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384eb79b722866980935dba622e022af860772ef
|
# Dataset Card for "truthfulqa_helm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
lighteval/truthfulqa_helm
|
[
"region:us"
] |
2023-05-12T10:42:54+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold_index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 59000, "num_examples": 163}, {"name": "valid", "num_bytes": 218075, "num_examples": 654}], "download_size": 130906, "dataset_size": 277075}}
|
2023-05-12T10:42:58+00:00
|
ae581b5d65d889d512166af33c7cb454075eff19
|
# Dataset Card for "Fashion_controlnet_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Abrumu/Fashion_controlnet_dataset
|
[
"region:us"
] |
2023-05-12T10:48:44+00:00
|
{"dataset_info": {"features": [{"name": "target", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "control", "dtype": "image"}, {"name": "CLIP_captions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9533440093.0, "num_examples": 11647}], "download_size": 9530317166, "dataset_size": 9533440093.0}}
|
2023-05-15T23:45:16+00:00
|
1c9fdebfabd1ff1dc7278b3cdcd5d692a34c7b36
|
# Dataset Card for "chrf-eferenceless-salt-dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sunbird/chrf-referenceless-salt-dev
|
[
"region:us"
] |
2023-05-12T10:48:46+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "source_language", "dtype": "string"}, {"name": "target_language", "dtype": "string"}, {"name": "chrf", "dtype": "float64"}, {"name": "hypothesis", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 497164, "num_examples": 2500}], "download_size": 281727, "dataset_size": 497164}}
|
2023-05-17T14:41:46+00:00
|
cb14887958cfff84c4eff127992cc944e894095b
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
burtenshaw/test
|
[
"region:us"
] |
2023-05-12T11:19:46+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "BATTERIES", "1": "CABLES & WIRES", "2": "HVA & FANS", "3": "LIGHTING", "4": "MOTORS", "5": "POWERSUPPL", "6": "SWITCHES", "7": "TUBES"}}}}], "splits": [{"name": "train", "num_bytes": 252368.8, "num_examples": 2400}, {"name": "test", "num_bytes": 63092.2, "num_examples": 600}], "download_size": 207275, "dataset_size": 315461.0}}
|
2023-05-12T11:24:36+00:00
|
e342f33bdb5db18ba3c3118736b7733b5db8d880
|
# Dataset Card for "lyrr-lanadelrey"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
adabingw/lyrr-lanadelrey
|
[
"region:us"
] |
2023-05-12T11:31:31+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 548833, "num_examples": 367}], "download_size": 241641, "dataset_size": 548833}}
|
2023-05-15T01:02:06+00:00
|
f589236efcb1550fb358401eed264bf94ac8d13e
|
# Overview
SGDD-TST - [Schema-Guided Dialogue Dataset for Text Style Transfer](https://arxiv.org/abs/2206.09676) is a dataset for evaluating the quality of content similarity measures for text style transfer in the domain of the personal plans. The original texts were obtained from [The Schema-Guided
Dialogue Dataset](https://arxiv.org/pdf/1909.05855.pdf) and were paraphrased by the [T5-based model](https://huggingface.co/ceshine/t5-paraphrase-paws-msrp-opinosis) trained on [GYAFC formality dataset](https://aclanthology.org/N18-1012/). The results were annotated by the crowdsource workers using [Yandex.Toloka](https://toloka.yandex.ru/).
# File description
The file consists of the following columns
- INPUT:text_first - the original text
- INPUT:text_second - formality transferred text
- OUTPUT:result - automatically assigned the label of the annotation (David-Skene aggregation method is used)
- CONFIDENCE:result - confidence of the annotation
- vote_type -
- vote_different - number of votes for the option "The texts are completely different"
- vote_some_details_lost - number of votes for the option "The texts are similar but have significant differences"
- vote_OK - number of votes for the option "The texts mean the same or have minor differences"
- **average - an averaged score of content similarity. This score can be used for evaluating the quality of content similarity measures, e.g. by calculating the Spearman Rank Correlation Coefficient between these scores and automatic scores**
# Contact and Citations
If you have any questions feel free to drop a line to [Nikolay](mailto:[email protected])
If you find this repository helpful, feel free to cite our publication:
```
@InProceedings{10.1007/978-3-031-08473-7_40,
author="Babakov, Nikolay
and Dale, David
and Logacheva, Varvara
and Krotova, Irina
and Panchenko, Alexander",
editor="Rosso, Paolo
and Basile, Valerio
and Mart{\'i}nez, Raquel
and M{\'e}tais, Elisabeth
and Meziane, Farid",
title="Studying the Role of Named Entities for Content Preservation in Text Style Transfer",
booktitle="Natural Language Processing and Information Systems",
year="2022",
publisher="Springer International Publishing",
address="Cham",
pages="437--448",
abstract="Text style transfer techniques are gaining popularity in Natural Language Processing, finding various applications such as text detoxification, sentiment, or formality transfer. However, the majority of the existing approaches were tested on such domains as online communications on public platforms, music, or entertainment yet none of them were applied to the domains which are typical for task-oriented production systems, such as personal plans arrangements (e.g. booking of flights or reserving a table in a restaurant). We fill this gap by studying formality transfer in this domain.",
isbn="978-3-031-08473-7"
}
```
|
NiGuLa/SGDD-TST
|
[
"task_categories:sentence-similarity",
"language:en",
"license:cc",
"text style transfer",
"arxiv:2206.09676",
"arxiv:1909.05855",
"region:us"
] |
2023-05-12T11:38:44+00:00
|
{"language": ["en"], "license": "cc", "task_categories": ["sentence-similarity"], "pretty_name": "Schema-Guided Dialogue Dataset for Text Style Transfer", "tags": ["text style transfer"], "viewer": true}
|
2023-05-12T12:16:58+00:00
|
09a64b35e1d692baa75f5210e33952aff7045fc2
|
robbinfan/live
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-12T12:21:09+00:00
|
{"license": "apache-2.0"}
|
2023-05-12T12:21:09+00:00
|
|
13576043b24ed177e28a43573fbe3266a611ade4
|
# Dataset Card for "old_push2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
polinaeterna/old_push2
|
[
"region:us"
] |
2023-05-12T12:22:07+00:00
|
{"dataset_info": [{"config_name": "custom", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 80, "num_examples": 5}], "download_size": 1317, "dataset_size": 80}, {"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 160, "num_examples": 10}], "download_size": 1371, "dataset_size": 160}], "builder_config": [{"config_name": "custom", "data_files": [{"split": "train", "pattern": "custom/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "pattern": "data/train-*"}]}]}
|
2023-05-12T12:41:32+00:00
|
213fb92565bff3452e3b77c98ee67f2322c3869e
|
allwefantasy/example_videos
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-12T12:24:58+00:00
|
{"license": "apache-2.0"}
|
2023-05-12T12:24:58+00:00
|
|
fc66ff22c614f7ec5136d7cea757d96f4b112f51
|
# Dataset Card for "rlhf-hackathon-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
center-for-humans-and-machines/rlhf-hackathon-data
|
[
"region:us"
] |
2023-05-12T12:29:17+00:00
|
{"dataset_info": {"features": [{"name": "prompt_id", "dtype": "int64"}, {"name": "answer_id_1", "dtype": "int64"}, {"name": "answer_id_2", "dtype": "int64"}, {"name": "identity", "dtype": "string"}, {"name": "preference_prompt", "dtype": "string"}, {"name": "preference", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33114, "num_examples": 30}], "download_size": 14217, "dataset_size": 33114}}
|
2023-05-12T12:29:21+00:00
|
531de44c7ef26932f51de63a0af98bc4c9f84c8c
|
# ニコニコ実況 過去ログアーカイブ
ニコニコ実況 過去ログアーカイブは、[ニコニコ実況](https://jk.nicovideo.jp)のサービス開始から現在までのすべての過去ログコメントを収集したデータセットです。
去る2020年12月、ニコニコ実況は[ニコニコ生放送内の一公式チャンネルとしてリニューアル](https://blog.nicovideo.jp/niconews/143148.html)されました。
これに伴い、2009年11月から運用されてきた旧システムは提供終了となり（事実上のサービス終了）、torne や BRAVIA などの家電への対応が軒並み終了する中、当時の生の声が詰まった約11年分の過去ログも同時に失われることとなってしまいました。
そこで 5ch の DTV 板の住民が中心となり、旧ニコニコ実況が終了するまでに11年分の全チャンネルの過去ログをアーカイブする計画が立ち上がりました。紆余曲折あり Nekopanda 氏が約11年分のラジオや BS も含めた全チャンネルの過去ログを完璧に取得してくださったおかげで、11年分の過去ログが電子の海に消えていく事態は回避できました。
しかし、旧 API が廃止されてしまったため過去ログを API 経由で取得することができなくなり、またアーカイブされた過去ログから見たい範囲のログを探す場合も、アーカイブのサイズが合計約 150GB もあることから、とても以前のように手軽に過去ログに触れることはできなくなってしまいました。
一方、ニコニコ生放送内の一公式チャンネルとして移行した新ニコニコ実況では、タイムシフト（旧ニコニコ実況での過去ログに相当）の視聴期限は3週間までとなっているため、その期限を過ぎると過去ログは視聴できなくなってしまいます。
また一般会員は事前にタイムシフト予約をしておく必要があるなど、以前のような利便性は失われています。
私たちは、ニコニコ実況に投稿された日本のテレビ放送についてのコメントは、当時の世相や時代背景を端的に表す、歴史的価値のある資料だと考えています。
このデータセットでは、ニコニコ実況のすべての過去ログを後世に残すべく、Nekopanda 氏が配布されていた旧ニコニコ実況の 2020/12/15 までのすべての過去ログに加え、コミュニティベースの番組も含めた新ニコニコ実況の当日分の過去ログを5分に1回収集し、随時反映しています。
過去ログをかんたんに取得するための [API](https://jikkyo.tsukumijima.net/) もあります。
よろしければそちらもご活用ください。
## Dataset Structure
### Builder Config
| Key | Value Type | Default Value | Description |
| --------------- | ---------- | ------------- | ----------- |
| channel_id | string | None | 過去ログを取得するニコニコ実況チャンネルの ID (省略時はすべてのチャンネル) |
| year | int | None | 取得する過去ログの年 (省略時はすべての年) |
| number_of_files | int | None | 取得する過去ログファイルの数 (省略時はすべてのファイル) |
### Data Splits
| Split | Approximate Size | Description |
| ------- | ---------------- | ----------- |
| sample | 1GB | サンプルとして、2022年中に投稿された TOKYO MX (ID: jk9) のすべての過去ログコメントを取得します。1GB ほどあります。 |
| all | 180GB | 全チャンネル/全期間のすべての過去ログコメントを取得します。180GB 近くあるため注意してください。 |
### Data Fields
| Field | Type | Description |
| --------------- | -------- | ----------- |
| thread | string | コメントのスレッド ID |
| no | int64 | コメント番号 (コメ番) |
| vpos | int64 | スレッド ID から起算したコメントの再生位置 (1/100秒) |
| date | int64 | コメント投稿時間の UNIX タイムスタンプ |
| date_usec | int64 | コメント投稿時間の小数点以下の時間 |
| user_id | string | ユーザー ID (コマンドに 184 が指定されている場合は匿名化され、1週間ほどでシャッフルされる) |
| mail | string | コメントのコマンド (184, red naka big など、省略されることもある) |
| premium | boolean | コメントしたユーザーがプレミアム会員であれば True |
| anonymity | boolean | 匿名コメントであれば True |
| content | string | コメント本文 (AA など、まれに複数行コメントがあるので注意) |
## Example
```python
from datasets import load_dataset
dataset = load_dataset('KakologArchives/KakologArchives', 'all', channel_id='jk211', year=2023, number_of_files=10)
for data in dataset['train']:
print(data)
```
## Licensing Information
[MIT License](https://opensource.org/license/mit/)
|
KakologArchives/KakologArchives
|
[
"task_categories:text-classification",
"language:ja",
"license:mit",
"region:us"
] |
2023-05-12T12:31:56+00:00
|
{"language": ["ja"], "license": "mit", "task_categories": ["text-classification"], "pretty_name": "\u30cb\u30b3\u30cb\u30b3\u5b9f\u6cc1 \u904e\u53bb\u30ed\u30b0\u30a2\u30fc\u30ab\u30a4\u30d6"}
|
2024-02-17T17:42:32+00:00
|
9248c7433697aeb6c9d33accdb4ded5c8c9b6674
|
# Dataset Card for XNLI-ca
## 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)
- [Example](#example)
- [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
- **Website:** https://zenodo.org/record/7973976
- **Point of Contact:** [email protected]
### Dataset Summary
Professional translation into Catalan of the Cross-lingual Natural Language Inference [XNLI dataset](https://github.com/facebookresearch/XNLI), an evaluation corpus for language transfer and cross-lingual sentence classification.
