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result-kand2-sdxl-wuerst-karlo/0eb4c62d | 2023-10-01T19:54:21.000Z | [
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jigsaw_toxicity_pred | 2023-01-25T14:33:17.000Z | [
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"region:us"
] | null | This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. | null | 16 | 352 | 2022-03-02T23:29:22 | ---
annotations_creators:
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task_categories:
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task_ids:
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pretty_name: JigsawToxicityPred
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feat... | 6,369 | [
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newsqa | 2023-06-01T14:59:49.000Z | [
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] | null | NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. | @inproceedings{trischler2017newsqa,
title={NewsQA: A Machine Comprehension Dataset},
author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},
booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},
pages=... | 8 | 352 | 2022-03-02T23:29:22 | ---
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ai-forever/spellcheck_benchmark | 2023-10-04T16:13:44.000Z | [
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It includes four datasets, each of which consists of pairs of sentences in Russian language.
Each pair embodies sentence, which may contain spelling errors, and its corresponding correction.
... | # TODO: add citation | 2 | 352 | 2023-04-28T09:49:40 | ---
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antolin/python-150_interduplication | 2023-09-18T08:35:19.000Z | [
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] | antolin | null | null | 1 | 352 | 2023-09-05T11:21:06 | ---
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multi_x_science_sum | 2022-11-18T21:31:34.000Z | [
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] | null | Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | @article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
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Nicolas-BZRD/Original_Songs_Lyrics_with_French_Translation | 2023-10-16T14:02:02.000Z | [
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"language:... | Nicolas-BZRD | null | null | 5 | 351 | 2023-09-12T21:21:44 | ---
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result-kand2-sdxl-wuerst-karlo/78fe0016 | 2023-10-03T01:21:52.000Z | [
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conll2002 | 2023-06-01T14:59:51.000Z | [
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Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-indep... | @inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
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... | 3 | 350 | 2022-03-02T23:29:22 | ---
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voidful/NMSQA | 2023-04-04T04:46:23.000Z | [
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"language_creators:crowdsourced",... | voidful | null | null | 7 | 350 | 2022-03-16T16:03:42 | ---
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task... | 8,140 | [
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x_stance | 2023-04-05T13:45:10.000Z | [
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"stance-dete... | null | The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions.
It can be used to train and evaluate stance detection systems. | @inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \\& 16th Conference on Natural Language Processing (KONVENS)"... | 4 | 349 | 2022-03-02T23:29:22 | ---
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nbertagnolli/counsel-chat | 2023-06-17T17:55:38.000Z | [
"region:us"
] | nbertagnolli | null | null | 9 | 348 | 2023-02-07T03:51:53 | # Dataset Card for [Dataset Name]
## 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-str... | 4,890 | [
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0.047393798828125,
0.0271453857421875,
-0.05584716796875,
-0.0694580078125,
-0.041778564453125,
-0.... |
teven/enwiki_100k | 2023-04-03T17:16:55.000Z | [
"region:us"
] | teven | null | null | 1 | 348 | 2023-04-03T17:13:51 | ---
dataset_info:
features:
- name: metadata
dtype: string
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 2570893740
num_examples: 1000000
download_size: 1550572660
dataset_size: 2570893740
---
# Dataset Card for "enwiki_100k"
[More Information ... | 433 | [
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GATE-engine/medical_decathlon | 2023-06-28T00:08:47.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 348 | 2023-06-27T04:48:55 | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence:
sequence: float32
- name: label
sequence:
sequence:
sequence:
sequence: float32
- name: image_meta_dict
struct:
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sequence: float64
- n... | 5,144 | [
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nielsr/cord-layoutlmv3 | 2022-05-02T16:41:30.000Z | [
"region:us"
] | nielsr | https://github.com/clovaai/cord/ | @article{park2019cord,
title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
} | 2 | 347 | 2022-05-02T16:37:54 | Entry not found | 15 | [
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0.0379028... |
hakurei/open-instruct-v1 | 2023-04-17T03:03:13.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | hakurei | null | null | 87 | 346 | 2023-04-04T23:10:41 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
