | ---
license: cc-by-sa-4.0
language:
- hr
task_categories:
- token-classification
dataset_info:
#- config_name: hr_set
# splits:
# - name: test
# num_examples: 1135
# - name: dev
# num_examples: 959
# - name: train
# num_examples: 6917
---
# Dataset Card for Universal NER v1 in the Aya format - Croatian subset
This dataset is a format conversion for the Croatian data in the original Universal NER v1 into the Aya instruction format and it's released here under the same CC-BY-SA 4.0 license and conditions.
The dataset contains different subsets and their dev/test/train splits, depending on language. For more details, please refer to:
## Dataset Details
For the original Universal NER dataset v1 and more details, please check https://huggingface.co/datasets/universalner/universal_ner.
For details on the conversion to the Aya instructions format, please see the complete version: https://huggingface.co/datasets/universalner/uner_llm_instructions
## Citation
If you utilize this dataset version, feel free to cite/footnote the complete version at https://huggingface.co/datasets/universalner/uner_llm_instructions, but please also cite the *original dataset publication*.
**BibTeX:**
```
@preprint{mayhew2023universal,
title={{Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark}},
author={Stephen Mayhew and Terra Blevins and Shuheng Liu and Marek Šuppa and Hila Gonen and Joseph Marvin Imperial and Börje F. Karlsson and Peiqin Lin and Nikola Ljubešić and LJ Miranda and Barbara Plank and Arij Riabi and Yuval Pinter},
year={2023},
eprint={2311.09122},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
joey234/mmlu-high_school_world_history-rule-neg | ---
dataset_info:
features:
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: question
dtype: string
splits:
- name: test
num_bytes: 380246
num_examples: 237
download_size: 200389
dataset_size: 380246
---
# Dataset Card for "mmlu-high_school_world_history-rule-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 43386
num_examples: 100
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 43408
num_examples: 100
download_size: 20841
dataset_size: 86794
---
# Dataset Card for "OxfordFlowers_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/89cada8f | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1337
dataset_size: 180
---
# Dataset Card for "89cada8f"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tsuinzues/rarity | ---
license: openrail
---
|
ReginaFoley/sar_data_512 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 4195311400.0
num_examples: 8000
download_size: 3282557159
dataset_size: 4195311400.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Moghazy/xyz | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 75274
num_examples: 398
download_size: 16836
dataset_size: 75274
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "xyz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/skeletons_art_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 20339280
num_examples: 100000
download_size: 1119913
dataset_size: 20339280
---
# Dataset Card for "skeletons_art_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
emozilla/booksum-summary-analysis | ---
language: en
dataset_info:
features:
- name: chapter
dtype: string
- name: text
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 215494460.97875556
num_examples: 11834
- name: test
num_bytes: 27122769.0
num_examples: 1658
- name: validation
num_bytes: 43846669.0
num_examples: 2234
download_size: 134838536
dataset_size: 286463898.9787556
---
# Dataset Card for "booksum-summary-analysis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/1070906e | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1329
dataset_size: 180
---
# Dataset Card for "1070906e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Gustav114514/work | ---
language: ja
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Japanese by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice ja
type: common_voice
args: ja
metrics:
- name: Test WER
type: wer
value: 81.80
- name: Test CER
type: cer
value: 20.16
---
# Fine-tuned XLSR-53 large model for speech recognition in Japanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Usage
The model can be used directly (without a language model) as follows...
Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
```python
from huggingsound import SpeechRecognitionModel
model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
transcriptions = model.transcribe(audio_paths)
```
Writing your own inference script:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "ja"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
SAMPLES = 10
test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = batch["sentence"].upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
for i, predicted_sentence in enumerate(predicted_sentences):
print("-" * 100)
print("Reference:", test_dataset[i]["sentence"])
print("Prediction:", predicted_sentence)
```
| Reference | Prediction |
| ------------- | ------------- |
| 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 人母は重にきね起くさいがしている |
| 財布をなくしたので、交番へ行きます。 | 財布をなく手端ので勾番へ行きます |
| 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ宮屋のお親じ旅館の主に医者をはじめ交際のアル人トに聞いて回ったらみんな私より収入が多いはなうに税金は安い |
| 新しい靴をはいて出かけます。 | だらしい靴をはいて出かけます |
| このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表弁することがある |
| 松井さんはサッカーより野球のほうが上手です。 | 松井さんはサッカーより野球のほうが上手です |
| 新しいお皿を使います。 | 新しいお皿を使います |
| 結婚以来三年半ぶりの東京も、旧友とのお酒も、夜行列車も、駅で寝て、朝を待つのも久しぶりだ。 | 結婚ル二来三年半降りの東京も吸とのお酒も野越者も駅で寝て朝を待つの久しぶりた |
| これまで、少年野球、ママさんバレーなど、地域スポーツを支え、市民に密着してきたのは、無数のボランティアだった。 | これまで少年野球<unk>三バレーなど地域スポーツを支え市民に満着してきたのは娘数のボランティアだった |
| 靴を脱いで、スリッパをはきます。 | 靴を脱いでスイパーをはきます |
## Evaluation
The model can be evaluated as follows on the Japanese test data of Common Voice.
```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "ja"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
test_dataset = load_dataset("common_voice", LANG_ID, split="test")
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```
**Test Result**:
In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| jonatasgrosman/wav2vec2-large-xlsr-53-japanese | **81.80%** | **20.16%** |
| vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | 23.40% |
| qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 70.77% |
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{grosman2021xlsr53-large-japanese,
title={Fine-tuned {XLSR}-53 large model for speech recognition in {J}apanese},
author={Grosman, Jonatas},
howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese}},
year={2021}
}
``` |
deetsadi/processed_dwi_with_adc | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 35260067.0
num_examples: 200
download_size: 0
dataset_size: 35260067.0
---
# Dataset Card for "processed_dwi_with_adc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CanadianGamer/Flirty-Dialouge | ---
license: apache-2.0
---
|
hongerzh/nft_prediction_all_with_dates | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: float64
- name: time
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 5747708188.67
num_examples: 29339
- name: validation
num_bytes: 1910519375.185
num_examples: 9777
- name: test
num_bytes: 2129490317.38
num_examples: 9780
download_size: 9022605212
dataset_size: 9787717881.235
---
# Dataset Card for "nft_prediction_all_with_dates"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
akkasi/NLI4CT | ---
dataset_info:
features:
- name: Ids
dtype: string
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 54080452
num_examples: 155754
- name: validation
num_bytes: 4928949
num_examples: 14432
download_size: 6497603
dataset_size: 59009401
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
HuggingFaceH4/llava-instruct-mix-vsft | ---
dataset_info:
features:
- name: messages
list:
- name: content
list:
- name: index
dtype: int64
- name: text
dtype: string
- name: type
dtype: string
- name: role
dtype: string
- name: images
sequence: image
splits:
- name: train
num_bytes: 9992582190.928007
num_examples: 259155
- name: test
num_bytes: 525935525.39699405
num_examples: 13640
download_size: 11407075653
dataset_size: 10518517716.325
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
[theblackcat102/llava-instruct-mix](https://huggingface.co/datasets/theblackcat102/llava-instruct-mix) reformated for VSFT with TRL's SFT Trainer.
See https://github.com/huggingface/trl/blob/main/examples/scripts/vsft_llava.py.