XNLI-ca is a collection of 7,500 sentence pairs annotated with textual entailment.
XNLI is restricted to only non-commercial research purposes under the [Creative Commons Attribution Non-commercial 4.0 International Public License](https://creativecommons.org/licenses/by-nc/4.0/).
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Two JSON files, one for each split.
### Example:
<pre>
{
"label": "contradiction",
"premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
"hypothesis": "No he tornat a parlar amb ell."
},
{
"label": "entailment",
"premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
"hypothesis": "Estava tan molest que vaig començar a parlar amb ell de nou."
},
{
"label": "neutral",
"premise": "Bé, ni tan sols estava pensant en això, però estava molt frustrat i vaig acabar tornant a parlar amb ell.",
"hypothesis": "Vam tenir una gran xerrada."
}
</pre>
### Data Fields
- premise: text
- hypothesis: text related to the premise
- label: relation between premise and hypothesis:
* 0: entailment
* 1: neutral
* 2: contradiction
### Data Splits
* dev.json: 2490 examples
* test.json: 5010 examples
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
[XNLI](https://github.com/facebookresearch/XNLI).
#### Initial Data Collection and Normalization
This dataset is a professional translation of XNLI into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.
#### Who are the source language producers?
For more information on how XNLI was created, refer to the paper [XNLI: Evaluating Cross-lingual Sentence Representations](https://arxiv.org/abs/1809.05053), or
visit the [XNLI's webpage](https://github.com/facebookresearch/XNLI).
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
This is a professional translation of the XNLI corpus and its annotations.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
XNLI is restricted to only non-commercial research purposes under the [Creative Commons Attribution Non-commercial 4.0 International Public License](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
```
[DOI](https://doi.org/10.5281/zenodo.7973976)
### Contributions
[N/A]
|
projecte-aina/xnli-ca
|
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:professional translators",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-sa-4.0",
"arxiv:1809.05053",
"region:us"
] |
2023-05-12T12:32:41+00:00
|
{"annotations_creators": ["professional translators"], "language": ["ca"], "license": ["cc-by-nc-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "xnli-ca"}
|
2024-01-29T15:00:50+00:00
|
c9be41311cf578d510084546c23c5336fae29dd7
|
Liiizt9/230512pics
|
[
"license:openrail",
"region:us"
] |
2023-05-12T12:42:14+00:00
|
{"license": "openrail"}
|
2023-05-12T12:47:32+00:00
|
|
720b89e7c29d240e47f1feacfef45b6d58bcc45d
|
polinaeterna/no_configs_in_metadata_copy
|
[
"region:us"
] |
2023-05-12T12:49:58+00:00
|
{"duplicated_from": "polinaeterna/audiofolder_no_configs_in_metadata"}
|
2023-05-12T12:49:58+00:00
|
|
907a48d3cd8008f11ae9782b2d54b999f2aebe54
|
# Dataset Card for "rlhf-hackathon-prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
center-for-humans-and-machines/rlhf-hackathon-prompts
|
[
"region:us"
] |
2023-05-12T12:58:27+00:00
|
{"dataset_info": {"features": [{"name": "prompt_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1266, "num_examples": 19}], "download_size": 2556, "dataset_size": 1266}}
|
2023-05-12T14:06:24+00:00
|
418b036f939f87ea4348662677465a5a75d329c9
|
Cacau/wylarllysBase
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-12T13:06:12+00:00
|
{"license": "apache-2.0"}
|
2023-05-13T13:49:06+00:00
|
|
54d11f2b539c23eceea55023dd196a9ec3c6bf19
|
Датасет dialogsum переведенный на русский язык. Глюки перевода устранены автоматической чисткой
|
rcp-meetings/rudialogsum_v2
|
[
"task_categories:text2text-generation",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ru",
"license:mit",
"region:us"
] |
2023-05-12T13:30:27+00:00
|
{"language": ["ru"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["text2text-generation", "summarization"]}
|
2023-05-12T13:35:48+00:00
|
9585988079b67ffee934cd417f3edb23c3eb5401
|
Original STORIES dataset from the paper [A Simple Method for Commonsense Reasoning](https://arxiv.org/pdf/1806.02847v2.pdf).
|
lucadiliello/STORIES
|
[
"task_categories:fill-mask",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc",
"arxiv:1806.02847",
"region:us"
] |
2023-05-12T13:42:41+00:00
|
{"language": ["en"], "license": "cc", "size_categories": ["100K<n<1M"], "task_categories": ["fill-mask", "text-generation"], "pretty_name": "STORIES", "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34099206982, "num_examples": 945354}, {"name": "dev", "num_bytes": 41804891, "num_examples": 946}, {"name": "test", "num_bytes": 42356443, "num_examples": 947}], "download_size": 15347401118, "dataset_size": 34183368316}}
|
2023-07-18T06:19:25+00:00
|
7324f71acea2bbf708dddd716d2274be655c649f
|
Skrillll/categorization
|
[
"license:openrail",
"region:us"
] |
2023-05-12T13:47:01+00:00
|
{"license": "openrail"}
|
2023-05-12T13:47:02+00:00
|
|
a2ffbfa0511c44d288307ee67c99ae05c631ff26
|
biglam/on_the_books
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-3.0",
"lam",
"legal",
"region:us"
] |
2023-05-12T13:54:18+00:00
|
{"language": ["en"], "license": "cc-by-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "On the Books Training Set", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "jim_crow", "dtype": {"class_label": {"names": {"0": "no_jim_crow", "1": "jim_crow"}}}}, {"name": "type", "dtype": "string"}, {"name": "chapter_num", "dtype": "int32"}, {"name": "section_num", "dtype": "int32"}, {"name": "chapter_text", "dtype": "string"}, {"name": "section_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2119395, "num_examples": 1785}], "download_size": 944579, "dataset_size": 2119395}, "tags": ["lam", "legal"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2024-01-09T10:37:26+00:00
|
|
37f168f76da1cbc0fead701e96006892ed723549
|
code:
https://i2d2.allen.ai/
https://arxiv.org/abs/2212.09246
```
@inproceedings{Bhagavatula2022GenGen,
title={Generating Generics: Knowledge Induction with NeuroLogic and Self-Imitation},
author={Chandra Bhagavatula, Jena D. Hwang, Doug Downey, Ronan Le Bras, Ximing Lu, Lianhui Qin, Keisuke Sakaguchi, Swabha Swayamdipta, Peter West, Yejin Choi},
booktitle={arXiv},
year={2022}
}
```
|
tasksource/I2D2
|
[
"task_categories:text-classification",
"language:en",
"license:apache-2.0",
"commonsense",
"arxiv:2212.09246",
"region:us"
] |
2023-05-12T13:55:55+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-classification"], "tags": ["commonsense"]}
|
2023-05-31T07:34:55+00:00
|
573301ba20219a35166586b9619c04e891ddd801
|
# Dataset Card for "russian_poetry_with_keywords"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AnyaSchen/russian_poetry_with_keywords
|
[
"region:us"
] |
2023-05-12T14:10:56+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "keywords", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4073925, "num_examples": 7755}], "download_size": 2114437, "dataset_size": 4073925}}
|
2023-05-23T09:32:26+00:00
|
6c3d1e4476f14696a0f2093a746dc4386a191e05
|
# DAIS-Question-Answers Dataset
This dataset contains question-answer pairs created using ChatGPT using text data scraped from the Databricks Data and AI Summit 2023 (DAIS 2023) [homepage](https://www.databricks.com/dataaisummit/)
as well as text from any public page that is linked in that page or is a two-hop linked page.
We have used this dataset to fine-tune our [DAIS DLite model](https://huggingface.co/aisquared/dlite-dais-2023), along with our dataset of [webpage texts](https://huggingface.co/datasets/aisquared/dais-2023). Feel free to check them out!