# Open Instruct V1 - A dataset for having LLMs follow instructions.
Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
## Dataset ... | 1,181 | [
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cedr | 2023-01-25T14:27:50.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:apache-2... | null | This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in
Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion
categories. The data collected from different sources: posts of the Li... | @article{sboev2021data,
title={Data-Driven Model for Emotion Detection in Russian Texts},
author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
journal={Procedia Computer Science},
volume={190},
pages={637--642},
year={2021},
publisher={Elsevier}
} | 4 | 345 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pretty_name: The Corpus... | 8,739 | [
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0.00466156005859375,
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-0.05621337890625,
0.019241... |
multilingual_librispeech | 2022-11-18T21:31:47.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"so... | null | Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | 7 | 345 | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- es
- fr
- it
- nl
- pl
- pt
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- origi... | 11,627 | [
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0.03662109375,
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0.0162... |
squad_v1_pt | 2023-04-05T13:40:41.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:mit",
"arxiv:1606.052... | null | Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250}... | 6 | 345 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: SquadV1P... | 6,868 | [
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0.015... |
result-kand2-sdxl-wuerst-karlo/040dec0a | 2023-10-03T08:51:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 345 | 2023-10-03T08:51:40 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 160
num_examples: 10
download_size: 1292
dataset_size: 160
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "040dec0... | 455 | [
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0.0... |
result-kand2-sdxl-wuerst-karlo/f4d8fc49 | 2023-10-03T08:54:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 345 | 2023-10-03T08:54:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 159
num_examples: 10
download_size: 1306
dataset_size: 159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4d8fc4... | 455 | [
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... |
anon8231489123/ShareGPT_Vicuna_unfiltered | 2023-04-12T05:23:59.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | anon8231489123 | null | null | 587 | 344 | 2023-04-02T05:30:31 | ---
license: apache-2.0
language:
- en
---
**Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes insta... | 4,413 | [
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0.051666259765625,
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-0.0506591796875,
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0.0... |
shibing624/medical | 2023-06-02T07:03:41.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] | shibing624 | 纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。 | null | 151 | 344 | 2023-05-22T14:45:06 | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibi... | 6,202 | [
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... |
gtfintechlab/fomc_communication | 2023-09-12T21:18:49.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"finance",
"region:us"
] | gtfintechlab | null | null | 1 | 344 | 2023-09-12T21:00:59 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
---
## Citation and Contact Information
### Cite
Please cite our paper if you use any code, data, or models.
```c
@inproceedings{shah-etal-2023-trillion,
title = "Trillion Dollar Words: ... | 1,924 | [
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0.032806396484375,
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... |
result-kand2-sdxl-wuerst-karlo/6a3f723d | 2023-10-03T08:48:05.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 344 | 2023-10-03T08:48:04 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1317
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6a3f723... | 455 | [
[
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0.0151824951171875,
0.021087646484375,
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0.033111572265625,
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0.03863525390625,
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-0.00... |
bigbio/quaero | 2022-12-22T15:46:29.000Z | [
"multilinguality:monolingual",
"language:fr",
"license:other",
"region:us"
] | bigbio | The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].