|
adamjweintraut/bart-finetuned-lyrlen-256-tokens_2024-03-22_run | ---
dataset_info:
features:
- name: id
dtype: int64
- name: orig
dtype: string
- name: predicted
dtype: string
- name: label
dtype: string
- name: rougeL_min_precision
dtype: float64
- name: rougeL_min_recall
dtype: float64
- name: rougeL_min_fmeasure
dtype: float64
- name: rougeL_median_precision
dtype: float64
- name: rougeL_median_recall
dtype: float64
- name: rougeL_median_fmeasure
dtype: float64
- name: rougeL_max_precision
dtype: float64
- name: rougeL_max_recall
dtype: float64
- name: rougeL_max_fmeasure
dtype: float64
- name: predicted_label_sim
dtype: float32
splits:
- name: train
num_bytes: 127244
num_examples: 50
download_size: 80501
dataset_size: 127244
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Rewcifer/trainset3_2000_cutoff_llama | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 249703784.98341143
num_examples: 50000
download_size: 45234048
dataset_size: 249703784.98341143
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "trainset3_2000_cutoff_llama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mekaneeky/Synthetic_Ateso_VITS_22.5k | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: eng
dtype: string
- name: lug
dtype: string
- name: ach
dtype: string
- name: teo
dtype: string
- name: lgg
dtype: string
- name: nyn
dtype: string
- name: ID
dtype: string
- name: teo_tts
sequence:
sequence: float32
splits:
- name: train
num_bytes: 12491742528
num_examples: 23947
- name: dev
num_bytes: 260929100
num_examples: 500
- name: test
num_bytes: 264178952
num_examples: 500
download_size: 13028184575
dataset_size: 13016850580
---
# Dataset Card for "Synthetic_Ateso_VITS_22.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kpriyanshu256/MultiTabQA-multitable_pretraining-train-v2-7000 | ---
dataset_info:
features:
- name: tables
sequence: string
- name: table_names
sequence: string
- name: query
dtype: string
- name: answer
dtype: string
- name: source
dtype: string
- name: target
dtype: string
- name: source_latex
dtype: string
- name: target_latex
dtype: string
- name: source_html
dtype: string
- name: target_html
dtype: string
- name: source_markdown
dtype: string
- name: target_markdown
dtype: string
splits:
- name: train
num_bytes: 2422554050
num_examples: 500
download_size: 487473476
dataset_size: 2422554050
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_85 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1447706408.0
num_examples: 282094
download_size: 1481896823
dataset_size: 1447706408.0
---
# Dataset Card for "chunk_85"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-joelito__brazilian_court_decisions-joelito__brazilian_c-4bed1b-1985466167 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- joelito/brazilian_court_decisions
eval_info:
task: multi_class_classification
model: Luciano/bertimbau-base-finetuned-brazilian_court_decisions
metrics: []
dataset_name: joelito/brazilian_court_decisions
dataset_config: joelito--brazilian_court_decisions
dataset_split: test
col_mapping:
text: decision_description
target: judgment_label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Luciano/bertimbau-base-finetuned-brazilian_court_decisions
* Dataset: joelito/brazilian_court_decisions
* Config: joelito--brazilian_court_decisions
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Luciano](https://huggingface.co/Luciano) for evaluating this model. |
unalignment/airoboros-2.2 | ---
license: other
tags:
- not-for-all-audiences
---
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
__*I've gated access with request, due to the de-alignment data. To download, you must agree to the following:*__
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with extreme caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- https://huggingface.co/jondurbin/airoboros-l2-70b-2.1/discussions/3#64f325ce352152814d1f796a
- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/225#64f0997659da193a12b78c32
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used `thenlper/gte-small` to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### De-alignment
I included a small sampling of "de-alignment" data. The llama-2 base models seem extremely reluctant to discuss certain topics, curse, or otherwise produce other-than-pg content. I don't want a vile model, but I also don't *NOT* want a vile model.
- comedy skits, to add more comedy and occasional cursing
- instruction/response pairs that would typically otherwise be refused
- various (LLM ehanced) stories from the internet with somewhat spicy content
- story-writing tasks as a re-telling of popular horror/fantasy films (by default, the LLM generated stories often have too sunny of a disposition, so hopefully this will allow for some twists or more interesting stories)
- rude responses (if a character card specifies they are rude and curse, it should actually do so without prompt hacking IMO)
None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web. Use with extreme caution, particularly in locations with strict speech laws!
See "instructions-clean.jsonl" for a version without dealignment data.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting. |
AdapterOcean/chemistry_dataset_standardized_cluster_2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 31918454
num_examples: 3339
download_size: 8651715
dataset_size: 31918454
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "chemistry_dataset_standardized_cluster_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ariesutiono/entailment-bank-v3 |
---
license: cc-by-4.0
---
# Entailment bank dataset
This dataset raw source can be found at [allenai's Github](https://github.com/allenai/entailment_bank/).
If you use this dataset, it is best to cite the original paper
```
@article{entalmentbank2021,
title={Explaining Answers with Entailment Trees},
author={Dalvi, Bhavana and Jansen, Peter and Tafjord, Oyvind and Xie, Zhengnan and Smith, Hannah and Pipatanangkura, Leighanna and Clark, Peter},
journal={EMNLP},
year={2021}
}
``` |
autoevaluate/autoeval-eval-mathemakitten__winobias_antistereotype_test_cot_v1-math-1bbcaf-1917164991 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- mathemakitten/winobias_antistereotype_test_cot_v1
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-2.7b_eval
metrics: []
dataset_name: mathemakitten/winobias_antistereotype_test_cot_v1
dataset_config: mathemakitten--winobias_antistereotype_test_cot_v1
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-2.7b_eval
* Dataset: mathemakitten/winobias_antistereotype_test_cot_v1
* Config: mathemakitten--winobias_antistereotype_test_cot_v1
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mathemakitten](https://huggingface.co/mathemakitten) for evaluating this model. |
minskiter/msra_dev | ---
dataset_info:
features:
- name: text
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': B-NS
'2': M-NS
'3': E-NS
'4': S-NS
'5': B-NT
'6': M-NT
'7': E-NT
'8': S-NT
'9': B-NR
'10': M-NR
'11': E-NR
'12': S-NR
splits:
- name: train
num_bytes: 32917977
num_examples: 46364
- name: validation
num_bytes: 2623860
num_examples: 4365
- name: test
num_bytes: 2623860
num_examples: 4365
download_size: 4762958
dataset_size: 38165697
---
### How to loading dataset?
```python
from datasets import load_dataset
datasets = load_dataset("minskiter/msra_dev",save_infos=True)
train,test = datasets['train'],datasets['test']
# convert label to str
print(train.features['labels'].feature.int2str(0))
```
### Force update
```python
from datasets import load_dataset
datasets = load_dataset("minskiter/msra_dev", download_mode="force_redownload")
```
### Fit your train
```python
def transform(example):
# edit example here
return example
for key in datasets:
datasets[key] = datasets.map(transform)
``` |
autoevaluate/autoeval-eval-ccdv__arxiv-summarization-section-8d788a-42021145085 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ccdv/arxiv-summarization
eval_info:
task: summarization
model: ArtifactAI/led_large_16384_arxiv_summarization
metrics: []
dataset_name: ccdv/arxiv-summarization
dataset_config: section
dataset_split: test
col_mapping:
text: article
target: abstract
---
# 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: ArtifactAI/led_large_16384_arxiv_summarization
* Dataset: ccdv/arxiv-summarization
* Config: section
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@ArtifactAI](https://huggingface.co/ArtifactAI) for evaluating this model. |
result-kand2-sdxl-wuerst-karlo/e82d3dfd | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1327
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e82d3dfd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LLukas22/cqadupstack | ---
license: apache-2.0
task_categories:
- sentence-similarity
- feature-extraction
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "cqadupstack"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** [http://nlp.cis.unimelb.edu.au/resources/cqadupstack/](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
### Dataset Summary
This is a preprocessed version of cqadupstack, to make it easily consumable via huggingface. The original dataset can be found [here](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/).
CQADupStack is a benchmark dataset for community question-answering (cQA) research. It contains threads from twelve StackExchange1 subforums, annotated with duplicate question information and comes with pre-defined training, development, and test splits, both for retrieval and classification experiments.
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{
"question": "Very often, when some unknown company is calling me, in couple of seconds I see its name and logo on standard ...",
"answer": "You didn't explicitely mention it, but from the context I assume you're using a device with Android 4.4 (Kitkat). With that ...",
"title": "Why Dialer shows contact name and image, when contact is not in my address book?",
"forum_tag": "android"
}
```
### Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `title`: a `string` feature.
- `forum_tag`: a categorical `string` feature.
## Additional Information
### Licensing Information
This dataset is distributed under the Apache 2.0 licence.