**Note that, due to the use of ChatGPT to curate these question-answer pairs, this dataset is not licensed for commercial use.**
|
aisquared/dais-question-answers
|
[
"task_categories:conversational",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] |
2023-05-12T14:15:25+00:00
|
{"language": ["en"], "license": "cc-by-nc-4.0", "task_categories": ["conversational"], "pretty_name": "Databricks Data and AI Summit 2023 Question-Answer Pairs"}
|
2023-06-26T13:56:43+00:00
|
6fab59b1d86629077dc8aa25a333f9fff7cdf5a7
|
# Dataset Card for "rmh_tokenized_512_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/rmh_tokenized_512_train
|
[
"region:us"
] |
2023-05-12T14:18:41+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 71104398036.0, "num_examples": 10663527}], "download_size": 5448818845, "dataset_size": 71104398036.0}}
|
2023-05-12T22:06:14+00:00
|
1c43d78b2ba45cd2a55e9bfd336d4935b2f50ec7
|
# Dataset Card for "rmh_tokenized_512_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
stoddur/rmh_tokenized_512_test
|
[
"region:us"
] |
2023-05-12T14:19:26+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3742341652.0, "num_examples": 561239}], "download_size": 1025438698, "dataset_size": 3742341652.0}}
|
2023-05-12T14:31:20+00:00
|
f95716fb8655324baf7cea89944a12c7fbd2dc04
|
# Dataset Card for "cup_it_ds_split_with_lang_with_topic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ummagumm-a/cup_it_ds_split_with_lang_with_topic
|
[
"region:us"
] |
2023-05-12T14:36:34+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "comments", "list": [{"name": "score", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "lang", "dtype": "string"}, {"name": "lang_score", "dtype": "float64"}, {"name": "topic", "dtype": "float64"}, {"name": "topic_prob", "dtype": "float64"}, {"name": "was_outlier", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 219441173, "num_examples": 79296}, {"name": "validation", "num_bytes": 24600381, "num_examples": 8811}, {"name": "test", "num_bytes": 40295844, "num_examples": 14004}], "download_size": 178475671, "dataset_size": 284337398}}
|
2023-05-12T14:48:29+00:00
|
8eb5660e23fa15ba702aa7b2ee72edb51d0aab15
|
# Dataset Card for "eli5-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
explodinggradients/eli5-test
|
[
"region:us"
] |
2023-05-12T14:57:48+00:00
|
{"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "ground_truth", "sequence": "string"}, {"name": "references", "sequence": "null"}, {"name": "generated_text", "dtype": "string"}], "splits": [{"name": "test_eli5", "num_bytes": 1159353, "num_examples": 500}], "download_size": 716889, "dataset_size": 1159353}}
|
2023-05-12T16:20:22+00:00
|
f79e7504f15d77a57896a91e79f324d2690fcc3e
|
# Dataset Card for "reddit_posts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ummagumm-a/reddit_posts
|
[
"region:us"
] |
2023-05-12T15:50:51+00:00
|
{"dataset_info": {"features": [{"name": "post_id", "dtype": "string"}, {"name": "post_title", "dtype": "string"}, {"name": "post_body", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "post_url", "dtype": "string"}, {"name": "flair_text", "dtype": "string"}, {"name": "score", "dtype": "int64"}, {"name": "comments", "dtype": "int64"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "date-time", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 97334, "num_examples": 320}, {"name": "test", "num_bytes": 27972, "num_examples": 80}], "download_size": 81893, "dataset_size": 125306}}
|
2023-05-13T03:33:20+00:00
|
fd1af4375c6681434f62ea052c65f526351a18fa
|
# Dataset Card for "reddit_posts_comments"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ummagumm-a/reddit_posts_comments
|
[
"region:us"
] |
2023-05-12T15:54:13+00:00
|
{"dataset_info": {"features": [{"name": "post_id", "dtype": "string"}, {"name": "comment", "dtype": "string"}, {"name": "controversiality", "dtype": "int64"}, {"name": "edited", "dtype": "string"}, {"name": "is_submitter", "dtype": "bool"}, {"name": "score", "dtype": "int64"}, {"name": "num_comments_below", "dtype": "int64"}, {"name": "date-time", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 591662, "num_examples": 3201}, {"name": "test", "num_bytes": 115871, "num_examples": 643}], "download_size": 367223, "dataset_size": 707533}}
|
2023-05-13T03:33:30+00:00
|
9a964ca7c62165acd72993526cb574a72481d5df
|
kingjambal/jambal_common_voice
|
[
"license:openrail",
"region:us"
] |
2023-05-12T15:55:40+00:00
|
{"license": "openrail"}
|
2023-05-15T03:24:58+00:00
|
|
cec6ddc8693caf83cff3d42800d36668c6623c1e
|
# Dataset Card for "summary_seq_label"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Astonzzh/summary_seq_label
|
[
"region:us"
] |
2023-05-12T15:58:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "ids", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "summary", "dtype": "string"}, {"name": "sentences", "sequence": "string"}, {"name": "sentence_labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9076109.781886647, "num_examples": 9321}, {"name": "test", "num_bytes": 504390.6090566766, "num_examples": 518}, {"name": "validation", "num_bytes": 504390.6090566766, "num_examples": 518}], "download_size": 3898256, "dataset_size": 10084890.999999998}}
|
2023-05-15T15:58:00+00:00
|
6b3821b0e765c349cbf3c3285b618312635e5af7
|
tasksource/wiki-hades
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-12T16:44:51+00:00
|
{"license": "apache-2.0"}
|
2023-05-12T16:46:57+00:00
|
|
9b9e083f6ed8f1a2a50070a59838de40ce313073
|
# Dataset Card for "news_section"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Hansollll/news_section
|
[
"region:us"
] |
2023-05-12T16:47:30+00:00
|
{"dataset_info": {"features": [{"name": "news_content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 705995812, "num_examples": 295635}], "download_size": 399317655, "dataset_size": 705995812}}
|
2023-05-12T16:53:19+00:00
|
9160346b537ea540c01a1da7867df53ed876763c
|
# Dataset Card for "VQAv2_test_no_image_split_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_0
|
[
"region:us"
] |
2023-05-12T16:48:20+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2406349850, "num_examples": 44780}], "download_size": 651260802, "dataset_size": 2406349850}}
|
2023-05-12T17:11:39+00:00
|
ef0cf3c6707b3b3ddfd8c4df1eb0da21f6c71792
|
# Dataset Card for "VQAv2_test_no_image_split_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_1
|
[
"region:us"
] |
2023-05-12T16:48:27+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2264713694, "num_examples": 44780}], "download_size": 599150115, "dataset_size": 2264713694}}
|
2023-05-12T17:12:47+00:00
|
49e2601b05b8cac63e89a2890dcdb5911af21aa4
|
# Dataset Card for "VQAv2_test_no_image_split_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_2
|
[
"region:us"
] |
2023-05-12T16:48:33+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2111764519, "num_examples": 44780}], "download_size": 545247690, "dataset_size": 2111764519}}
|
2023-05-12T17:13:41+00:00
|
73ca804d34cf5fae11bf51a3cf8fd101aa4c077c
|
# Dataset Card for "VQAv2_test_no_image_split_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_3
|
[
"region:us"
] |
2023-05-12T16:48:40+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2150238682, "num_examples": 44779}], "download_size": 540758200, "dataset_size": 2150238682}}
|
2023-05-12T17:14:39+00:00
|
db1610fee1b0943990d444ffaf45a9db036f40e5
|
# Dataset Card for "VQAv2_test_no_image_split_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_4
|
[
"region:us"
] |
2023-05-12T16:48:46+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2173513371, "num_examples": 44779}], "download_size": 570289348, "dataset_size": 2173513371}}
|
2023-05-12T17:15:55+00:00
|
d545c7fada5f2b0d2c442ecb6a151bbddfea05dd
|
# Dataset Card for "VQAv2_test_no_image_split_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_5
|
[
"region:us"
] |
2023-05-12T16:48:53+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2150328389, "num_examples": 44779}], "download_size": 551567211, "dataset_size": 2150328389}}
|
2023-05-12T17:17:02+00:00
|
089906663cf4c9f2b9661c42a16df4ae5643e5f8
|
# Dataset Card for "VQAv2_test_no_image_split_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_6
|
[
"region:us"
] |
2023-05-12T16:48:59+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2205691499, "num_examples": 44779}], "download_size": 577059344, "dataset_size": 2205691499}}
|
2023-05-12T17:17:56+00:00
|
80dedbb4090332071dc936f1e9d1fbefe4f1098d
|
# Dataset Card for "VQAv2_test_no_image_split_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_7
|
[
"region:us"
] |
2023-05-12T16:50:07+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2142349083, "num_examples": 44779}], "download_size": 542841593, "dataset_size": 2142349083}}
|
2023-05-12T17:18:53+00:00
|
161687707cf53db24d94bb054dbd44f0b83b1e2f
|
# Dataset Card for "VQAv2_test_no_image_split_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_8
|
[
"region:us"
] |
2023-05-12T16:52:01+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2167201531, "num_examples": 44779}], "download_size": 559008780, "dataset_size": 2167201531}}
|
2023-05-12T17:19:54+00:00
|
311e38be71924bceb7dfef42913c4ddd62d5cd55
|
# Dataset Card for "VQAv2_test_no_image_split_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_no_image_split_9
|
[
"region:us"
] |
2023-05-12T16:52:12+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2186231101, "num_examples": 44779}], "download_size": 571318700, "dataset_size": 2186231101}}
|
2023-05-12T17:20:57+00:00
|
2fab856d27642ec6287b4162b11aa576b23ef0c2
|
# Dataset Card for Bulgarian QnA reasoning with ~2.7K entries.
### Dataset Summary
Contains Parquet of a list of instructions and answers.
Each row consists of
* INSTRUCTION
* RESPONSE
* SOURCE (reasoning_bg)
* METADATA (json with language, url, id).
### Original Dataset is available here:
* https://huggingface.co/datasets/reasoning_bg
|
0x22almostEvil/reasoning_bg_oa
|
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:bg",
"license:apache-2.0",
"QnA",
"reasoning",
"region:us"
] |
2023-05-12T17:04:40+00:00
|
{"language": ["bg"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "tags": ["QnA", "reasoning"]}
|
2023-05-13T14:42:11+00:00
|
d2d5e037fc2d3fb676c9f6381ce2cd34a4678a45
|
Thouph/incomplete-384
|
[
"license:mit",
"region:us"
] |
2023-05-12T17:19:26+00:00
|
{"license": "mit"}
|
2023-05-17T13:00:07+00:00
|
|
7d96081ef1d29fc846c06583d541a6a7ddfd77cf
|
# Dataset Card for "BilbaoCaptions"
## Dataset Description
- **Homepage:** https://github.com/TheMrguiller/MUCSI_Modal
- **Repository:** https://github.com/TheMrguiller/MUCSI_Modal
- **Paper:** It is a follow up of the Flamingo model paper
- **Leaderboard:**
- **Point of Contact:** https://github.com/TheMrguiller/MUCSI_Modal
### Dataset Summary
This dataset was collected for a proyect for a master degree in Computation and Intelligent System from University of Deusto. It was done by students and recolected from webpages famous in the Basque Country: Deia and Getimages.