A selection o... | @InProceedings{neveol14quaero,
author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy
and Rosset, Sophie and Zweigenbaum, Pierre},
title = {The {QUAERO} {French} Medical Corpus: A Ressource for
Medical Entity Recognition and Normalization},
OPTbooktitle = {Proceedings of the Fourt... | 1 | 343 | 2022-11-13T22:11:53 |
---
language:
- fr
bigbio_language:
- French
license: other
multilinguality: monolingual
bigbio_license_shortname: GFDL_1p3
pretty_name: QUAERO
homepage: https://quaerofrenchmed.limsi.fr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset C... | 4,968 | [
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0.041259765625,
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0.0... |
keremberke/pokemon-classification | 2023-01-15T18:41:29.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] | keremberke | null | @misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
... | 5 | 343 | 2023-01-15T18:40:15 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="keremberke/pokemon-classification" src="https://huggingface.co/datasets/keremberke/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Porygon'... | 3,749 | [
[
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-0.03155517578125,
-0.048492431640625,
-0.05328369140625,
... |
Lakera/gandalf_ignore_instructions | 2023-10-02T09:26:29.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"prompt injection",
"region:us"
] | Lakera | null | null | 4 | 343 | 2023-09-21T08:49:47 | ---
language:
- en
license: mit
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: text
dtype: string
- name: similarity
dtype: float64
splits:
- name: train
num_bytes: 66400
num_examples: 777
- name: validation
num_bytes: 9633
num_examples: 111
- name: test
num_bytes:... | 2,505 | [
[
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-0.0780029296875,
0.044891357421875,
0.0090789794921875,
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0.01296234130859375,
-0.01519775390625,
0.003204345703125,
0.042510986328125,
-0.04278564453125,
-0.04779052734375,
-0.049652099609375,
0.0... |
open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1 | 2023-10-04T07:22:11.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 343 | 2023-10-04T07:21:12 | ---
pretty_name: Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/Hugg... | 64,984 | [
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0.0164337158203125,
0.018280029296875,
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0.0013113021850585938,
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0.039764404296875,
-0.0022716522216796875,
-0.03448486328125,
-0.046905517578125,
-0.031372070312... |
open-llm-leaderboard/details_tiiuae__falcon-40b | 2023-09-08T21:43:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 342 | 2023-08-21T11:07:51 | ---
pretty_name: Evaluation run of tiiuae/falcon-40b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe data... | 67,932 | [
[
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-0.049713134765625,
0.01470184326171875,
0.0180511474609375,
-0.006710052490234375,
0.01448822021484375,
-0.0247802734375,
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0.03460693359375,
0.036895751953125,
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... |
bigbio/biored | 2023-01-12T05:54:49.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"arxiv:2204.04263",
"region:us"
] | bigbio | Relation Extraction corpus with multiple entity types (e.g., gene/protein,
disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
on a set of 600 PubMed articles | @article{DBLP:journals/corr/abs-2204-04263,
author = {Ling Luo and
Po{-}Ting Lai and
Chih{-}Hsuan Wei and
Cecilia N. Arighi and
Zhiyong Lu},
title = {BioRED: {A} Comprehensive Biomedical Relation Extraction Dataset},
journal = {CoRR},
volume ... | 1 | 341 | 2022-11-13T22:07:21 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BioRED
homepage: https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Datas... | 1,416 | [
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... |
Dahoas/svamp | 2023-10-16T11:27:40.000Z | [
"region:us"
] | Dahoas | null | null | 0 | 341 | 2023-10-16T11:24:12 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 347184
num_examples: 700
- name: test
num_bytes: 148692
num_examples: 300
download_size: ... | 513 | [
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0.033843994140625,
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-0.04296875,
-0.021209716... |
erwanlc/cocktails_recipe_no_brand | 2022-10-25T09:17:08.000Z | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language:en",
"license:other",
"region:us"
] | erwanlc | null | null | 1 | 340 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe_no_brand
language_bcp47:
- en
- en-US
---
# Dataset Card for ... | 1,708 | [
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-0.0333251953125,
-0.00133323669... |
kili-technology/plastic_in_river | 2022-10-21T07:13:58.000Z | [
"task_categories:object-detection",
"size_categories:1K<n<10K",
"source_datasets:original",
"other-object-detection",
"region:us"
] | kili-technology | This dataset contains photos of rivers on which there may be waste. The waste items are annotated
through bounding boxes, and are assigned to one of the 4 following categories: plastic bottle, plastic bag,
another plastic waste, or non-plastic waste. Note that some photos may not contain any waste. | null | 13 | 339 | 2022-03-02T23:29:22 | ---