|
duanqin/training_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 270718.0
num_examples: 3
download_size: 253883
dataset_size: 270718.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Ram07/text-extract-1 | ---
license: mit
---
|
autoevaluate/autoeval-staging-eval-launch__gov_report-plain_text-2fa37c-16136224 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- launch/gov_report
eval_info:
task: summarization
model: Blaise-g/longt5_tglobal_large_sumpubmed
metrics: ['bertscore']
dataset_name: launch/gov_report
dataset_config: plain_text
dataset_split: test
col_mapping:
text: document
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: Blaise-g/longt5_tglobal_large_sumpubmed
* Dataset: launch/gov_report
* Config: plain_text
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. |
open-llm-leaderboard/details_Fredithefish__Guanaco-7B-Uncensored | ---
pretty_name: Evaluation run of Fredithefish/Guanaco-7B-Uncensored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Fredithefish/Guanaco-7B-Uncensored](https://huggingface.co/Fredithefish/Guanaco-7B-Uncensored)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Fredithefish__Guanaco-7B-Uncensored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T17:19:39.610338](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-7B-Uncensored/blob/main/results_2023-10-12T17-19-39.610338.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001363255033557047,\n\
\ \"em_stderr\": 0.00037786091964606556,\n \"f1\": 0.05823930369127524,\n\
\ \"f1_stderr\": 0.001346062439091187,\n \"acc\": 0.38665835314476715,\n\
\ \"acc_stderr\": 0.009009374850629389\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964606556,\n\
\ \"f1\": 0.05823930369127524,\n \"f1_stderr\": 0.001346062439091187\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04245640636846096,\n \
\ \"acc_stderr\": 0.005553837749990045\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7308602999210734,\n \"acc_stderr\": 0.012464911951268733\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Fredithefish/Guanaco-7B-Uncensored
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|arc:challenge|25_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T17_19_39.610338
path:
- '**/details_harness|drop|3_2023-10-12T17-19-39.610338.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T17-19-39.610338.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T17_19_39.610338
path:
- '**/details_harness|gsm8k|5_2023-10-12T17-19-39.610338.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T17-19-39.610338.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hellaswag|10_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T09:42:26.662725.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T09:42:26.662725.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-05T09:42:26.662725.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T17_19_39.610338
path:
- '**/details_harness|winogrande|5_2023-10-12T17-19-39.610338.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T17-19-39.610338.parquet'
- config_name: results
data_files:
- split: 2023_09_05T09_42_26.662725
path:
- results_2023-09-05T09:42:26.662725.parquet
- split: 2023_10_12T17_19_39.610338
path:
- results_2023-10-12T17-19-39.610338.parquet
- split: latest
path:
- results_2023-10-12T17-19-39.610338.parquet
---
# Dataset Card for Evaluation run of Fredithefish/Guanaco-7B-Uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/Guanaco-7B-Uncensored
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Fredithefish/Guanaco-7B-Uncensored](https://huggingface.co/Fredithefish/Guanaco-7B-Uncensored) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Fredithefish__Guanaco-7B-Uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T17:19:39.610338](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__Guanaco-7B-Uncensored/blob/main/results_2023-10-12T17-19-39.610338.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964606556,
"f1": 0.05823930369127524,
"f1_stderr": 0.001346062439091187,
"acc": 0.38665835314476715,
"acc_stderr": 0.009009374850629389
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964606556,
"f1": 0.05823930369127524,
"f1_stderr": 0.001346062439091187
},
"harness|gsm8k|5": {
"acc": 0.04245640636846096,
"acc_stderr": 0.005553837749990045
},
"harness|winogrande|5": {
"acc": 0.7308602999210734,
"acc_stderr": 0.012464911951268733
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
Melanit/testset | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: example
num_bytes: 5698770.0
num_examples: 10
download_size: 4383029
dataset_size: 5698770.0
configs:
- config_name: default
data_files:
- split: example
path: data/example-*
---
# Dataset Card for "testset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DL3DV/DL3DV-ALL-480P | ---
tags:
- 3D Vision
- NeRF
- 3D Gaussian
- Dataset
- Novel View Synthesis
- Text to 3D
- Image to 3D
pretty_name: Dl3DV-Dataset
size_categories:
- 100B<n<1T
---
# DL3DV-Dataset
This repo has all the 480P frames with camera poses of DL3DV-10K Dataset. We are working hard to review all the dataset to avoid sensitive information. Thank you for your patience.
# Download
If you have enough space, you can use git to download a dataset from huggingface. See this [link](https://huggingface.co/docs/hub/en/datasets-downloading). [480P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-480P)/[960P](https://huggingface.co/datasets/DL3DV/DL3DV-ALL-960P) versions should satisfies most needs.
If you do not have enough space, we further provide a [download script](https://github.com/DL3DV-10K/Dataset/blob/main/scripts/download.py) here to download a subset. The usage:
```Bash
usage: download.py [-h] --odir ODIR --subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K} --resolution {4K,2K,960P,480P} --file_type {images+poses,video,colmap_cache} [--hash HASH]
[--clean_cache]
optional arguments:
-h, --help show this help message and exit
--odir ODIR output directory
--subset {1K,2K,3K,4K,5K,6K,7K,8K,9K,10K}
The subset of the benchmark to download
--resolution {4K,2K,960P,480P}
The resolution to donwnload
--file_type {images+poses,video,colmap_cache}
The file type to download
--hash HASH If set subset=hash, this is the hash code of the scene to download
--clean_cache If set, will clean the huggingface cache to save space
```
Here are some examples:
```Bash
# Make sure you have applied for the access.
# Use this to download the download.py script
wget https://raw.githubusercontent.com/DL3DV-10K/Dataset/main/scripts/download.py
# Download 480P resolution images and poses, 0~1K subset, output to DL3DV-10K directory
python download.py --odir DL3DV-10K --subset 1K --resolution 480P --file_type images+poses --clean_cache
# Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory
python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --clean_cache
```
You can also download a specific scene with its hash. The scene-hash pair visualization can be found [here](https://htmlpreview.github.io/?https://github.com/DL3DV-10K/Dataset/blob/main/visualize/index.html).
```Bash
# Download 480P resolution images and poses, 1K~2K subset, output to DL3DV-10K directory
python download.py --odir DL3DV-10K --subset 2K --resolution 480P --file_type images+poses --hash e2cedefea8a0ed2d0ffbd5bdc08acbe7e1f85c96f72f7b790e9dfe1c98963047 --clean_cache
```
# News
- [x] DL3DV-1K, 2K, 3K, 4K
- [ ] DL3DV-5K ~ 10K |
linhphanff/phobert-vietnamse-nomic-embed-mlm-dummy | ---
license: apache-2.0
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: special_tokens_mask
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 7391280
num_examples: 515
download_size: 2063633
dataset_size: 7391280
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sanagnos/processed_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 24027415200.0
num_examples: 6674282
download_size: 5731603526
dataset_size: 24027415200.0
---
# Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Trino123/lex-friedman-chunked | ---
license: mit
---
|
jadasdn/sv_corpora_parliament_processed | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 292351437
num_examples: 1892723
download_size: 161955796
dataset_size: 292351437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_facebook__galactica-1.3b | ---
pretty_name: Evaluation run of facebook/galactica-1.3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [facebook/galactica-1.3b](https://huggingface.co/facebook/galactica-1.3b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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 agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_facebook__galactica-1.3b\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-02T13:58:08.758268](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__galactica-1.3b/blob/main/results_2023-09-02T13%3A58%3A08.758268.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\": 0.2724936919306173,\n\
\ \"acc_stderr\": 0.03226048262400512,\n \"acc_norm\": 0.2744720231892299,\n\
\ \"acc_norm_stderr\": 0.03227238640653428,\n \"mc1\": 0.2484700122399021,\n\
\ \"mc1_stderr\": 0.015127427096520667,\n \"mc2\": 0.41399712836660274,\n\
\ \"mc2_stderr\": 0.01494063292915903\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2960750853242321,\n \"acc_stderr\": 0.013340916085246261,\n\
\ \"acc_norm\": 0.3412969283276451,\n \"acc_norm_stderr\": 0.01385583128749772\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.3375821549492133,\n\
\ \"acc_stderr\": 0.004719187890948067,\n \"acc_norm\": 0.40908185620394344,\n\
\ \"acc_norm_stderr\": 0.004906595857916765\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n\
\ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.34074074074074073,\n\
\ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.03459777606810536,\n\
\ \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.03459777606810536\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.2,\n\
\ \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \
\ \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.26037735849056604,\n \"acc_stderr\": 0.027008766090708094,\n\
\ \"acc_norm\": 0.26037735849056604,\n \"acc_norm_stderr\": 0.027008766090708094\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\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\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24277456647398843,\n\
\ \"acc_stderr\": 0.0326926380614177,\n \"acc_norm\": 0.24277456647398843,\n\
\ \"acc_norm_stderr\": 0.0326926380614177\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.04023382273617749,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.04023382273617749\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n\
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3148936170212766,\n \"acc_stderr\": 0.030363582197238167,\n\
\ \"acc_norm\": 0.3148936170212766,\n \"acc_norm_stderr\": 0.030363582197238167\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n\
\ \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.26455026455026454,\n \"acc_stderr\": 0.022717467897708624,\n \"\
acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.022717467897708624\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n\
\ \"acc_stderr\": 0.03268454013011743,\n \"acc_norm\": 0.15873015873015872,\n\
\ \"acc_norm_stderr\": 0.03268454013011743\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.27741935483870966,\n\
\ \"acc_stderr\": 0.025470196835900055,\n \"acc_norm\": 0.27741935483870966,\n\
\ \"acc_norm_stderr\": 0.025470196835900055\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.29064039408866993,\n \"acc_stderr\": 0.0319474007226554,\n\
\ \"acc_norm\": 0.29064039408866993,\n \"acc_norm_stderr\": 0.0319474007226554\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.30303030303030304,\n \"acc_stderr\": 0.03588624800091708,\n\
\ \"acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.03588624800091708\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.2727272727272727,\n \"acc_stderr\": 0.03173071239071724,\n \"\
acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03173071239071724\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.29015544041450775,\n \"acc_stderr\": 0.032752644677915166,\n\
\ \"acc_norm\": 0.29015544041450775,\n \"acc_norm_stderr\": 0.032752644677915166\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2846153846153846,\n \"acc_stderr\": 0.022878322799706283,\n\