### Supported Tasks and Leaderboards
The dataset is prepared to used it for visual question-answering.
### Languages
The dataset is in english.
## Dataset Structure
### Data Fields
- `Caption`: This field has the description of the image.
- `Image`: This field has the image corresponding to the description.
### Data Splits
The dataset is split in 80% train and 20% test.
## Considerations for Using the Data
The dataset has some flaws regarding to the descriptions. The descriptions sometimes are to specific for a captioning task. There are also to many futbol match data, so it isnt to well balanced. There are also some description that are to generic.
## Additional Information
### Dataset Curators
The curators of this dataset where the students from the Masters degree in Computation and Inteligent Systems from University of Deusto.
|
TheMrguiller/BilbaoCaptions
|
[
"size_categories:100B<n<1T",
"language:en",
"code",
"region:us"
] |
2023-05-12T17:30:37+00:00
|
{"language": ["en"], "size_categories": ["100B<n<1T"], "dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1372144989.6, "num_examples": 3960}, {"name": "test", "num_bytes": 343036247.4, "num_examples": 990}], "download_size": 1709055735, "dataset_size": 1715181237}, "tags": ["code"]}
|
2023-08-24T10:43:23+00:00
|
2c761d9f2ce70bee372055bc6c80f5b3d93fdf40
|
AKAWIZ/telugu_asr_custom
|
[
"license:unlicense",
"region:us"
] |
2023-05-12T17:39:18+00:00
|
{"license": "unlicense"}
|
2023-05-12T17:39:18+00:00
|
|
691b0d9bd48ade766778c940011ca1c549f6359b
|
License: CDLA-Sharing-1.0
-------------
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759.
The models referred to in the paper were trained on TinyStories-train.txt (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of the stories together with metadata and the prompt that was used to create each story.
TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
Evaluation_prompts.yaml: List of prompts used to evaluate our models (see paper)
|
roneneldan/TinyStories
|
[
"arxiv:2305.07759",
"region:us"
] |
2023-05-12T18:04:09+00:00
|
{}
|
2023-12-04T15:12:38+00:00
|
a81294498cc4a2465df5186ea1bfa5b7c5581bbf
|
# Dataset Card for "VQAv2_test_split_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_0
|
[
"region:us"
] |
2023-05-12T18:19:50+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9462509481.0, "num_examples": 44780}], "download_size": 1944305372, "dataset_size": 9462509481.0}}
|
2023-05-12T18:44:08+00:00
|
8de58158714fd31bff13530852b4e1d06862abc8
|
# Dataset Card for "VQAv2_test_split_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_1
|
[
"region:us"
] |
2023-05-12T18:21:47+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9252683950.0, "num_examples": 44780}], "download_size": 1921165507, "dataset_size": 9252683950.0}}
|
2023-05-12T18:47:18+00:00
|
5e55a222ddb457ab34b87b8261b47ff37fb7998f
|
# Dataset Card for "VQAv2_test_split_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_2
|
[
"region:us"
] |
2023-05-12T18:23:48+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9209134640.0, "num_examples": 44780}], "download_size": 1869493165, "dataset_size": 9209134640.0}}
|
2023-05-12T18:50:23+00:00
|
ae13de32bce9f539112461b95715a936da4ee86e
|
# Dataset Card for "VQAv2_test_split_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_3
|
[
"region:us"
] |
2023-05-12T18:25:43+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9127511526.0, "num_examples": 44779}], "download_size": 1825926763, "dataset_size": 9127511526.0}}
|
2023-05-12T18:53:21+00:00
|
c1e0bdefa7c4d8d1b612f57dc03b9dc5088099af
|
# Dataset Card for "VQAv2_test_split_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_4
|
[
"region:us"
] |
2023-05-12T18:27:52+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9135639447.0, "num_examples": 44779}], "download_size": 1867482751, "dataset_size": 9135639447.0}}
|
2023-05-12T18:56:33+00:00
|
84498b18c7e9a2575bea164a20307a57fba4a0f1
|
# Dataset Card for "VQAv2_test_split_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_5
|
[
"region:us"
] |
2023-05-12T18:30:07+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9194077268.0, "num_examples": 44779}], "download_size": 1868487689, "dataset_size": 9194077268.0}}
|
2023-05-12T18:59:46+00:00
|
42bfb0c5aa236c1bed35e46c021b587e52188c98
|
# Dataset Card for "VQAv2_test_split_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_6
|
[
"region:us"
] |
2023-05-12T18:32:06+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9245469054.0, "num_examples": 44779}], "download_size": 1848721947, "dataset_size": 9245469054.0}}
|
2023-05-12T19:02:53+00:00
|
46f72bfea875fb7e025d7c58b142eb7b5e33bfa0
|
# Dataset Card for "VQAv2_test_split_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_7
|
[
"region:us"
] |
2023-05-12T18:33:59+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9124723541.0, "num_examples": 44779}], "download_size": 1820072755, "dataset_size": 9124723541.0}}
|
2023-05-12T19:05:57+00:00
|
d6c36ae89ba8d4128a29affb7b3b9042b5e13e31
|
# Dataset Card for "VQAv2_test_split_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_8
|
[
"region:us"
] |
2023-05-12T18:36:01+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9157441080.0, "num_examples": 44779}], "download_size": 1848746963, "dataset_size": 9157441080.0}}
|
2023-05-12T19:09:05+00:00
|
11b063b6ab6217a321afbc6dc0663845feef83b3
|
# Dataset Card for "VQAv2_test_split_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Multimodal-Fatima/VQAv2_test_split_9
|
[
"region:us"
] |
2023-05-12T18:38:30+00:00
|
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_B_16_with_openai", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 9224825085.0, "num_examples": 44779}], "download_size": 1858242052, "dataset_size": 9224825085.0}}
|
2023-05-12T19:12:10+00:00
|
6263585072ce3b435ed09658613553fbf4e74184
|
# Dataset Card for NevIR: Negation in Neural Information Retrieval
## Dataset Description
- **Repository:** [https://github.com/orionw/NevIR](https://github.com/orionw/NevIR)
- **Paper:** [https://arxiv.org/abs/2212.10002](https://arxiv.org/abs/2212.10002)
- **Point of Contact:** [email protected]
## Dataset Summary
Data from the paper: ["NevIR: Negation in Neural Information Retrieval"](https://arxiv.org/abs/2305.07614).
If you use this dataset, we would appreciate you citing our work:
```
@inproceedings{weller-et-al-2023-nevir,
title={NevIR: Negation in Neural Information Retrieval},
author={Weller, Orion and Lawrie, Dawn, and Van Durme, Benjamin},
year={2023},
eprint={2305.07614},
archivePrefix={arXiv},
year={2023}
}
```
Please also consider citing the work that created the initial documents:
```
@inproceedings{ravichander-et-al-2022-condaqa,
title={CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation},
author={Ravichander, Abhilasha and Gardner, Matt and Marasovi\'{c}, Ana},
proceedings={EMNLP 2022},
year={2022}
}
```
From the paper: "Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on this theme: asking IR models to rank two documents that differ only by negation. We show that the results vary widely according to the type of IR architecture: cross-encoders perform best, followed by late-interaction models, and in last place are bi-encoder and sparse neural architectures. We find that most current information retrieval models do not consider negation, performing similarly or worse than randomly ranking.We show that although the obvious approach of continued fine-tuning on a dataset of contrastive documents containing negations increases performance (as does model size), there is still a large gap between machine and human performance."
### Supported Tasks and Leaderboards
The task is to rank each query in the pair correctly, where only one query is relevant to one document in the pair. There is no official leaderboard.
### Language
English
## Dataset Structure
### Data Instances
Here's an example instance:
```
{
"id": "1-2",
"WorkerId": 0,
"q1": "Which mayor did more vetoing than anticipated?",
"q2": "Which mayor did less vetoing than anticipated?",
"doc1": "In his first year as mayor, Medill received very little legislative resistance from the Chicago City Council. While he vetoed what was an unprecedented eleven City Council ordinances that year, most narrowly were involved with specific financial practices considered wasteful and none of the vetoes were overridden. He used his new powers to appoint the members of the newly constituted Chicago Board of Education and the commissioners of its constituted public library. His appointments were approved unanimously by the City Council.",
"doc2": "In his first year as mayor, Medill received very little legislative resistance from the Chicago City Council. While some expected an unprecedented number of vetoes, in actuality he only vetoed eleven City Council ordinances that year, and most of those were narrowly involved with specific financial practices he considered wasteful and none of the vetoes were overridden. He used his new powers to appoint the members of the newly constituted Chicago Board of Education and the commissioners of its constituted public library. His appointments were approved unanimously by the City Council."
}
```
### Data Fields
* `id`: unique ID for the pair, the first number indicates the document pair number in CondaQA and the second number indicates the PassageEditID in CondaQA.
* `WorkerId`: The ID for the Worker who created the queries for the pair.
* `q1`: the query that is only relevant to `doc1`
* `q2`: the query that is only relevant to `doc2`
* `doc1`: the original document, from CondaQA
* `doc2`: the edited document, from CondaQA
### Data Splits
Data splits can be accessed as:
```
from datasets import load_dataset
train_set = load_dataset("orionweller/nevir", "train")
dev_set = load_dataset("orionweller/nevir", "validation")
test_set = load_dataset("orionweller/nevir", "test")
```
## Dataset Creation
Full details are in the paper: https://arxiv.org/abs/2305.07614
|
orionweller/NevIR
|
[
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"negation",
"information_retrieval",
"IR",
"arxiv:2212.10002",
"arxiv:2305.07614",
"region:us"
] |
2023-05-12T18:40:48+00:00
|
{"language_creators": ["crowdsourced"], "language": ["en"], "license": "mit", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "pretty_name": "NevIR", "tags": ["negation", "information_retrieval", "IR"]}
|
2023-05-26T13:53:16+00:00
|
918dce81f05bb339db1b2bc2ebbd2d16594fb269
|
# Dataset Card for "DR_Grading"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sngsfydy/DR_Grading
|
[
"region:us"
] |
2023-05-12T19:17:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4"}}}}], "splits": [{"name": "train", "num_bytes": 261501746.0, "num_examples": 413}, {"name": "test", "num_bytes": 64805638.0, "num_examples": 103}], "download_size": 0, "dataset_size": 326307384.0}}
|
2023-05-13T05:19:33+00:00
|
bd8fd30cc9e92e4f30d97bc29299ece440c1419e
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
For this dataset, we selected literary texts in Russian that are closest in style and subject matter to real diary entries, giving priority to texts written in the first person and paying considerable attention to the inner state of the characters. By parsing popular Internet resources with retellings of literary works, we received briefings for each of the works selected in the previous step and supplemented the dataset.