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: Plastic in river
tags:
- other-object-detection
---
# Plastic in river
This dataset is an export of the annotated assets from the [Kili's Community Challenge - Plastic in River dataset](https://kili... | 496 | [
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0.07171630859375,
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0.005... |
kresnik/zeroth_korean | 2023-01-04T06:54:55.000Z | [
"region:us"
] | kresnik | This is Zeroth-Korean corpus,
licensed under Attribution 4.0 International (CC BY 4.0)
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 ut... | \ | 5 | 339 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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0.0379... |
baber/agieval | 2023-10-26T00:49:22.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2304.06364",
"region:us"
] | baber | null | @ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year=... | 2 | 339 | 2023-07-23T00:31:09 | ---
license: mit
language:
- en
task_categories:
- question-answering
- text-generation
pretty_name: AGIEval
---
# Dataset Card for AGIEval
## Dataset Description
- **Homepage:** https://github.com/microsoft/AGIEval/blob/main/README.md
- **Repository:** https://github.com/microsoft/AGIEval
- **Paper:** https://arxiv.... | 4,091 | [
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HumanCompatibleAI/ppo-CartPole-v1 | 2023-07-18T14:43:49.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 338 | 2023-07-18T14:43:44 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 2103613
num_examples: 100
download_size: 126383... | 519 | [
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-0.0... |
OleehyO/latex-formulas | 2023-08-15T17:24:50.000Z | [
"task_categories:image-to-text",
"license:openrail",
"region:us"
] | OleehyO | null | null | 11 | 338 | 2023-07-29T09:15:40 | ---
license: openrail
task_categories:
- image-to-text
---
# Dataset Description
> English version is [here](./README_English.md)
这里有两个数据集：*raw_formulas*和*tokenized_formulas*。
我们在*arxiv*上爬取了约100万条未经过清洗以及文本分词的latex公式的图片文本对从而得到了*raw_formulas*数据集。在将*raw_formulas*数据集进行**清洗**以及**文本分词**后得到了*tokenized_formulas*数据集。
渲染公式对应... | 1,795 | [
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open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1 | 2023-09-19T01:01:39.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 338 | 2023-09-19T01:00:38 | ---
pretty_name: Evaluation run of ICBU-NPU/FashionGPT-70B-V1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ICBU-NPU/FashionGPT-70B-V1.1](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_... | 64,897 | [
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Elriggs/openwebtext-100k | 2023-10-03T20:23:28.000Z | [
"region:us"
] | Elriggs | null | null | 1 | 338 | 2023-10-03T20:22:13 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 497257202
num_examples: 100000
download_size: 302558045
dataset_size: 497257202
---
# Dataset Card for "openwebtext-100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUT... | 366 | [
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-0.0... |
hyperpartisan_news_detection | 2023-06-13T07:46:19.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"bias-classification",
"regio... | null | Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
There are 2 parts:
- byarticle: Labeled t... | @inproceedings{kiesel-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Kiesel, Johannes and
Mestre, Maria and
Shukla, Rishabh and
Vincent, Emmanuel and
Adineh, Payam and
Corney, David and
Stein, Benno and
Potthast, Mar... | 8 | 337 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HyperpartisanNewsDetection
tags:
- bias-class... | 9,740 | [
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0... |
ChristophSchuhmann/MS_COCO_2017_URL_TEXT | 2021-11-27T15:39:29.000Z | [
"region:us"
] | ChristophSchuhmann | null | null | 11 | 337 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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jamescalam/youtube-transcriptions | 2022-10-22T01:20:07.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"task_ids:document-retrieval",
"task_ids:visual-question-answering",
"annotations_creators:no-an... | jamescalam | null | null | 18 | 336 | 2022-10-13T20:31:27 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Youtube Transcriptions
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- technical
- speech to text
- speech
- video
- video search
- audio
- audio... | 2,135 | [
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... |
argilla/databricks-dolly-15k-curated-multilingual | 2023-06-14T07:47:54.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:es",
"language:de",
"language:fr",
"license:cc-by-sa-3.0",
"machine-translated",
"instruction-following",
"region:us"
] | argilla | null | null | 35 | 336 | 2023-04-13T12:18:17 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: instruction_original_en
dtype: string
- name: context_original_en
dtype: string
- name: response_original_en
dtype... | 8,482 | [
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... |
SetFit/ade_corpus_v2_classification | 2022-09-05T14:14:53.000Z | [
"region:us"
] | SetFit | null | null | 0 | 335 | 2022-09-05T11:20:19 | # ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for classification if a sentence is ADE-related (True) or not (False).