\ \"acc_norm\": 0.2846153846153846,\n \"acc_norm_stderr\": 0.022878322799706283\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.27037037037037037,\n \"acc_stderr\": 0.027080372815145668,\n \
\ \"acc_norm\": 0.27037037037037037,\n \"acc_norm_stderr\": 0.027080372815145668\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.24789915966386555,\n \"acc_stderr\": 0.028047967224176896,\n\
\ \"acc_norm\": 0.24789915966386555,\n \"acc_norm_stderr\": 0.028047967224176896\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658753,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658753\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.25688073394495414,\n \"acc_stderr\": 0.018732492928342465,\n \"\
acc_norm\": 0.25688073394495414,\n \"acc_norm_stderr\": 0.018732492928342465\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2638888888888889,\n \"acc_stderr\": 0.03005820270430985,\n \"\
acc_norm\": 0.2638888888888889,\n \"acc_norm_stderr\": 0.03005820270430985\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591361,\n \"\
acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591361\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.28270042194092826,\n \"acc_stderr\": 0.029312814153955924,\n \
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.029312814153955924\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.27802690582959644,\n\
\ \"acc_stderr\": 0.03006958487449405,\n \"acc_norm\": 0.27802690582959644,\n\
\ \"acc_norm_stderr\": 0.03006958487449405\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22137404580152673,\n \"acc_stderr\": 0.0364129708131373,\n\
\ \"acc_norm\": 0.22137404580152673,\n \"acc_norm_stderr\": 0.0364129708131373\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.3884297520661157,\n \"acc_stderr\": 0.04449270350068382,\n \"\
acc_norm\": 0.3884297520661157,\n \"acc_norm_stderr\": 0.04449270350068382\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3067484662576687,\n \"acc_stderr\": 0.036230899157241474,\n\
\ \"acc_norm\": 0.3067484662576687,\n \"acc_norm_stderr\": 0.036230899157241474\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.043642261558410445,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.043642261558410445\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.20388349514563106,\n \"acc_stderr\": 0.03989139859531771,\n\
\ \"acc_norm\": 0.20388349514563106,\n \"acc_norm_stderr\": 0.03989139859531771\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.02934311479809445,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.02934311479809445\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2835249042145594,\n\
\ \"acc_stderr\": 0.01611731816683229,\n \"acc_norm\": 0.2835249042145594,\n\
\ \"acc_norm_stderr\": 0.01611731816683229\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.28034682080924855,\n \"acc_stderr\": 0.02418242749657761,\n\
\ \"acc_norm\": 0.28034682080924855,\n \"acc_norm_stderr\": 0.02418242749657761\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\
\ \"acc_stderr\": 0.01440029642922559,\n \"acc_norm\": 0.24581005586592178,\n\
\ \"acc_norm_stderr\": 0.01440029642922559\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.27124183006535946,\n \"acc_stderr\": 0.02545775669666788,\n\
\ \"acc_norm\": 0.27124183006535946,\n \"acc_norm_stderr\": 0.02545775669666788\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3215434083601286,\n\
\ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.3215434083601286,\n\
\ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.3055555555555556,\n \"acc_stderr\": 0.025630824975621358,\n\
\ \"acc_norm\": 0.3055555555555556,\n \"acc_norm_stderr\": 0.025630824975621358\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.26595744680851063,\n \"acc_stderr\": 0.026358065698880592,\n \
\ \"acc_norm\": 0.26595744680851063,\n \"acc_norm_stderr\": 0.026358065698880592\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2685788787483703,\n\
\ \"acc_stderr\": 0.011320056629121734,\n \"acc_norm\": 0.2685788787483703,\n\
\ \"acc_norm_stderr\": 0.011320056629121734\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.2610294117647059,\n \"acc_stderr\": 0.026679252270103114,\n\
\ \"acc_norm\": 0.2610294117647059,\n \"acc_norm_stderr\": 0.026679252270103114\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.3022875816993464,\n \"acc_stderr\": 0.018579232711113877,\n \
\ \"acc_norm\": 0.3022875816993464,\n \"acc_norm_stderr\": 0.018579232711113877\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.23636363636363636,\n\
\ \"acc_stderr\": 0.040693063197213775,\n \"acc_norm\": 0.23636363636363636,\n\
\ \"acc_norm_stderr\": 0.040693063197213775\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2653061224489796,\n \"acc_stderr\": 0.028263889943784606,\n\
\ \"acc_norm\": 0.2653061224489796,\n \"acc_norm_stderr\": 0.028263889943784606\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\
\ \"acc_stderr\": 0.030360490154014673,\n \"acc_norm\": 0.24378109452736318,\n\
\ \"acc_norm_stderr\": 0.030360490154014673\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.29518072289156627,\n\
\ \"acc_stderr\": 0.03550920185689631,\n \"acc_norm\": 0.29518072289156627,\n\
\ \"acc_norm_stderr\": 0.03550920185689631\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.26900584795321636,\n \"acc_stderr\": 0.0340105262010409,\n\
\ \"acc_norm\": 0.26900584795321636,\n \"acc_norm_stderr\": 0.0340105262010409\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2484700122399021,\n\
\ \"mc1_stderr\": 0.015127427096520667,\n \"mc2\": 0.41399712836660274,\n\
\ \"mc2_stderr\": 0.01494063292915903\n }\n}\n```"
repo_url: https://huggingface.co/facebook/galactica-1.3b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|arc:challenge|25_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hellaswag|10_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T13:58:08.758268.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-02T13:58:08.758268.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T13:58:08.758268.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-02T13:58:08.758268.parquet'
- config_name: results
data_files:
- split: 2023_09_02T13_58_08.758268
path:
- results_2023-09-02T13:58:08.758268.parquet
- split: latest
path:
- results_2023-09-02T13:58:08.758268.parquet
---
# Dataset Card for Evaluation run of facebook/galactica-1.3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/facebook/galactica-1.3b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [facebook/galactica-1.3b](https://huggingface.co/facebook/galactica-1.3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_facebook__galactica-1.3b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-02T13:58:08.758268](https://huggingface.co/datasets/open-llm-leaderboard/details_facebook__galactica-1.3b/blob/main/results_2023-09-02T13%3A58%3A08.758268.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": 0.2724936919306173,
"acc_stderr": 0.03226048262400512,
"acc_norm": 0.2744720231892299,
"acc_norm_stderr": 0.03227238640653428,
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520667,
"mc2": 0.41399712836660274,
"mc2_stderr": 0.01494063292915903
},
"harness|arc:challenge|25": {
"acc": 0.2960750853242321,
"acc_stderr": 0.013340916085246261,
"acc_norm": 0.3412969283276451,
"acc_norm_stderr": 0.01385583128749772
},
"harness|hellaswag|10": {
"acc": 0.3375821549492133,
"acc_stderr": 0.004719187890948067,
"acc_norm": 0.40908185620394344,
"acc_norm_stderr": 0.004906595857916765
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.03459777606810536,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.03459777606810536
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.26037735849056604,
"acc_stderr": 0.027008766090708094,
"acc_norm": 0.26037735849056604,
"acc_norm_stderr": 0.027008766090708094
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"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": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.24277456647398843,
"acc_stderr": 0.0326926380614177,
"acc_norm": 0.24277456647398843,
"acc_norm_stderr": 0.0326926380614177
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.04023382273617749,
"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.04023382273617749
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3148936170212766,
"acc_stderr": 0.030363582197238167,
"acc_norm": 0.3148936170212766,
"acc_norm_stderr": 0.030363582197238167
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.04142439719489362,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.04142439719489362
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.036951833116502325,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.036951833116502325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.26455026455026454,
"acc_stderr": 0.022717467897708624,
"acc_norm": 0.26455026455026454,
"acc_norm_stderr": 0.022717467897708624
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.15873015873015872,
"acc_stderr": 0.03268454013011743,
"acc_norm": 0.15873015873015872,
"acc_norm_stderr": 0.03268454013011743
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.27741935483870966,
"acc_stderr": 0.025470196835900055,
"acc_norm": 0.27741935483870966,
"acc_norm_stderr": 0.025470196835900055
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.29064039408866993,
"acc_stderr": 0.0319474007226554,
"acc_norm": 0.29064039408866993,
"acc_norm_stderr": 0.0319474007226554
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.30303030303030304,
"acc_stderr": 0.03588624800091708,
"acc_norm": 0.30303030303030304,
"acc_norm_stderr": 0.03588624800091708
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.2727272727272727,
"acc_stderr": 0.03173071239071724,
"acc_norm": 0.2727272727272727,
"acc_norm_stderr": 0.03173071239071724
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.29015544041450775,
"acc_stderr": 0.032752644677915166,
"acc_norm": 0.29015544041450775,
"acc_norm_stderr": 0.032752644677915166
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2846153846153846,
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.21,
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},
"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
"mc1": 0.2484700122399021,
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"mc2": 0.41399712836660274,
"mc2_stderr": 0.01494063292915903
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ywan111/macbook-dataset-b2 | ---
license: apache-2.0
---
|
financeart/HON_4 | ---
license: mit
---
|
Rashedul12/embeddings | ---
license: mit
---
### Setting up a dataset by defining an embedding
- Contains text and vector embeddings |
Marcela341/Noob | ---
license: openrail
---
|
mask-distilled-one-sec-cv12/chunk_270 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 815249568
num_examples: 160104
download_size: 828699420
dataset_size: 815249568
---
# Dataset Card for "chunk_270"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Hack90/ncbi_genbank_part_0 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: sequence
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: features
dtype: int64
- name: seq_length
dtype: int64
splits:
- name: train
num_bytes: 257341428
num_examples: 156
download_size: 118952731
dataset_size: 257341428
---
# Dataset Card for "ncbi_genbank_part_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nvm472001/cvdataset-layoutlmv3 | ---
license: mit
---
|
sainv/multilingual_prompt | ---
license: mit
---
|
ownwaifu/test | ---
license: openrail
---
|
LennardZuendorf/interpretor | ---
license: mit
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: label
dtype: int64
- name: label_text
dtype: string
splits:
- name: train
num_bytes: 12150228.415975923
num_examples: 74159
- name: test
num_bytes: 1350043.584024078
num_examples: 8240
download_size: 8392302
dataset_size: 13500272
language:
- en
size_categories:
- 10K<n<100K
tags:
- not-for-all-audiences
- legal
---
# Dataset Card for Dataset Name
This is an edit of original work from Bertie Vidgen, Tristan Thrush, Zeerak Waseem and Douwe Kiela. Which I have uploaded to Huggingface [here](https://huggingface.co/datasets/LennardZuendorf/Dynamically-Generated-Hate-Speech-Dataset/edit/main/README.md). It is not my original work, I just edited it.