### Supported Tasks and Leaderboards
[Summarization]
### Languages
[Russian]
## 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]
|
senyukhin/ru-ego-literature
|
[
"task_categories:summarization",
"size_categories:n<1K",
"language:ru",
"license:openrail",
"art",
"region:us"
] |
2023-05-12T20:05:41+00:00
|
{"language": ["ru"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["summarization"], "tags": ["art"], "viewer": true}
|
2023-06-25T08:42:11+00:00
|
b02965cb213359218fe5083750a3613e381d9599
|
# LICENSE
This is a dataset generated with the help of WizardLM. Therefore, the terms of use are restricted to research/academic only.
# What is this
This is a collection of .txt files with a prompt and the expected output. For instance:
```
#####PROMPT:
Question: Make sure the task is unique and adds value to the original list.
Thought:#####OUTPUT: I should check if the task is already in the list.
Action: Python REPL
Action Input:
if task not in tasks:
print("Task not found.")
else:
print("Task found.")
```
# What is it for
This is meant to help training LLama based models at using the Langchain ReAct tooling, specifically with the Python REPL.
# How good is it?
Not much, the dataset is quite dirty at the moment. Still fine-tuning the first LoRA, so no tests have been made.
# Next steps
1. Redo steps using a base model that has a more permissive license
2. Fix problems in the dataset generation phase, e.g.
* model tries to install packages and fail
* langchain agent tooling sometimes seem buggy and don't return the stdout correctly
* model likes to ask for user input
* model likes to exit the chain by calling sys.exit()
* once model gets stuck with installation steps, it's just an infinite loop
3. Clean dataset better
# How was it created
There are a f ew steps involved in the generation of this dataset.
1. created a mechanism to log pair of prompt/output generated by a running Langchain Agent on a local server
Server link: https://github.com/paolorechia/learn-langchain/blob/a3c288c43845d19692478f06757ed326c222f095/servers/vicuna_server.py#L39
```python
class PromptLogger:
_instances = {}
@staticmethod
def get(session):
if session not in PromptLogger._instances:
PromptLogger._instances[session] = PromptLogger(session)
return PromptLogger._instances[session]
def __init__(self, session) -> None:
self.input_step = 0
self.output_step = 0
self.session = session
self._dir = f"logged_prompts/session_{session}/"
try:
os.makedirs(self._dir)
except FileExistsError:
pass
def log(self, input_str, prefix="input"):
filename = os.path.join(self._dir, f"{prefix}_{self.input_step}")
with open(filename, "w") as fp:
if prefix == "input":
input_str = input_str.split("Now begin for real!\n")[1]
fp.write(input_str)
if prefix == "input":
self.input_step += 1
elif prefix == "output":
self.output_step += 1
else:
raise ValueError("Invalid prefix")
@app.post("/prompt")
def process_prompt(prompt_request: PromptRequest):
params = {
"prompt": prompt_request.prompt,
"temperature": prompt_request.temperature,
"max_new_tokens": prompt_request.max_new_tokens,
"stop": prompt_request.stop,
}
print("Received prompt: ", params["prompt"])
output = compute_until_stop(model, tokenizer, params, config.device)
print("Output: ", output)
if prompt_request.logging_session is not None:
prompt_logger = PromptLogger.get(prompt_request.logging_session)
prompt_logger.log(prompt_request.prompt, prefix="input")
prompt_logger.log(output, prefix="output")
return {"response": output}
```
2. created a short list of tasks and then extended it with the help of a LLM until about 390 tasks were generated
Script link: https://github.com/paolorechia/learn-langchain/blob/main/task_generation/generate_tasks.py
```python
from langchain_app.models.llama_http_llm import build_llama_base_llm
output = None
# Now let's test it out!
while True:
params = {"temperature": 1.3, "max_new_tokens": 1024, "stop": []}
llm = build_llama_base_llm(parameters=params)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
output = llm._call("""
You are given a list of tasks. Please extend it with new unique tasks:
1. "Print hello world to the terminal",
2. "Fetch a Chuck Norris joke from this endpoint https://api.chucknorris.io/jokes/random",
3. "Parse this HTML page https://api.chucknorris.io/ and find all the API endpoints ",
4. "Generate 10 unique cat jokes and store them in a CSV file with two columns, punch line and joke finisher",
5. "Connect to a Postgres database and return the existing databases names. Use the following credentials: \n\nhost localhost\nport 7036\nuser admin\npassword admin",
6. List the existing files in the current directory",
7. "Find out your existing working directory" ,
8. "Fix the syntax error of this code snippet:\ndef myfunc():\n\tprint(“hello",
9. "Find the keys of the JSON payload stored in the variable response_json",
10. "Extract the key called 'address' from the JSON stored in the variable json_ and store into a variable called address",
11. "Create a joke about AI bots and save it in a local text file",
12. "Create an unit test for the following snippet of code:\ndef sum_2(x, y):\n\treturn x + y",
13. "Create random data and plot it using matplotlib and store the result as a .PNG image",
14. "Download a CSV file about suicide from the webpage https://catalog.data.gov/dataset/?res_format=CSV and plot a bar chart comparing the suicide numbers of male vs ,female",
15. "Design a Todo list system. Write the explanation in a file called 'todo_list_system_design.txt'",
16. Search for the source code called 'example.py' in the directory, inspect the file, write unit tests for it and execute them to make sure everything is correct.",
17. "Write a data pipeline that ingests data from the Crime Data from 2020 to present from https://catalog.data.gov/dataset/?res_format=CSV. Use the requests and pandas, save the csv to the local disk. Create a directory if necessary, give an appropriate name"
""")
with open("generated_tasks.txt", "a") as fp:
fp.write(output)
```
The output can then be filtered with a simple bash script:
```bash
cat generated_tasks.txt | tr -s ' ' | grep -oE '\s*[0-9]+\.[A-Za-z, ]+[A-Za-z, ]+\.' | awk 'length >= 50' | sed -e 's/[0-9\. ]*//' > filtered_generated.txt
```
And then deduplicated with a few lines of code:
```python
import json
with open("filtered_generated.txt", "r") as fp:
tasks = fp.readlines()
with open("dedup_generated_tasks.json", "w") as fp:
json.dump(list(set(tasks)), fp, indent=4)
```
Result: https://github.com/paolorechia/learn-langchain/blob/main/task_generation/dedup_generated_tasks.json
3. used a prompted WizardLM 7b unquantized version to execute each task in the last, using the logger from item 1
https://github.com/paolorechia/learn-langchain/blob/main/langchain_app/agents/log_task_prompts_agent.py
```
from langchain.agents import Tool, initialize_agent, AgentType
from langchain.tools.python.tool import PythonAstREPLTool
from langchain_app.models.llama_http_llm import build_llama_base_llm
import json
prompt_template = """python
For instance:
Question: Find out how much 2 plus 2 is.
Thought: I must use the Python shell to calculate 2 + 2
Action: Python REPL
Action Input:
2 + 2
Observation: 4
Thought: I now know the answer
Final Answer: 4
Example 2:
Question: You have a variable age in your scope. If it's greater or equal than 21, say OK. Else, say Nay.
Thought: I should write an if/else block in the Python shell.
Action: Python REPL
Action Input:
if age >= 21:
print("OK") # this line has four spaces at the beginning
else:
print("Nay") # this line has four spaces at the beginning
Observation: OK
Thought: I have executed the task successfully.
Final Answer: I have executed the task successfully.
Example 3:
Question: Write and execute a script that sleeps for 2 seconds and prints 'Hello, World'
Thought: I should import the sleep function.
Action: Python REPL
Action Input:
from time import sleep
Observation:
Thought: I should call the sleep function passing 2 as parameter
Action: Python REPL
Action Input:
sleep(2)
Observation:
Thought: I should use the 'print' function to print 'Hello, World'
Action: Python REPL
Action Input:
print('Hello, World')
Observation:
Thought: I now finished the script
Final Answer: I executed the following script successfully:
from time import sleep
sleep(2)
print('Hello, World')
Additional Hints:
1. If an error thrown along the way, try to understand what happened and retry with a new code version that fixes the error.
2. DO NOT IGNORE ERRORS.
3. If an object does not have an attribute, call dir(object) to debug it.
4. SUPER IMPORTANT: ALWAYS respect the indentation in Python. Loops demand an idendentation. For example:
for i in range(10):
print(i) # this line has four spaces at the beginning
Same for ifs:
if True:
print("hello") # this line has four spaces at the beginning
An error be thrown because of the indentation, something like... "expected an indented block after 'for' statement on line..."
To fix, make sure to indent the lines!
5. Do not use \ in variable names, otherwise you'll see the syntax error "unexpected character after line continuation character..."
6. If the variable is not defined, use vars() to see the defined variables.
7. Do not repeat the same statement twice without a new reason.
8. NEVER print the HTML directly.
Now begin for real!