**Train size: 17,637**
**Test size: 5,879**
[Source dataset](https://huggingface.co/datasets/ade_corpus_v2)
[Paper](https://www.sciencedirect.com/science/article/pii/S1532046... | 331 | [
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open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1 | 2023-10-26T01:30:07.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 335 | 2023-09-27T15:31:20 | ---
pretty_name: Evaluation run of mistralai/Mistral-7B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leade... | 39,083 | [
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doc2dial | 2022-11-18T19:58:53.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of di... | @inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natu... | 2 | 334 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: doc2dial
pretty_name: doc2dial
dataset_... | 22,650 | [
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web_of_science | 2023-04-05T13:42:58.000Z | [
"language:en",
"region:us"
] | null | The Web Of Science (WOS) dataset is a collection of data of published papers
available from the Web of Science. WOS has been released in three versions: WOS-46985, WOS-11967 and WOS-5736. WOS-46985 is the
full dataset. WOS-11967 and WOS-5736 are two subsets of WOS-46985. | @inproceedings{kowsari2017HDLTex,
title={HDLTex: Hierarchical Deep Learning for Text Classification},
author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE Inter... | 2 | 334 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: web-of-science-dataset
pretty_name: Web of Science Dataset
dataset_info:
- config_name: WOS5736
features:
- name: input_data
dtype: string
- name: label
dtype: int32
- name: label_level_1
dtype: int32
- name: label_level_2
dtype: int32
splits:
- name: ... | 8,273 | [
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Kyle1668/AG-Tweets | 2023-08-09T22:22:37.000Z | [
"region:us"
] | Kyle1668 | null | null | 0 | 334 | 2023-06-29T22:07:56 | ---
pretty_name: AG News Tweets
---
\subsection{Motivation}
AG News is a four-way topic classification task introduced in \cite{Zhang2015CharacterlevelCN}. In this setup, a task model must classify whether a given news article is about world events (\textbf{\textit{World}}), sports and athletics (\textbf{\textit{Spor... | 2,750 | [
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GonzaloA/fake_news | 2022-07-04T18:09:58.000Z | [
"region:us"
] | GonzaloA | null | null | 6 | 333 | 2022-03-02T23:29:22 | TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
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source_dataset... | 6,731 | [
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rubrix/gutenberg_spacy-ner | 2022-02-24T21:48:13.000Z | [
"region:us"
] | rubrix | null | null | 0 | 333 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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SajjadAyoubi/persian_qa | 2021-04-29T06:11:18.000Z | [
"region:us"
] | SajjadAyoubi | \\\\\\\Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia.