Data is used in the similarly named Interpretor Model.
## Dataset Description
- **Homepage:** [zuendorf.me](https://www.zuendorf.me)
- **Repository:** [GitHub Monorepo](https://github.com/LennardZuendorf/interpretor)
- **Author:** Lennard Zündorf
### Original Dataset Description
- **Original Source Contact:** [bertievidgen@gmail.com](mailto:bertievidgen@gmail.com)
- **Original Source:** [Dynamically-Generated-Hate-Speech-Dataset](https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset)
- **Original Author List:** Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook AI Research), Zeerak Waseem (University of Sheffield) and Douwe Kiela (Facebook AI Research).
**Refer to the Huggingface or GitHub Repo for more information**
### Dataset Summary
This Dataset contains dynamically generated hate-speech, processed to be used in classification tasks with i.E. BERT.
### Edit Summary
- I have edited the dataset to use it in training the similarly named [Interpretor Classifier]()
- see data/label fields below and the original dataset [here](https://huggingface.co/datasets/LennardZuendorf/Dynamically-Generated-Hate-Speech-Dataset/edit/main/README.md)
- Edits mostly include cleaning out information not needed for a simple binary classification tasks and adding a numerical binary label
## Dataset Structure
### Split
- The dataset is split into train and test, in a 90% to 10% split
- Train = ~ 74k entries
- Test = ~ 8k entries
### Data Fields
| id | text | label | label_text |
| - | - | - | - |
| numeric id | text of the comment | binary label, 0 = not hate, 1 = hate | label in text form
## Additional Information
### Licensing Information
- The original repository does not provide any license, but is free for use with proper citation of the original paper (see link above)
- This dataset can be used under the MIT license, with proper citation of both the original and this source.
- But I suggest taking data from the original source and doing your own editing.
### Citation Information
Please cite this repository and the original authors (see above) when using it.
### Contributions
I removed some data fields and did a new split with hugging face datasets. |
FelixdoingAI/IP2P-edit-try-step50-7.5_1.5-200 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: original_prompt
dtype: string
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_prompt
dtype: string
- name: edited_image
dtype: image
- name: adversarial_image
dtype: image
- name: edit_adv_image
dtype: image
splits:
- name: train
num_bytes: 86919610.0
num_examples: 200
download_size: 86923093
dataset_size: 86919610.0
---
# Dataset Card for "IP2P-edit-try-step50-7.5_1.5-200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/shibuya_rin_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of shibuya_rin/渋谷凛/시부야린 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of shibuya_rin/渋谷凛/시부야린 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `long_hair, brown_hair, green_eyes, breasts, earrings`, 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 | 562.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shibuya_rin_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 373.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shibuya_rin_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1166 | 741.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shibuya_rin_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 518.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shibuya_rin_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1166 | 954.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/shibuya_rin_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/shibuya_rin_idolmastercinderellagirls',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 13 |  |  |  |  |  | 1girl, solo, looking_at_viewer, hair_flower, black_gloves, choker, bare_shoulders, black_dress, blush, cleavage, medium_breasts |
| 1 | 8 |  |  |  |  |  | 1girl, dress, looking_at_viewer, smile, solo, tiara, medium_breasts, elbow_gloves, white_gloves, cleavage, necklace, blush |
| 2 | 10 |  |  |  |  |  | 1girl, blue_dress, solo, looking_at_viewer, bare_shoulders, hair_ornament, necklace, sleeveless_dress, black_gloves, bangs, simple_background, smile, white_background, blush, closed_mouth |
| 3 | 15 |  |  |  |  |  | 1girl, cardigan, necktie, school_uniform, solo, looking_at_viewer, skirt, necklace, bag |
| 4 | 6 |  |  |  |  |  | 1girl, cardigan, looking_at_viewer, necklace, necktie, school_uniform, simple_background, skirt, solo, white_background, hand_in_pocket, blush |
| 5 | 5 |  |  |  |  |  | 1girl, cardigan, kneehighs, necklace, necktie, school_uniform, skirt, solo, black_socks, blush, looking_at_viewer, sitting, simple_background, white_background, aqua_eyes |
| 6 | 17 |  |  |  |  |  | 1girl, school_uniform, solo, bangs, long_sleeves, looking_at_viewer, white_shirt, blush, pleated_skirt, black_cardigan, miniskirt, striped_necktie, simple_background, white_background, closed_mouth, collared_shirt, grey_skirt, hair_between_eyes, cowboy_shot, green_necktie, school_bag, smile, standing, jewelry |
| 7 | 7 |  |  |  |  |  | 1girl, ass, looking_at_viewer, school_uniform, solo, blush, cardigan, looking_back, white_panties, from_behind, pantyshot, pleated_skirt, bag, socks, thighs, upskirt |
| 8 | 10 |  |  |  |  |  | 1girl, cape, solo, looking_at_viewer, sword, navel, armor, midriff, black_thighhighs, hair_flower, garter_straps, black_hair, blue_gloves |
| 9 | 19 |  |  |  |  |  | 1girl, solo, medium_breasts, navel, looking_at_viewer, black_bikini, cleavage, smile, hair_flower, jewelry, short_shorts, front-tie_top, black_hair, open_fly |
| 10 | 8 |  |  |  |  |  | 1girl, bangs, hair_flower, solo, floral_print, looking_at_viewer, obi, wide_sleeves, blush, long_sleeves, outdoors, print_kimono, :d, holding, jewelry, open_mouth, sidelocks, single_hair_bun, upper_body, yukata |
| 11 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, medium_breasts, bangs, blush, solo, closed_mouth, outdoors, denim_shorts, hair_between_eyes, jewelry, ribbed_sweater, shirt, short_shorts, smile, ass, bag, coffee_cup, cowboy_shot, holding_cup, long_sleeves, midriff, pants, sleeveless, turtleneck_sweater |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | hair_flower | black_gloves | choker | bare_shoulders | black_dress | blush | cleavage | medium_breasts | dress | smile | tiara | elbow_gloves | white_gloves | necklace | blue_dress | hair_ornament | sleeveless_dress | bangs | simple_background | white_background | closed_mouth | cardigan | necktie | school_uniform | skirt | bag | hand_in_pocket | kneehighs | black_socks | sitting | aqua_eyes | long_sleeves | white_shirt | pleated_skirt | black_cardigan | miniskirt | striped_necktie | collared_shirt | grey_skirt | hair_between_eyes | cowboy_shot | green_necktie | school_bag | standing | jewelry | ass | looking_back | white_panties | from_behind | pantyshot | socks | thighs | upskirt | cape | sword | navel | armor | midriff | black_thighhighs | garter_straps | black_hair | blue_gloves | black_bikini | short_shorts | front-tie_top | open_fly | floral_print | obi | wide_sleeves | outdoors | print_kimono | :d | holding | open_mouth | sidelocks | single_hair_bun | upper_body | yukata | denim_shorts | ribbed_sweater | shirt | coffee_cup | holding_cup | pants | sleeveless | turtleneck_sweater |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------|:--------------------|:--------------|:---------------|:---------|:-----------------|:--------------|:--------|:-----------|:-----------------|:--------|:--------|:--------|:---------------|:---------------|:-----------|:-------------|:----------------|:-------------------|:--------|:--------------------|:-------------------|:---------------|:-----------|:----------|:-----------------|:--------|:------|:-----------------|:------------|:--------------|:----------|:------------|:---------------|:--------------|:----------------|:-----------------|:------------|:------------------|:-----------------|:-------------|:--------------------|:--------------|:----------------|:-------------|:-----------|:----------|:------|:---------------|:----------------|:--------------|:------------|:--------|:---------|:----------|:-------|:--------|:--------|:--------|:----------|:-------------------|:----------------|:-------------|:--------------|:---------------|:---------------|:----------------|:-----------|:---------------|:------|:---------------|:-----------|:---------------|:-----|:----------|:-------------|:------------|:------------------|:-------------|:---------|:---------------|:-----------------|:--------|:-------------|:--------------|:--------|:-------------|:---------------------|
| 0 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | | X | | X | | X | | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 15 |  |  |  |  |  | X | X | X | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | X | | | | | | X | | | | | | | | X | | | | | X | X | | X | X | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | X | | | | | | X | | | | | | | | X | | | | | X | X | | X | X | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 17 |  |  |  |  |  | X | X | X | | | | | | X | | | | X | | | | | | | | X | X | X | X | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | | | | | | X | | | | | | | | | | | | | | | | X | | X | | X | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 10 |  |  |  |  |  | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 19 |  |  |  |  |  | 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 | | | | | | | | |
| 11 | 7 |  |  |  |  |  | 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 |
|
Atipico1/NQ-10k_preprocessed_with_o-u_case | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: original_case
list:
- name: answer
dtype: string
- name: context
dtype: string
- name: distance
dtype: string
- name: question
dtype: string
- name: unans_case
list:
- name: answer
dtype: string
- name: context
dtype: string
- name: distance
dtype: string
- name: question
dtype: string
splits:
- name: train
num_bytes: 93316343
num_examples: 10000
- name: test
num_bytes: 33979723
num_examples: 3610
download_size: 72472873
dataset_size: 127296066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
whatisslove11/80_ms_eval | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype:
class_label:
names:
'0': normal_speech
'1': whisper
'2': music
'3': scream
splits:
- name: train
num_bytes: 179537759.144
num_examples: 12672
download_size: 169378508
dataset_size: 179537759.144
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
MariaIsabel/NFR_Spanish_requirements_classification | ---
annotations_creators:
- other
language:
- es
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ReSpaN - Spanish requirements labeled in non-functional categories and subcategories (ISO/IEC 25010 quality model).