Question: {}
"""
offset = 0
with open("task_generation/dedup_generated_tasks.json", "r") as fp:
tasks = json.load(fp)
tasks = tasks[offset:]
for idx, task in enumerate(tasks):
params = {"temperature": 0, "max_new_tokens": 2048, "stop": ["Observation:"], "logging_session": f"medium_size_dataset{idx+offset}"}
llm = build_llama_base_llm(parameters=params)
python_tool = PythonAstREPLTool()
tools = [
Tool(
name="Python REPL",
func=python_tool,
description="useful for when you need to execute Python code",
),
]
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
first_task = tasks[idx]
try:
agent.run(prompt_template.format(first_task))
except Exception:
pass
```
5. extract all logs and consolidate into txt files inside a directory
```python
import os
dataset_folder = "medium_size_generated_tasks"
# -1 means no number of max_actions
max_actions_per_task = -1
if __name__ == "__main__":
try:
os.makedirs(dataset_folder)
except FileExistsError:
pass
dir_ = "logged_prompts/"
sessions = os.listdir(dir_)
datapoints = 0
for session in sessions:
session_dir = os.path.join(dir_, session)
logs_files = os.listdir(session_dir)
inputs_step_tuple = [log.split("_") for log in logs_files if "input" in log]
outputs_step_tuple = [log.split("_") for log in logs_files if "output" in log]
inputs_step_tuple.sort(key=lambda x: x[1])
outputs_step_tuple.sort(key=lambda x: x[1])
i = 0
for input_tuple, output_tuple in zip(inputs_step_tuple, outputs_step_tuple):
input_filename = input_tuple[0]+"_"+input_tuple[1]
output_filename = output_tuple[0]+"_"+output_tuple[1]
input_ = os.path.join(session_dir, input_filename)
output_ = os.path.join(session_dir, output_filename)
with open(input_, "r") as fp:
prompt = fp.read()
with open(output_, "r") as fp:
output = fp.read()
datapoint_filename = os.path.join(dataset_folder, f"{datapoints}.txt")
with open(datapoint_filename, "w") as fp:
fp.write(f"#####PROMPT: {prompt}")
fp.write(f"#####OUTPUT: {output}")
datapoints+=1
i += 1
if i == max_actions_per_task:
break
```
6. Use the dataset!
For instance, to convert it to JSON
```python
dataset_list = []
# dir_ = "easy_task_mini_dataset_cleaned"
dir_ = "medium_size_generated_tasks"
files_ = os.listdir(dir_)
for f in files_:
filename = os.path.join(dir_, f)
print(filename)
with open(filename, "r") as fp:
txt = fp.read()
prompt = txt.split("#####PROMPT:")[1].split("#####OUTPUT:")[0].strip()
output = txt.split("#####OUTPUT:")[1].strip()
dataset_list.append({
"prompt":prompt,
"output": output,
})
with open("data.json", "w") as fp:
json.dump(dataset_list, fp, indent=4)
```
You can also use my fork directly to train a LoRA:
https://github.com/paolorechia/vicuna-react-lora/blob/main/finetune_wizard_react.py
|
paolorechia/medium-size-generated-tasks
|
[
"size_categories:1K<n<10K",
"language:en",
"license:other",
"ReAct",
"LLM",
"Agent",
"langchain",
"region:us"
] |
2023-05-12T20:13:16+00:00
|
{"language": ["en"], "license": "other", "size_categories": ["1K<n<10K"], "tags": ["ReAct", "LLM", "Agent", "langchain"]}
|
2023-05-12T20:45:52+00:00
|
2a46ea4ded1f26faa61e49859164bfe03c720294
|
# Dataset Card for "oig_small_chip2_noncode"
From LAION's Open Instruction Generalist (OIG) dataset, we provide a subset whose samples are not code-related. OIG text elements are formatted as dialogue exerpts between a "human" and "bot" agent. The code generation prompt is parsed from the initial "human" agent's statement and the resultant response from the "bot" agent's statement. We then reformat the text/response pairs according to the format of the original Alpaca dataset; that is, instruction/input/output triplets.
The OIG dataset was prepared by LAION, and released under the Apache 2.0 license.
Numbers:
Prompts: 150453
Tokens: 11522004 using the EleutherAI/gpt-neox-20b tokenizer (counting instruction+input+output)
|
lucasmccabe-lmi/oig_small_chip2_noncode
|
[
"region:us"
] |
2023-05-12T20:21:50+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54969398.0, "num_examples": 150453}], "download_size": 34598598, "dataset_size": 54969398.0}}
|
2023-05-15T19:15:32+00:00
|
d298bbe730ce26f8b9fef84dcb67cfaed98ee962
|
# Dataset Card for "ar-tweets1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sanaeai/ar_tweets1
|
[
"region:us"
] |
2023-05-12T20:32:13+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7864711, "num_examples": 46452}], "download_size": 3336933, "dataset_size": 7864711}}
|
2023-05-12T20:32:16+00:00
|
6e32506b06d0418d4a7166e4284a24a8a470e8bf
|
# Dataset Card for "BilbaoQA"
## Dataset Description
- **Homepage:** https://github.com/TheMrguiller/MUCSI_Modal
- **Repository:** https://github.com/TheMrguiller/MUCSI_Modal
- **Paper:** It is a follow up of the Flamingo model paper
- **Leaderboard:**
- **Point of Contact:** https://github.com/TheMrguiller/MUCSI_Modal
### Dataset Summary
This dataset was collected for a proyect for a master degree in Computation and Intelligent System from University of Deusto. It was done by students and recolected from webpages famous in the Basque Country: Deia and Getimages. The questions and answers were created using a set of models that are able to generate this information from a description of a text.
### Supported Tasks and Leaderboards
The dataset is prepared to used it for visual question-answering.
### Languages
The dataset is in english.
## Dataset Structure
### Data Fields
- `image`: This field has the image, which is the context given to the model.
- `question`: This field incorporates the question that has to answer the model from the image context.
- `choices`: Multiple choice selection.
- `answer`: The answer from the multiple choice.
- `solution`: The chain of thought process of the solution selection.
- `CTH`: A flag that indicates whether it doesnt have chain of thought in that row.
### Data Splits
The dataset is split in 80% train and 20% test.
## Considerations for Using the Data
The dataset has some flaws regarding to the descriptions. The descriptions sometimes are to specific for a captioning task. There are also to many futbol match data, so it isnt to well balanced. There are also some description that are to generic. There are some repetition in the answers due to the bad quality of the descriptions, be aware of this.
## Additional Information
### Dataset Curators
The curators of this dataset where the students from the Masters degree in Computation and Inteligent Systems from University of Deusto.
|
TheMrguiller/BilbaoQA
|
[
"task_categories:question-answering",
"task_categories:visual-question-answering",
"size_categories:100B<n<1T",
"language:en",
"code",
"region:us"
] |
2023-05-12T20:48:21+00:00
|
{"language": ["en"], "size_categories": ["100B<n<1T"], "task_categories": ["question-answering", "visual-question-answering"], "dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "choices", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "CTH", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 1368875715, "num_examples": 3960}, {"name": "test", "num_bytes": 346986615, "num_examples": 990}], "download_size": 1709263149, "dataset_size": 1715862330}, "tags": ["code"]}
|
2023-08-24T10:48:31+00:00
|
48eab62ea15c3e2c3baa23b618ce5893f6e2e113
|
# Dataset Card for "Fashion_controlnet_dataset_V2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Abrumu/Fashion_controlnet_dataset_V2
|
[
"region:us"
] |
2023-05-12T20:59:41+00:00
|
{"dataset_info": {"features": [{"name": "target", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "control", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5104536210.928, "num_examples": 11647}], "download_size": 5149782695, "dataset_size": 5104536210.928}}
|
2023-05-12T21:07:49+00:00
|
d552a301552878e66f170a525d7aab9e44a04854
|
DanGlado/ddpm-butterflies-128
|
[
"license:other",
"region:us"
] |
2023-05-12T21:07:09+00:00
|
{"license": "other"}
|
2023-05-12T21:07:09+00:00
|
|
7737960779e1a2547d022f3e96d94f4cb40b54f4
|
# Dataset Card for Dataset: OktoberfestFoodDatasetPlus
## Dataset Description
- **Homepage: www.ilass.com**
- **Repository: https://github.com/ilassAG/OktoberfestFoodDataset**
- **Paper: https://arxiv.org/abs/1912.05007**
### Dataset Summary
This dataset comprises three categories: drinkServed, foodServed, person.
Part of it consists of real camera footage annotated by hand, while the rest is synthetically generated and annotated data.
A demo space is available to view results after training on the YOLO8 platform:
https://huggingface.co/spaces/ilass/yolov8_foodServed_drinkServed_Person
### Annotations
#### Annotation process
1000 images were annotated by hand.
1000 person images were sourced from COCO.
3000 images were synthetically produced and annotated.
|
ilass/OktoberfestFoodDatasetPlus
|
[
"task_categories:object-detection",
"size_categories:1K<n<10K",
"license:bsd",
"arxiv:1912.05007",
"region:us"
] |
2023-05-12T21:21:02+00:00
|
{"license": "bsd", "size_categories": ["1K<n<10K"], "task_categories": ["object-detection"]}
|
2023-05-28T12:14:54+00:00
|
f7c267f857feb7410d0af89273d68dfac1650c0a
|
# Dataset Card for "rest23_sentiment_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
javilonso/rest23_sentiment_data
|
[
"region:us"
] |
2023-05-12T21:53:31+00:00
|
{"dataset_info": {"features": [{"name": "Title", "dtype": "string"}, {"name": "Review", "dtype": "string"}, {"name": "Polarity", "dtype": "int64"}, {"name": "Country", "dtype": "int64"}, {"name": "Type", "dtype": "int64"}, {"name": "Title_Review", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 163527418.84820387, "num_examples": 201032}, {"name": "test", "num_bytes": 40882668.151796125, "num_examples": 50259}], "download_size": 127084046, "dataset_size": 204410087.0}}
|
2023-05-12T21:53:59+00:00
|
4ef381da248c0dcc147fad955ebb0147d7ebf1af
|
!pip install PyPDF2
import PyPDF2
archivo_pdf = open('/content/drive/MyDrive/FULL-Seminario/Proyectos/rac_gpt/notebooks/pruebas/https___www.aerocivil.gov.co_normatividad_RAC_RAC 1 - Definiciones.pdf', 'rb')
lector_pdf = PyPDF2.PdfReader(archivo_pdf)
contenido_texto = ""
for pagina in lector_pdf.pages:
contenido_texto += pagina.extract_text()
archivo_pdf.close()
archivo_texto = open('/content/drive/MyDrive/FULL-Seminario/Proyectos/rac_gpt/notebooks/pruebas/https___www.aerocivil.gov.co_normatividad_RAC_RAC 1 - Definiciones.txt', 'w')
archivo_texto.write(contenido_texto)
archivo_texto.close()
|
BlitzKriegM/prueba
|
[
"region:us"
] |
2023-05-12T22:14:48+00:00
|
{}
|
2023-05-13T00:33:53+00:00
|
c320b8d22a317bcb994207db937f1b60546cabcf
|
# Dataset Card for "c4_t5_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/c4_t5_test
|
[
"region:us"
] |
2023-05-12T22:29:13+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 263101800, "num_examples": 49270}, {"name": "validation", "num_bytes": 26283480, "num_examples": 4922}], "download_size": 121664633, "dataset_size": 289385280}}
|
2023-05-12T22:29:27+00:00
|
8a9d309da9157ad56c547a4f0d034f521acf0b7f
|
# Dataset Card for "gpt4all_code_small"
We provide a code-related subset of the original nomic-ai/gpt4all-j-prompt-generations (v1.2-jazzy revision) dataset, which represents 1) those records whose prompts were sourced from pacovaldez/stackoverflow-questions, 2) who explicitly mention one of Python, Java, C++, SQL, Kotlin, PHP, Swift, MATLAB, Typescript, Scala, HTML, CSS, Rust, or Perl, and 3) who include a code block in the response body.
Output records are responses from OpenAI’s GPT3.5-Turbo. Prompt/response pairs have been reformatted to fit the Alpaca format.