The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the ques... | \@misc{PersianQA,
author = {Sajjad Ayoubi, Mohammad Yasin Davoodeh},
title = {PersianQA: a dataset for Persian Question Answering},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/SajjjadAyobi/PersianQA}... | 4 | 332 | 2022-03-02T23:29:22 | # PersianQA: a dataset for Persian Question Answering
Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in... | 6,199 | [
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sagnikrayc/mctest | 2022-10-25T00:16:37.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"explanations-in-question-answering",
"region:us"
] | sagnikrayc | MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension. | @inproceedings{richardson-etal-2013-mctest,
title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
author = "Richardson, Matthew and
Burges, Christopher J.C. and
Renshaw, Erin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural ... | 2 | 332 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mctest
language_bcp47:
- en-US
tags:
- explanatio... | 2,944 | [
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open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1 | 2023-09-22T18:17:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 332 | 2023-09-22T18:16:15 | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/Hugg... | 65,057 | [
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biosses | 2022-11-03T16:31:20.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region... | null | BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset containing articles from the biomedical domain. The sentence pairs were eval... | @article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\\u{g}}anc{\\i}o{\\u{g}}lu, Gizem and {\\"O}zt{\\"u}rk, Hakime and {\\"O}zg{\\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
yea... | 4 | 331 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
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- found
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- en
license:
- gpl-3.0
multilinguality:
- monolingual
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- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: biosses
pretty_nam... | 6,641 | [
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totto | 2023-02-23T09:49:19.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2004.14373",
"region:us"
] | null | ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. | @inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
} | 5 | 331 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: totto
pretty_name: ToTTo
dataset_info:
features:
- n... | 17,009 | [
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HuggingFaceH4/instruction-dataset | 2023-02-28T22:30:11.000Z | [
"license:apache-2.0",
"region:us"
] | HuggingFaceH4 | null | null | 16 | 331 | 2023-02-28T21:26:43 | ---
license: apache-2.0
---
This is the blind eval dataset of high-quality, diverse, human-written instructions with demonstrations. We will be using this for step 3 evaluations in our RLHF pipeline. | 199 | [
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crystina-z/inlang-mrtydi-corpus | 2022-01-17T15:24:18.000Z | [
"region:us"
] | crystina-z | null | null | 0 | 330 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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allenai/soda | 2023-01-04T09:24:32.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|Atomic10x",
"language:en",
"license:cc-by-4.0",
"dialogue",
"narrative",
"co... | allenai | null | null | 99 | 330 | 2023-01-04T08:51:53 | ---
language:
- en
language_creators:
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- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SODA
size_categories:
- 1M<n<10M
splits:
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num_examples: 1191582
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data... | 4,886 | [
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ai4bharat/IndicSentiment | 2023-05-26T11:07:29.000Z | [
"region:us"
] | ai4bharat | \ | \ | 2 | 330 | 2023-01-14T16:26:02 | Entry not found | 15 | [
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open-llm-leaderboard/details_uni-tianyan__Uni-TianYan | 2023-09-18T02:40:22.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 330 | 2023-09-03T12:28:00 | ---
pretty_name: Evaluation run of uni-tianyan/Uni-TianYan
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uni-tianyan/Uni-TianYan](https://huggingface.co/uni-tianyan/Uni-TianYan) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard... | 38,583 | [
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result-kand2-sdxl-wuerst-karlo/023acaec | 2023-10-03T22:23:40.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 330 | 2023-10-03T22:23:39 | ---
dataset_info:
features:
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dtype: string
- name: id
dtype: int64
splits:
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num_bytes: 233
num_examples: 10
download_size: 1392
dataset_size: 233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "023acae... | 455 | [
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vincentmin/eli5_rlhf_explainlikeim5 | 2023-04-10T10:52:49.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] | vincentmin | null | null | 5 | 329 | 2023-04-07T19:22:14 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: Reddit Explain Like I'm 5 for Reinforcement Learning Human Feedback
size_categories:
- 100K<n<1M
---
# ELI5 paired
This is a processed version of the [`eli5`](https://huggingface.co/datasets/eli5) dataset.