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
ReSpaN(Spanish Dataset for non-functional requirements classification): Published version of dataset used for paper 'Towards a FAIR Dataset for non-functional requirements'.This dataset was created following the FAIR principles.
### Languages
Spanish
## Dataset Structure
### Data Fields
In the dataset_structure file.
## Dataset Creation
### Initial Data Collection and Normalization
This dataset was created from a collection of non-functional requirements extracted from 19 final degree carried out from the University of A Coruna. It consist in 109 non-funtcional requirements. Manual labeling was performed by 7 annotators in such a way that each requirement had at least 3 labels. The labels were the categories and subcategories of the ISO/IEC 25010 quality model. The label ’No agreement’ was used for requirements with no majority in the labeling process. The final classification of each requirement is based on unanimity or majority.
## Additional Information
### Citation Information
https://doi.org/10.1145/3555776.3578611 |
Clip11/clip11 | ---
license: apache-2.0
---
|
bigscience-data/roots_indic-hi_ted_talks_iwslt | ---
language: hi
license: cc-by-nc-nd-4.0
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
---
ROOTS Subset: roots_indic-hi_ted_talks_iwslt
# WIT Ted Talks
- Dataset uid: `ted_talks_iwslt`
### Description
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
### Homepage
https://github.com/huggingface/datasets/blob/master/datasets/ted_talks_iwslt/README.md
### Licensing
- open license
- cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
TED makes its collection of video recordings and transcripts of talks available under the Creative Commons BY-NC-ND license (look here). WIT3 acknowledges the authorship of TED talks (BY condition) and does not redistribute transcripts for commercial purposes (NC). As regards the integrity of the work (ND), WIT3 only changes the format of the container, while preserving the original contents. WIT3 aims to support research on human language processing as well as the diffusion of TED Talks!
### Speaker Locations
- Southern Europe
- Italy
### Sizes
- 0.0305 % of total
- 0.0736 % of ar
- 0.2002 % of pt
- 0.0128 % of zh
- 0.2236 % of vi
- 0.0330 % of fr
- 0.0545 % of es
- 0.0122 % of en
- 0.3704 % of id
- 0.0373 % of indic-hi
- 0.0330 % of indic-ta
- 0.1393 % of indic-mr
- 0.0305 % of ca
- 0.1179 % of indic-ur
- 0.0147 % of indic-bn
- 0.0240 % of indic-ml
- 0.0244 % of indic-te
- 0.0503 % of indic-gu
- 0.0211 % of indic-kn
- 0.0274 % of eu
- 0.0023 % of indic-as
- 0.0001 % of indic-pa
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-mr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: ca
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: indic-ur
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-bn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-as
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-pa
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
|
romanjanik/PONER | ---
license: apache-2.0
task_categories:
- token-classification
language:
- cs
tags:
- historical Czech
- Named Entity Recognition
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: "data/hugging_face/train/data-00000-of-00001.arrow"
- split: test
path: "data/hugging_face/test/data-00000-of-00001.arrow"
- split: dev
path: "data/hugging_face/validation/data-00000-of-00001.arrow"
---
# Dataset Card for PERO OCR NER 1.0
This is a dataset created for master thesis "Document Information Extraction".
Author: Roman Janík, 2023
Faculty of Information Technology, Brno University of Technology
## Dataset Description
- **Repository:** [PONER repository](https://github.com/roman-janik/PONER)
- **Paper:** [Document Information Extraction](https://dspace.vutbr.cz/handle/11012/213801?locale-attribute=en)
### Dataset Summary
This is a **P**ERO **O**CR **NER** 1.0 dataset for Named Entity Recognition. The dataset consists of 9,310 Czech sentences with 14,639 named entities.
Source data are Czech historical chronicles mostly from the first half of the 20th century. The chronicles scanned images were processed by PERO OCR [1].
Text data were then annotated in the Label Studio tool. The process was semi-automated, first a NER model was used to pre-annotate the data and then
the pre-annotations were manually refined. Named entity types are: *Personal names*, *Institutions*, *Geographical names*, *Time expressions*, and *Artifact names/Objects*; the same as in Czech Historical Named Entity Corpus (CHNEC)[2].
### Supported Tasks and Leaderboards
- Named Entity Recognition
### Languages
The text in the dataset is in Czech, specifically historical Czech from the first half of the 20th century.
## Dataset Structure
The CoNLL files are formatted as follows:
Each line in
the corpus contains information about one word/token. The first column is the actual
word, and the second column is a Named Entity class in a BIO format. An empty line is a sentence separator.
For detailed documentation, please see [doc/documentation.pdf](https://huggingface.co/datasets/romanjanik/PONER/blob/main/doc/documentation.pdf). In case of any question, please use GitHub Issues.
### Data Instances
A data point consists of one sentence of text with corresponding NER annotation. An example from PONER Huggings Face dataset looks as follows:
```
{’id’: ’4138’,
’tokens’: [’Přednášel’, ’Frant’, ’.’, ’Pruský’, ’z’, ’Olomouce’, ’.’],
’ner_tags’: [0, 1, 2, 2, 0, 5, 0]}
```
### Data Fields
- `id`: data point id
- `tokens`: list of sentence words
- `ner_tags`: list of entity types
## Results
This dataset was used for training several NER models.
### RobeCzech
RobeCzech [3], a Czech version of RoBERTa [4] model was finetuned using PONER, CHNEC [2], and Czech Named Entity Corpus (CNEC)[5]. All datasets train and test splits were concatenated and used together during training and the model was then evaluated separately on each dataset.
| Model | CNEC 2.0 test | CHNEC 1.0 test | PONER 1.0 test |
| --------- | --------- | --------- | --------- |
| RobeCzech | 0.886 | 0.876 | **0.871** |
### Czech RoBERTa models
Smaller versions of RoBERTa [4] model were trained on an own text dataset and then finetuned using PONER, CHNEC [2] and Czech Named Entity Corpus (CNEC)[5]. All datasets train and test splits were concatenated and used together during training and the model was then evaluated separately on each dataset. Two configurations were used: CNEC + CHNEC + PONER and PONER.
| Model | Configuration | CNEC 2.0 test | CHNEC 1.0 test | PONER 1.0 test |
| --------- | --------- | --------- | --------- | --------- |
| Czech RoBERTa 8L_512H| CNEC + CHNEC + PONER | 0.800 | 0.867 | **0.841** |
| Czech RoBERTa 8L_512H | PONER | - | - | **0.832** |
## Data
Data are organized as follows: `data/conll` contains dataset CoNLL files, with whole data in `poner.conll` and splits used
for training in the original thesis. These splits are 0.45/0.50/0.05 for train/test/dev. You can create your own splits with `scripts/split_poner_dataset_conll.py`. `data/hugging_face` contains original splits in the Hugging Face format. `data/label_studio_annotations`
contains the final Label Studio JSON export file. `data/source_data` contains original text and image files of annotated pages.