Numbers:
Prompts: 36856
Tokens: 38643696 using the EleutherAI/gpt-neox-20b tokenizer (counting instruction+input+output)
|
lucasmccabe-lmi/gpt4all_code_small
|
[
"region:us"
] |
2023-05-12T22:48:36+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 130633918.0, "num_examples": 36856}], "download_size": 62911071, "dataset_size": 130633918.0}}
|
2023-05-15T18:55:32+00:00
|
265589302460bc30d2e62f6ba7ab642ecfa87f4d
|
# Hypernerf Practice custom scenes Datasets
- hand
- chess
- laptop
- dvd
- tomato-mark
|
xieyizheng/hypernerf_custom_scenes
|
[
"region:us"
] |
2023-05-12T23:01:40+00:00
|
{}
|
2023-05-15T14:44:15+00:00
|
9377b6b84db5381c0b89c9536015acb7e0c3fffd
|
# Dataset Card for "c4_t5_10m"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/c4_t5_10m
|
[
"region:us"
] |
2023-05-12T23:49:46+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 54681600000, "num_examples": 10240000}, {"name": "validation", "num_bytes": 53400000, "num_examples": 10000}], "download_size": 22999280634, "dataset_size": 54735000000}}
|
2023-05-13T00:27:45+00:00
|
2391a430b8bb92b7cf0677a541a180a310497d4f
|
A collection of annotation files vision language datasets used in OpenFlamingo's [evaluation suite](https://github.com/mlfoundations/open_flamingo/tree/main/open_flamingo/eval).
|
openflamingo/eval_benchmark
|
[
"region:us"
] |
2023-05-13T00:04:10+00:00
|
{}
|
2023-08-10T04:56:18+00:00
|
f0e6e5c55e7ff97034fd9137f73c1fa88d88bf19
|
DamarJati/SD-Prompts
|
[
"language:en",
"license:cc0-1.0",
"region:us"
] |
2023-05-13T01:00:22+00:00
|
{"language": ["en"], "license": "cc0-1.0"}
|
2023-05-13T09:24:44+00:00
|
|
50654e71b5f2d5bb646d7a23e36f4b480b1d0576
|
# Dataset Card for "code-search-net-java"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/code-search-net-Java
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This dataset is the Java portion of the CodeSarchNet annotated with a summary column.
The code-search-net dataset includes open source functions that include comments found at GitHub.
The summary is a short description of what the function does.
### Languages
The dataset's comments are in English and the functions are coded in Java
### Data Splits
Train, test, validation labels are included in the dataset as a column.
## Dataset Creation
May of 2023
### Curation Rationale
This dataset can be used to generate instructional (or many other interesting) datasets that are useful to train LLMs
### Source Data
The CodeSearchNet dataset can be found at https://www.kaggle.com/datasets/omduggineni/codesearchnet
### Annotations
This datasets include a summary column including a short description of the function.
#### Annotation process
The annotation procedure was done using [Salesforce](https://huggingface.co/Salesforce) T5 summarization models.
A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython
The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. (some may still be present in the dataset)
### Licensing Information
Apache 2.0
|
Nan-Do/code-search-net-java
|
[
"task_categories:text2text-generation",
"task_categories:summarization",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"code",
"java",
"CodeSearchNet",
"summary",
"region:us"
] |
2023-05-13T01:03:07+00:00
|
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text2text-generation", "summarization", "text-generation"], "pretty_name": "Java CodeSearchNet with Summaries", "dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "original_string", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "code_tokens", "sequence": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "docstring_tokens", "sequence": "string"}, {"name": "sha", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1595060592, "num_examples": 495953}], "download_size": 440273784, "dataset_size": 1595060592}, "tags": ["code", "java", "CodeSearchNet", "summary"]}
|
2023-05-14T23:57:06+00:00
|
9c7a83f9102f310934ca84258d40a5c7d116fa46
|
# Dataset Card for Dataset Name
### Dataset Summary
This dataset is a subset of Kaggle's Google Landmark Recognition 2021 competition with only the categories with more than 500 images.
https://www.kaggle.com/competitions/landmark-recognition-2021/data
The dataset consists of a total of 45579 224x224 color images in 51 categories.
### Languages
English
## Dataset Structure
### Data Fields
- `landmark_id`: Int - Numeric identifier of the category
- `category` : String - Name of the category
- `id` : String - Image identifier
- `image` : Image - PIL image object
- `label` : Int - Numeric label from 0 to 50
### Data Splits
The dataset was randomly split with 80% of the images for the train set and 20% for the test set.
| | train | test |
|----------------------|------:|-----:|
| Dataset | 36463 | 9116 |
### Source Data
The full dataset is from Kaggle Landmark Recognition 2021
"Towards A Fairer Landmark Recognition Dataset", Z. Kim, A. Araujo, B. Cao, C. Askew, J. Sim, M. Green, N. Yilla and T. Weyand, arxiv:2108.08874
https://www.kaggle.com/competitions/landmark-recognition-2021/data
### Citation Information
"Google Landmarks Dataset v2 - A Large-Scale Benchmark for Instance-Level Recognition and Retrieval", T. Weyand, A. Araujo, B. Cao and J. Sim, Proc. CVPR'20
"Towards A Fairer Landmark Recognition Dataset", Z. Kim, A. Araujo, B. Cao, C. Askew, J. Sim, M. Green, N. Yilla and T. Weyand, arxiv:2108.08874
|
pemujo/GLDv2_Top_51_Categories
|
[
"size_categories:n<1K",
"language:en",
"region:us"
] |
2023-05-13T01:54:50+00:00
|
{"language": ["en"], "size_categories": ["n<1K"], "pretty_name": "GLDv2 Top 51 Categories", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "landmark_id", "dtype": "int64"}, {"name": "category", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2428986323.125, "num_examples": 36463}, {"name": "test", "num_bytes": 606874794.5, "num_examples": 9116}], "download_size": 3034360629, "dataset_size": 3035861117.625}}
|
2023-05-18T07:30:54+00:00
|
492d7a0f7cae416d20629233e57b3eb03e1b3a44
|
# Dataset Card for luotuo-QA-A
## Dataset Description
- **Homepage:** https://github.com/LC1332/Luotuo-Chinese-LLM
- **Repository:** https://github.com/LC1332/Luotuo-QA
- **Point of Contact:** [email protected]
### Dataset Summary
CoQA(Conversational Question Answering)数据集是一个用于对话式问答任务的大规模数据集，包含超过127,000个问题及其对应的答案。这些文本来自七个不同领域的段落：儿童故事、文学作品、中学和高中英语考试、新闻、维基百科、Reddit和Science。
CoQA数据集经过简单清洗，共有7012个story，我们在此基础上将整个数据集翻译成了中文并进行了增广，其中每个story中包含5个左右的问题，每个问题进行了5次增广。
由于此数据集是我们Luotuo-QA项目的一部分，我们将它叫做luotuo-QA-A,旨在促进对话式问答在中文语境下的研究和应用。
您可以在这里查看Luotuo-QA项目：https://github.com/LC1332/Luotuo-QA
此数据集适用于训练和评估中文对话式问答模型。有益于推动中文自然语言处理领域的发展，同时也为研究人员和开发者提供了一个基准，用于比较不同模型的性能和探索新的方法。
我们希望这一工作能够促进全球范围内中文语境对话式问答任务的研究和进一步的创新。
The CoQA (Conversational Question Answering) dataset is a large-scale dataset for conversational question answering tasks, consisting of over 127,000 questions and their corresponding answers. These texts are derived from passages in seven different domains: children's stories, literature, middle and high school English exams, news, Wikipedia, Reddit, and Science.
The CoQA dataset has undergone simple cleaning and consists of 7,012 stories. Building upon this dataset, we have translated the entire collection into Chinese and performed augmentation. Each story contains around 5 questions, and each question has been augmented 5 times.
As this dataset is part of our Luotuo-QA project, we name this dataset as luotuo-QA-A. It aims to facilitate research and applications of conversational question answering in the Chinese language context.
You can find our Luotuo-QA project here: https://github.com/LC1332/Luotuo-QA
This dataset is suitable for training and evaluating Chinese conversational question answering models. It contributes to the advancement of Chinese natural language processing and provides researchers and developers with a benchmark to compare the performance of different models and explore new approaches.
We hope that this work will foster research and further innovation in conversational question answering tasks in the Chinese language context on a global scale.
### Languages
CHINESE
### Data Instances
```
文本：长妈妈曾经讲给我一个故事听：先前，有一个读书人住在古庙里用功，晚间， 在院子里纳凉的时候，突然听到有人在叫他。答应着，四面看时，却见一个美女的 脸露在墙头上，向他一笑，隐去了。他很高兴；但竟给那走来夜谈的老和尚识破了 机关。说他脸上有些妖气，一定遇见“美女蛇”了；这是人首蛇身的怪物，能唤人 名，倘一答应，夜间便要来吃这人的肉的。他自然吓得要死，而那老和尚却道无妨 ，给他一个小盒子，说只要放在枕边，便可高枕而卧。他虽然照样办，却总是睡不 着，——当然睡不着的。到半夜，果然来了，沙沙沙！门外象是风雨声。他正抖作 一团时，却听得豁的一声，一道金光从枕边飞出，外面便什么声音也没有了，那金 光也就飞回来，敛在盒子里。后来呢？后来，老和尚说，这是飞蜈蚣，它能吸蛇的 脑髓，美女蛇就被它治死了。
原始问题为：谁遇到了美女蛇？
问题转义为:谁被美女蛇所困扰?
答案为:读书人
问题转义为:美女蛇袭击了谁?
答案为:读书人
原始问题为：谁杀了美女蛇
问题转义为:谁杀死了美女蛇
答案为:飞蜈蚣
```
### Licensing Information
我们的协议与CoQA数据集原始协议保持一致，请阅读以下内容。
CoQA数据集包含来自七个领域的段落。我们将其中五个领域的段落以以下许可证公开：
文学和维基百科段落遵循CC BY-SA 4.0许可证共享。
儿童故事选自MCTest，该数据集附带MSR-LA许可证。
中学/高中考试段落选自RACE，该数据集有自己的许可证。
新闻段落选自DeepMind CNN数据集，该数据集有Apache许可证。
Our licenses aligns with the original licenses of the CoQA dataset. Please refer to the following information.
CoQA contains passages from seven domains. It make five of these public under the following licenses.
We did translation and augmentation on the CoQA dataset. Therefore, the generated part of the data still complies with the original agreement of CoQA:
Literature and Wikipedia passages are shared under CC BY-SA 4.0 license.