Compared to ["eli5_rlhf"... | 1,519 | [
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open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA | 2023-09-22T21:37:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 329 | 2023-09-22T21:36:15 | ---
pretty_name: Evaluation run of adonlee/LLaMA_2_70B_LoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adonlee/LLaMA_2_70B_LoRA](https://huggingface.co/adonlee/LLaMA_2_70B_LoRA) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderbo... | 64,899 | [
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0.0... |
ostapeno/qa-platy_icl5_clen128_maxD-1_maxC10000_0.jsonl_length_matched | 2023-10-15T15:48:56.000Z | [
"region:us"
] | ostapeno | null | null | 0 | 329 | 2023-10-15T15:47:44 | Filtered ostapeno/qa-platy_icl5_clen128_maxD-1_maxC10000_0.jsonl to match per subject length from sordonia/qa-platy_icl0_clen128_maxD-1_maxC5000_0 | 146 | [
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0.0589599609375,... |
ComponentSoft/k8s-kubectl-10k | 2023-10-20T06:29:25.000Z | [
"region:us"
] | ComponentSoft | null | null | 0 | 329 | 2023-10-20T06:29:23 | ---
dataset_info:
features:
- name: objective
dtype: string
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dtype: string
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corypaik/prost | 2022-10-25T09:07:34.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en-US",
"license:apache-... | corypaik | *Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: ... | @inproceedings{aroca-ouellette-etal-2021-prost,
title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
author = "Aroca-Ouellette, St{\'e}phane and
Paik, Cory and
Roncone, Alessandro and
Kann, Katharina",
booktitle = "Findings of the Association for Computational Linguistics: ... | 1 | 328 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
extended:
- original
language_creators:
- expert-generated
language:
- en-US
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: prost
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-cho... | 5,732 | [
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yxchar/imdb-tlm | 2021-11-04T18:01:06.000Z | [
"region:us"
] | yxchar | null | null | 0 | 328 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Chris1/cityscapes | 2022-11-03T19:06:29.000Z | [
"region:us"
] | Chris1 | null | null | 1 | 327 | 2022-04-06T10:57:03 | Entry not found | 15 | [
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colbertv2/lotte_passages | 2023-08-23T01:55:55.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | colbertv2 | LoTTE Passages Dataset for ColBERTv2 | @inproceedings{santhanam-etal-2022-colbertv2,
title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Santhanam, Keshav and
Khattab, Omar and
Saad-Falcon, Jon and
Potts, Christopher and
Zaharia, Matei",
booktitle = "Proceedings of th... | 0 | 327 | 2022-07-14T22:44:41 | ---
viewer: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte passages from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 1M<n<10M
source_datasets:
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zishuod/pokemon-icons | 2022-09-24T15:35:39.000Z | [
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] | zishuod | null | null | 2 | 327 | 2022-09-24T15:12:08 | ---
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pretty_name: pokemon-icons
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---
# Dataset Card for pokemon-icons
## Table of Contents
- [Table of Contents](#ta... | 1,672 | [
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dyngnosis/function_names_v2 | 2023-08-02T16:41:15.000Z | [
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tucan-ai/summaries-de-v1 | 2023-10-18T14:33:42.000Z | [
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result-kand2-sdxl-wuerst-karlo/dbd855c1 | 2023-10-04T01:53:22.000Z | [
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# Dataset Card for "dbd855c... | 455 | [
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humicroedit | 2023-06-01T14:59:51.000Z | [
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"license:unknown",... | null | This new dataset is designed to assess the funniness of edited news headlines. | @article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
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TurkuNLP/turku_paraphrase_corpus | 2022-07-01T15:25:27.000Z | [
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crystina-z/inlang-mrtydi | 2022-01-16T19:56:13.000Z | [
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SetFit/tweet_eval_stance_abortion | 2022-09-05T13:09:04.000Z | [
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reciprocate/number-pairs | 2023-05-04T07:14:58.000Z | [
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medarc/mednli | 2023-09-28T21:15:27.000Z | [
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smangrul/chat-instruct-mixer | 2023-09-08T05:44:19.000Z | [
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McGill-NLP/FaithDial | 2023-02-05T04:09:45.000Z | [
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cfilt/HiNER-original | 2023-03-07T16:42:05.000Z | [
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nthngdy/bert_dataset_202203 | 2023-01-17T10:10:06.000Z | [
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SetFit/go_emotions | 2022-09-08T15:41:33.000Z | [
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] | SetFit | null | null | 4 | 324 | 2022-03-02T23:29:22 | # GoEmotions
This dataset is a port of the official [`go_emotions` dataset](https://huggingface.co/datasets/go_emotions) on the Hub. It only contains the `simplified` subset as these are the only fields we need for text classification. | 236 | [
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midas/inspec | 2022-03-05T03:08:37.000Z | [
"arxiv:1910.08840",
"region:us"
] | midas | Benchmark dataset for automatic identification of keyphrases from text published with the work - Improved automatic keyword extraction given more linguistic knowledge. Anette Hulth. In Proceedings of EMNLP 2003. p. 216-223. | @inproceedings{hulth2003improved,
title={Improved automatic keyword extraction given more linguistic knowledge},
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pages={216--223},
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} | 8 | 324 | 2022-03-02T23:29:22 | A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of English scientific papers. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/pdf/10.3115/1119355.1119383](https://dl.acm.org/doi/pdf/10.3115/1119355.1119383).