#### Examples
CoNLL:
```
Od O
9. B-t
listopadu I-t
1895 I-t
zastupoval O
starostu O
Fr B-p
. I-p
Štěpka I-p
zemřel O
2. B-t
února I-t
1896 I-t
) O
pan O
Jindřich B-p
Matzenauer I-p
. O
```
Label Studio page:
## Scripts
Directory `scripts` contain Python scripts used for the creation of the dataset. There are two scripts for
editing Label Studio JSON annotation file, one for creating CoNLL version out of an annotation file and text files,
one for creating splits and one for loading CoNNL files and transforming them to the Hugging Face dataset format. Scripts are written in Python 10.0.
To be able to run all scripts, in the scripts directory run the:
```shellscript
pip install -r requirements.txt
```
## License
PONER is licensed under the Apache License Version 2.0.
## Citation
If you use PONER in your work, please cite the
[Document Information Extraction](https://dspace.vutbr.cz/handle/11012/213801?locale-attribute=en).
```
@mastersthesis{janik-2023-document-information-extraction,
title = "Document Information Extraction",
author = "Janík, Roman",
language = "eng",
year = "2023",
school = "Brno University of Technology, Faculty of Information Technology",
url = "https://dspace.vutbr.cz/handle/11012/213801?locale-attribute=en",
type = "Master’s thesis",
note = "Supervisor Ing. Michal Hradiš, Ph.D."
}
```
## References
[1] - **O Kodym, M Hradiš**: *Page Layout Analysis System for Unconstrained Historic Documents.* ICDAR, 2021, [PERO OCR](https://pero-ocr.fit.vutbr.cz/).
[2] - **Hubková, H., Kral, P. and Pettersson, E.** Czech Historical Named Entity
Corpus v 1.0. In: *Proceedings of the 12th Language Resources and Evaluation Conference.* Marseille, France: European Language Resources Association, May 2020, p. 4458–4465. ISBN 979-10-95546-34-4. Available at:
https://aclanthology.org/2020.lrec-1.549.
[3] - **Straka, M., Náplava, J., Straková, J. and Samuel, D.** RobeCzech: Czech
RoBERTa, a Monolingual Contextualized Language Representation Model. In: *24th
International Conference on Text, Speech and Dialogue.* Cham, Switzerland:
Springer, 2021, p. 197–209. ISBN 978-3-030-83526-2.
[4] - **Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M. et al.** RoBERTa: A Robustly
Optimized BERT Pretraining Approach. 2019. Available at:
http://arxiv.org/abs/1907.11692.
[5] - **Ševčíková, M., Žabokrtský, Z., Straková, J. and Straka, M.** Czech Named
Entity Corpus 2.0. 2014. LINDAT/CLARIAH-CZ digital library at the Institute of Formal
and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University.
Available at: http://hdl.handle.net/11858/00-097C-0000-0023-1B22-8. |
AndyReas/frontpage-news | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 10M<n<100M
---
# Frontpage News
## The Data
The data consists of ~13,000,000 English articles from ~90 outlets. The articles were collected from the [Sciride News Mine](http://sciride.org/news.html), after which some additional cleaning / processing was performed on the data. The articles span from 2015-07-18 to 2020-10-17.
### Data processing
- Removing duplicate articles (a result of being on the frontpage for multiple days.)
- Removing repeated "outlet tags" appearing before or after headlines such as "| Daily Mail Online".
- Removing dates that were not part of a natural sentence but rather "tags", such as "\[Some headline\] - 2020-12-03".
- Removing duplicate articles (again. This time due to dates making otherwise identical articles unique. Removing the date made them 100% identical.)
- Removing HTML elements that were missed on the first scraping.
- Unescaping HTML characters, replacing them with "regular" characters.
- Removing "junk" articles such as empty articles and articles with a length below a certain threshold.
Note: the cleaning process was not perfect and some "outlet tags" still remain.
For instance, some outlets use "--" instead of "|" before a tag, and those were missed.
There is also the case of uncommon characters, such as "\u00a" being used instead of regular characters. This specific example results in tokenizers not being able to properly tokenize sentences using that space.
There are possibly (likely) other things, that were overlooked during cleaning.
### Outlets
```
["9news.com.au", "abc.net.au", "abcnews.go.com", "afr.com", "aljazeera.com", "apnews.com", "bbc.com", "bostonglobe.com", "breakingnews.ie", "breitbart.com", "businessinsider.com", "cbc.ca", "cbsnews.com", "channel4.com", "chicagotribune.com", "cnbc.com", "csmonitor.com", "ctvnews.ca", "dailymail.co.uk", "dailystar.co.uk", "dw.com", "economist.com", "edition.cnn.com", "euronews.com", "express.co.uk", "foxnews.com", "france24.com", "globalnews.ca", "huffpost.com", "independent.co.uk", "independent.ie", "inquirer.com", "irishexaminer.com", "irishmirror.ie", "irishtimes.com", "itv.com", "latimes.com", "liverpoolecho.co.uk", "macleans.ca", "metro.co.uk", "mirror.co.uk", "montrealgazette.com", "morningstaronline.co.uk", "msnbc.com", "nbcnews.com", "news.com.au", "news.sky.com", "news.yahoo.com", "newshub.co.nz", "newsweek.com", "npr.org", "nypost.com", "nytimes.com", "nzherald.co.nz", "politico.com", "rcinet.ca", "reuters.com", "rfi.fr", "rnz.co.nz", "rt.com", "rte.ie", "sbs.com.au", "scoop.co.nz", "scotsman.com", "slate.com", "smh.com.au", "standard.co.uk", "stuff.co.nz", "telegraph.co.uk", "theage.com.au", "theatlantic.com", "theglobeandmail.com", "theguardian.com", "thehill.com", "thejournal.ie", "thestar.com", "thesun.co.uk", "thesun.ie", "thetimes.co.uk", "thewest.com.au", "time.com", "torontosun.com", "upi.com", "usatoday.com", "vancouversun.com", "walesonline.co.uk", "washingtonpost.com", "washingtontimes.com", "westernjournal.com", "wnd.com", "wsj.com"]
```
## Features (columns)
### title
A news headline.
### description
A news subheader.
### meta
- article_id: Article ID from the original sciride news mine. A hashing of the original title + description.
- date: The date on which the article appeared on the frontpage.
- outlet: The outlet which published the article on their frontpage.
### new_article_id
A new article ID created by hashing the title + description. Can be different from article_id because titles and descriptions changed during "cleaning". |
mmosiolek/pl_alpaca_data_cleaned | ---
license: cc-by-4.0
language:
- pl
tags:
- llama
- alpaca
- chat-gpt
- self-instruct
- gpt
---
# Polpaca: The Polish Alpaca
Please find the model here: https://huggingface.co/mmosiolek/polpaca-lora-7b
This repository contains the polish translations of the datasets for constructing and evaluating instruction following models: Alpaca.
### Training
The following dataset was translated: https://github.com/gururise/AlpacaDataCleaned
It might be also found here: https://huggingface.co/datasets/yahma/alpaca-cleaned
For the translation process, I relied on GPT-3.5-Turbo and the free $18 credits granted by the OpenAI platform.
Unfortunately, the cost of the translation exceeded the amount granted, so I had to add $7 from my own pocket ;)
Although the translation was extremely cheap, it took 5 days to complete.
The following prompt was used for the translation based on: https://arxiv.org/abs/2301.08745
```
Please provide the Polish translation for these sentences: [TEXT]
```
### Manual Quality Assessment
For evaluation the self-instruct (https://github.com/yizhongw/self-instruct) evaluation dataset was translated.
This time with the help of DeepL that offers translation of 500K characters for free each month.
Unfortunately this approach has certain limitations related to the fact, that some tasks from the original datasets can't be simply
translated to another language. For example we can't propagate ortographic errors from one language to another.