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hf-internal-testing/fixtures_ocr | 2021-12-07T08:07:29.000Z | [
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] | hf-internal-testing | \\n | \\n | 0 | 323 | 2022-03-02T23:29:22 | This dataset includes 2 images: one of the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) and one of the [SRIOE](https://rrc.cvc.uab.es/?ch=13) dataset.
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Bingsu/zeroth-korean | 2022-08-15T10:30:30.000Z | [
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---
# Zeroth-Korean
## Dataset Description
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EleutherAI/the_pile_deduplicated | 2022-12-02T23:49:09.000Z | [
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marmal88/skin_cancer | 2023-01-25T02:21:28.000Z | [
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thesistranslation/wmt14 | 2023-08-09T13:08:40.000Z | [
"region:us"
] | thesistranslation | null | @InProceedings{bojar-EtAl:2014:W14-33,
author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna... | 0 | 323 | 2023-07-31T09:21:04 | # Aim of this dataset
The code used to retrieve and create this dataset is almost identical to the one that you can find here [wmt14](https://huggingface.co/datasets/wmt14).
We only added the possibility to retrieve the "es-en" translation pairs from the wmt13. Keep in mind that for this language pair the validation an... | 582 | [
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0.0404052734375,
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0.02... |
ziozzang/EverythingLM-data-V2-Ko | 2023-08-23T07:03:47.000Z | [
"language:ko",
"license:mit",
"region:us"
] | ziozzang | null | null | 8 | 323 | 2023-08-23T06:53:09 | ---
license: mit
language:
- ko
---
# Translated into Korean with DeepL
All Texts are translated with DeepL. (Machine Translated.)
- Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and all datas are merged into single file and splitted by few code and hand.
- This... | 1,746 | [
[
-0.01605224609375,
-0.050201416015625,
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0.0709228515625,
-0.06658935546875,
-0.05255126953125,
-0.017730712890625,
-0.00149631500... |
autshumato | 2023-06-01T14:59:51.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:tn",
"language:ts",
"language:zu",
"lice... | null | Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine transla... | @article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
} | 2 | 322 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- tn
- ts
- zu
license:
- cc-by-2.5
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: aut... | 5,068 | [
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... |
ChaiML/20231007_chai_prize_model_feedback_all | 2023-10-08T00:36:27.000Z | [
"region:us"
] | ChaiML | null | null | 1 | 322 | 2023-10-08T00:34:03 | ---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: bot_id
dtype: string
- name: user_id
dtype: string
- name: conversation
dtype: string
- name: thumbs_up
dtype: bool
- name: feedback
dtype: string
- name: model_name
dtype: string
- name: season
d... | 744 | [
[
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0.050445556640625,
0.03863525390625,
-0.06842041015625,
-0.03668212890625,
-0.03759765625,
-0... |
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