It's necessary to keep it mind while manually reviewing the results. |
mcemilg/x-fact | ---
configs:
- config_name: pl
data_files:
- path: pl/test.csv
split: test
- path: pl/dev.csv
split: validation
- path: pl/train.csv
split: train
- config_name: sq
data_files:
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split: zeroshot
- config_name: mr
data_files:
- path: mr/zeroshot.csv
split: zeroshot
- config_name: 'no'
data_files:
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split: zeroshot
- config_name: gu
data_files:
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split: zeroshot
- config_name: it
data_files:
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split: ood
- path: it/test.csv
split: test
- path: it/dev.csv
split: validation
- path: it/train.csv
split: train
- config_name: ru
data_files:
- path: ru/zeroshot.csv
split: zeroshot
- config_name: ro
data_files:
- path: ro/test.csv
split: test
- path: ro/dev.csv
split: validation
- path: ro/train.csv
split: train
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data_files:
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split: ood
- path: pt/test.csv
split: test
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- config_name: sr
data_files:
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split: train
- config_name: pa
data_files:
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split: zeroshot
- config_name: si
data_files:
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split: zeroshot
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data_files:
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split: test
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split: validation
- path: ar/train.csv
split: train
- config_name: nl
data_files:
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split: zeroshot
- config_name: bn
data_files:
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split: zeroshot
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data_files:
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split: ood
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split: test
- path: hi/dev.csv
split: validation
- path: hi/train.csv
split: train
- config_name: ka
data_files:
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- path: ka/dev.csv
split: validation
- path: ka/train.csv
split: train
- config_name: de
data_files:
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split: test
- path: de/dev.csv
split: validation
- path: de/train.csv
split: train
- config_name: az
data_files:
- path: az/zeroshot.csv
split: zeroshot
- config_name: id
data_files:
- path: id/ood.csv
split: ood
- path: id/test.csv
split: test
- path: id/dev.csv
split: validation
- path: id/train.csv
split: train
- config_name: fr
data_files:
- path: fr/zeroshot.csv
split: zeroshot
- config_name: es
data_files:
- path: es/test.csv
split: test
- path: es/dev.csv
split: validation
- path: es/train.csv
split: train
- config_name: en
data_files:
- path: en/train.csv
split: train
- config_name: fa
data_files:
- path: fa/zeroshot.csv
split: zeroshot
- config_name: ta
data_files:
- path: ta/test.csv
split: test
- path: ta/dev.csv
split: validation
- path: ta/train.csv
split: train
- config_name: tr
data_files:
- path: tr/ood.csv
split: ood
- path: tr/test.csv
split: test
- path: tr/dev.csv
split: validation
- path: tr/train.csv
split: train
---
Homepage: https://github.com/utahnlp/x-fact |
Nexdata/Passenger_Behavior_Recognition_Data | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Passenger_Behavior_Recognition_Data
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/1083?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
122 People - Passenger Behavior Recognition Data. The data includes multiple age groups, multiple time periods and multiple races (Caucasian, Black, Indian). The passenger behaviors include passenger normal behavior, passenger abnormal behavior(passenger carsick behavior, passenger sleepy behavior, passenger lost items behavior). In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as passenger behavior analysis.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1083?source=Huggingface
### Supported Tasks and Leaderboards
face-detection, computer-vision, object-detection: The dataset can be used to train a model for face detection.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
alexshengzhili/mPLUG-owl | ---
dataset_info:
features:
- name: image_file
dtype: string
- name: id
dtype: string
- name: caption
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: first_mention
dtype: string
- name: response
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: q_a_pairs
sequence:
sequence: string
- name: response_mPLUG-owl
dtype: string
splits:
- name: 1_percent_as_validation
num_bytes: 19209561
num_examples: 3002
download_size: 8946500
dataset_size: 19209561
---
# Dataset Card for "mPLUG-owl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Shijiang/Handwritten-Latex-Datasets | ---
license: apache-2.0
task_categories:
- image-to-text
tags:
- code
size_categories:
- 1K<n<10K
---
# Dataset
This data set includes common handwritten formulas in junior high schools and high schools, and is labeled in Latex format. Can be used to train models that recognize common numbers, fractions, and sets.
# Dataset source
Collected in various junior high schools and high schools, handwritten by students.
# Usage
The label is stored at json folder and scanned hand-writted pictures are stored at pic folder.
Scan the qr code of the picture to get the index and find the correct label. |
MAWright327/dataset_demo | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: ad
dtype: string
splits:
- name: train
num_bytes: 27531
num_examples: 90
- name: test
num_bytes: 3037
num_examples: 10
download_size: 24912
dataset_size: 30568
---
# Dataset Card for "dataset_demo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
priyank-m/trdg_random_single_words_en_text_recognition | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
splits:
- name: train
num_bytes: 2595486075.0
num_examples: 155000
download_size: 2596520034
dataset_size: 2595486075.0
---
# Dataset Card for "trdg_random_single_words_en_text_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
minhapadaria/gustavosabongi | ---
license: openrail
---
|
mirajrambhiya/test | ---
license: bigcode-openrail-m
---
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b21b1b7e | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1340
dataset_size: 180
---
# Dataset Card for "b21b1b7e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-banking77-default-080492-51746145316 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: Laurie/bert-base-banking77-pt2
metrics: []
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Laurie/bert-base-banking77-pt2
* Dataset: banking77
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@edcody726ai@gmail.com](https://huggingface.co/edcody726ai@gmail.com) for evaluating this model. |
EgilKarlsen/PKDD_GPTNEO_Baseline | ---
configs:
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data_files:
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: string
splits:
- name: train
num_bytes: 307608907.5
num_examples: 37500
- name: test
num_bytes: 102536305.0
num_examples: 12500
download_size: 565384532
dataset_size: 410145212.5
---
# Dataset Card for "PKDD_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
breno30/LuizAugusto | ---
license: openrail
---
|
leongl/1c_github | ---
license: unknown
language:
- ru
task_categories:
- text-generation
size_categories:
- 1M<n<10M
--- |
MongoDB/cosmopedia-wikihow-chunked | ---
license: apache-2.0
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- vector search
- semantic search
- retrieval augmented generation
size_categories:
- 1M<n<10M
---
## Overview
This dataset is a chunked version of a subset of data in the [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) dataset curated by Hugging Face.
Specifically, we have only used a subset of Wikihow articles from the Cosmopedia dataset, and each article has been split into chunks containing no more than 2 paragraphs.
## Dataset Structure
Each record in the dataset represents a chunk of a larger article, and contains the following fields:
- `doc_id`: A unique identifier for the parent article
- `chunk_id`: A unique identifier for each chunk
- `text_token_length`: Number of tokens in the chunk text
- `text`: The raw text of the chunk
## Usage
This dataset can be useful for evaluating and testing:
- Performance of embedding models and RAG
- Retrieval quality of Semantic Search
- Question-Answering performance
## Ingest Data
To experiment with this dataset using MongoDB Atlas, first [create a MongoDB Atlas account](https://www.mongodb.com/cloud/atlas/register?utm_campaign=devrel&utm_source=community&utm_medium=organic_social&utm_content=Hugging%20Face%20Dataset&utm_term=apoorva.joshi).
You can then use the following script to load this dataset into your MongoDB Atlas cluster:
```
import os
from pymongo import MongoClient
import datasets
from datasets import load_dataset
from bson import json_util
# MongoDB Atlas URI and client setup
uri = os.environ.get('MONGODB_ATLAS_URI')
client = MongoClient(uri)
# Change to the appropriate database and collection names
db_name = 'your_database_name' # Change this to your actual database name
collection_name = 'cosmopedia-wikihow-chunked' # Change this to your actual collection name
collection = client[db_name][collection_name]
# Load the "cosmopedia-wikihow-chunked" dataset from Hugging Face
dataset = load_dataset("AIatMongoDB/cosmopedia-wikihow-chunked")
insert_data = []
# Iterate through the dataset and prepare the documents for insertion
# The script below ingests 1000 records into the database at a time
for item in dataset['train']:
# Convert the dataset item to MongoDB document format
doc_item = json_util.loads(json_util.dumps(item))
insert_data.append(doc_item)
# Insert in batches of 1000 documents
if len(insert_data) == 1000:
collection.insert_many(insert_data)
print("1000 records ingested")
insert_data = []
# Insert any remaining documents
if len(insert_data) > 0:
collection.insert_many(insert_data)
print("Data Ingested")
```
## Sample Document
Documents in MongoDB should look as follows:
```
{
"_id": {
"$oid": "65d93cb0653af71f15a888ae"
},
"doc_id": {
"$numberInt": "0"
},
"chunk_id": {
"$numberInt": "1"
},
"text_token_length": {
"$numberInt": "111"
},
"text": "**Step 1: Choose a Location **\nSelect a well-draining spot in your backyard, away from your house or other structures, as compost piles can produce odors. Ideally, locate the pile in partial shade or a location with morning sun only. This allows the pile to retain moisture while avoiding overheating during peak sunlight hours.\n\n_Key tip:_ Aim for a minimum area of 3 x 3 feet (0.9m x 0.9m) for proper decomposition; smaller piles may not generate enough heat for optimal breakdown of materials."
}
``` |
yzhuang/autotree_automl_pol_sgosdt_l256_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 369520000
num_examples: 10000
- name: validation
num_bytes: 369520000
num_examples: 10000
download_size: 84319622
dataset_size: 739040000
---
# Dataset Card for "autotree_automl_pol_sgosdt_l256_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arieg/bw_spec_cls_80_09 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '24216'
'1': '24217'
'2': '24218'
'3': '24362'
'4': '24363'
'5': '24364'
'6': '24365'
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'58': '25104'
'59': '25124'
'60': '25215'
'61': '25216'
'62': '25227'
'63': '25232'
'64': '25233'
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'68': '25378'
'69': '25601'
'70': '25603'
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'73': '25608'
'74': '25609'
'75': '25795'
'76': '25796'
'77': '25797'
'78': '25802'
'79': '25804'
splits:
- name: train
num_bytes: 87063169.6
num_examples: 1600
download_size: 86900268
dataset_size: 87063169.6
---
# Dataset Card for "bw_spec_cls_80_09"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RikeshSilwal/slr54 | ---
license: apache-2.0
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 9345272037.625
num_examples: 157905
download_size: 8034037643
dataset_size: 9345272037.625
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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