Children's stories are collected from MCTest which comes with MSR-LA license.
Middle/High school exam passages are collected from RACE which comes with its own license.
News passages are collected from the DeepMind CNN dataset which comes with Apache license.
### Citation Information
如果您在项目中使用了我们的模型、代码或者数据，请引用我们。
Please cite us if you use the data or code in this repo.
```bibtex
@article{your-article,
title = {Your Article Title},
author = {Author Name},
journal = {Journal Name},
year = {2023},
volume = {X},
number = {X},
pages = {X-X},
doi = {DOI}
}
```
### Contributions
Thanks to @XXX, @XXXXXX, @XXXX, @XXXXXX, @XXXXXX, @XXX for adding this dataset.
|
Logic123456789/Test_Liscence
|
[
"task_categories:question-answering",
"language:zh",
"language:en",
"license:other",
"region:us"
] |
2023-05-13T01:54:52+00:00
|
{"language": ["zh", "en"], "license": "other", "task_categories": ["question-answering"], "extra_gated_prompt": "\u6211\u4eec\u7ffb\u8bd1\u4e86CoQA\u6570\u636e\u96c6\uff0c\u8bf7\u4ed4\u7ec6\u9605\u8bfb\u4ee5\u4e0b\u4fe1\u606f\u3002", "extra_gated_heading": "\u60a8\u9700\u8981\u63a5\u53d7\u534f\u8bae\u5e76\u63d0\u4ea4\u4fe1\u606f\u4ee5\u83b7\u53d6\u6b64\u6570\u636e\u96c6", "extra_gated_fields": {"\u59d3\u540d": "text", "\u90ae\u7bb1": "text", "\u6240\u5728\u7ec4\u7ec7": "text", "\u4f7f\u7528\u76ee\u7684": "text", "\u6211\u540c\u610f\u4ec5\u5c06\u6b64\u6570\u636e\u96c6\u7528\u4e8e\u975e\u5546\u4e1a\u7528\u9014": "checkbox"}, "extra_gated_button_content": "\u6211\u5df2\u9605\u8bfb\u534f\u8bae\u5e76\u540c\u610f\u63d0\u4f9b\u76f8\u5173\u4fe1\u606f"}
|
2023-05-15T05:51:49+00:00
|
9b37f3786ff36ed234dd2542fe006aead9afe696
|
salgadev/spirit-patterns
|
[
"task_categories:image-classification",
"size_categories:n<1K",
"language:en",
"license:lgpl",
"chemistry",
"spirits",
"beverages",
"region:us"
] |
2023-05-13T02:17:19+00:00
|
{"language": ["en"], "license": "lgpl", "size_categories": ["n<1K"], "task_categories": ["image-classification"], "pretty_name": "SpiritPatterns", "tags": ["chemistry", "spirits", "beverages"]}
|
2023-05-25T15:42:29+00:00
|
|
8166a81d2fb4f02007f30dee0c7705b5211f0b39
|
Jung/TPS_ESM3b_Vectors
|
[
"license:unknown",
"region:us"
] |
2023-05-13T02:25:50+00:00
|
{"license": "unknown", "dataset_info": {"features": [{"name": "A0A8F4SKT7", "dtype": "float32"}, {"name": "Q1XBU4", "dtype": "float32"}, {"name": "Q6Z5I0", "dtype": "float32"}, {"name": "E9E766", "dtype": "float32"}, {"name": "P55350", "dtype": "float32"}, {"name": "P53799", "dtype": "float32"}, {"name": "Q6TH92", "dtype": "float32"}, {"name": "Q5NP67", "dtype": "float32"}, {"name": "A0A0P0ZEM1", "dtype": "float32"}, {"name": "P9WEX9", "dtype": "float32"}, {"name": "F1CKI9", "dtype": "float32"}, {"name": "G9M5S6", "dtype": "float32"}, {"name": "Q9LUD9", "dtype": "float32"}, {"name": "O65323", "dtype": "float32"}, {"name": "P9WEP0", "dtype": "float32"}, {"name": "P13513", "dtype": "float32"}, {"name": "Q9SLW0", "dtype": "float32"}, {"name": "Q672V6", "dtype": "float32"}, {"name": "A0A3G9EY38", "dtype": "float32"}, {"name": "O66952", "dtype": "float32"}, {"name": "Q1ERD3", "dtype": "float32"}, {"name": "Q0E088", "dtype": "float32"}, {"name": "A0A3L6G998", "dtype": "float32"}, {"name": "E3W207", "dtype": "float32"}, {"name": "F2XFA8", "dtype": "float32"}, {"name": "A0A4S8MAF3", "dtype": "float32"}, {"name": "Q9LHR4", "dtype": "float32"}, {"name": "Q3IPL1", "dtype": "float32"}, {"name": "Q2XSC5", "dtype": "float32"}, {"name": "E3VWJ0", "dtype": "float32"}, {"name": "D2X8Y8", "dtype": "float32"}, {"name": "C0KWV5", "dtype": "float32"}, {"name": "I6QSN0", "dtype": "float32"}, {"name": "A0A1I9LTE4", "dtype": "float32"}, {"name": "A0A0E3D8P4", "dtype": "float32"}, {"name": "A0A140JWS2", "dtype": "float32"}, {"name": "A0A059PYD5", "dtype": "float32"}, {"name": "M4HYC6", "dtype": "float32"}, {"name": "E5GAG0", "dtype": "float32"}, {"name": "P84466", "dtype": "float32"}, {"name": "TmTC-1", "dtype": "float32"}, {"name": "Q45220", "dtype": "float32"}, {"name": "Q4JHN6", "dtype": "float32"}, {"name": "G8H5M8", "dtype": "float32"}, {"name": "Q6QZW8", "dtype": "float32"}, {"name": "Q08291", "dtype": "float32"}, {"name": "A0A0P0ZD79", "dtype": "float32"}, {"name": "R9QMW8", "dtype": "float32"}, {"name": "DgTC-2", "dtype": "float32"}, {"name": "Q9FQM1", "dtype": "float32"}, {"name": "P53800", "dtype": "float32"}, {"name": "J7LP58", "dtype": "float32"}, {"name": "R9WSX5", "dtype": "float32"}, {"name": "A0A6S6QR11", "dtype": "float32"}, {"name": "E2E2P1", "dtype": "float32"}, {"name": "G0Y7D1", "dtype": "float32"}, {"name": "J9QS25", "dtype": "float32"}, {"name": "Q10231", "dtype": "float32"}, {"name": "P0CV95", "dtype": "float32"}, {"name": "F8TWC9", "dtype": "float32"}, {"name": "M4HY05", "dtype": "float32"}, {"name": "Q75WN1", "dtype": "float32"}, {"name": "Q70EZ7", "dtype": "float32"}, {"name": "H6VLG5", "dtype": "float32"}, {"name": "P24322", "dtype": "float32"}, {"name": "Q9HGZ6", "dtype": "float32"}, {"name": "A0A2A2D8W5", "dtype": "float32"}, {"name": "A0A3G1DJL2", "dtype": "float32"}, {"name": "A0A1U8QHE3", "dtype": "float32"}, {"name": "M2V8C1", "dtype": "float32"}, {"name": "A0A1S5RW73", "dtype": "float32"}, {"name": "RmTC-1", "dtype": "float32"}, {"name": "A0A7S5L324", "dtype": "float32"}, {"name": "E2IUA7", "dtype": "float32"}, {"name": "Q675L5", "dtype": "float32"}, {"name": "P9WEV6", "dtype": "float32"}, {"name": "H6WZF2", "dtype": "float32"}, {"name": "P05369", "dtype": "float32"}, {"name": "A0A7L7SG75", "dtype": "float32"}, {"name": "U5N0S4", "dtype": "float32"}, {"name": "A0A1Z3GBK8", "dtype": "float32"}, {"name": "A0A482AJV9", "dtype": "float32"}, {"name": "G8H5M9", "dtype": "float32"}, {"name": "A0A2Z6FZ31", "dtype": "float32"}, {"name": "Q8L5K1", "dtype": "float32"}, {"name": "Q9XJ32", "dtype": "float32"}, {"name": "B6SCF6", "dtype": "float32"}, {"name": "A0A7S5L3H2", "dtype": "float32"}, {"name": "M5AW86", "dtype": "float32"}, {"name": "H2KWF1", "dtype": "float32"}, {"name": "A1C8C3", "dtype": "float32"}, {"name": "Q56RZ3", "dtype": "float32"}, {"name": "Q6BDZ9", "dtype": "float32"}, {"name": "A0A0B4EB91", "dtype": "float32"}, {"name": "G5CV43", "dtype": "float32"}, {"name": "E5GAG4", "dtype": "float32"}, {"name": "Q29VN2", "dtype": "float32"}, {"name": "Q2XPU7", "dtype": "float32"}, {"name": "A0A1V0QSA8", "dtype": "float32"}, {"name": "P9WEQ2", "dtype": "float32"}, {"name": "B6SCF5", "dtype": "float32"}, {"name": "E3WDE2", "dtype": "float32"}, {"name": "J7FIX8", "dtype": "float32"}, {"name": "R4YZC3", "dtype": "float32"}, {"name": "P14324", "dtype": "float32"}, {"name": "R4YVJ5", "dtype": "float32"}, {"name": "A0A142ZC57", "dtype": "float32"}, {"name": "Q9LIA0", "dtype": "float32"}, {"name": "A0A1L7U8F2", "dtype": "float32"}, {"name": "S0EA85", "dtype": "float32"}, {"name": "F9XLC1", "dtype": "float32"}, {"name": "V6RG22", "dtype": "float32"}, {"name": "O82140", "dtype": "float32"}, {"name": "B5GMG2", "dtype": "float32"}, {"name": "P80042", "dtype": "float32"}, {"name": "Q9UR08", "dtype": "float32"}, {"name": "P37271", "dtype": "float32"}, {"name": "A0A6P6W6H5", "dtype": "float32"}, {"name": "A0A7L7SCQ9", "dtype": "float32"}, {"name": "O81086", "dtype": "float32"}, {"name": "Q84NC9", "dtype": 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"dtype": "float32"}, {"name": "A0A135LYJ9", "dtype": "float32"}, {"name": "A1CVK0", "dtype": "float32"}, {"name": "Q93YA3", "dtype": "float32"}, {"name": "O04806", "dtype": "float32"}, {"name": "M4HZ33", "dtype": "float32"}, {"name": "P0DI77", "dtype": "float32"}, {"name": "Q71MJ3", "dtype": "float32"}, {"name": "Q675L6", "dtype": "float32"}, {"name": "R9QMW3", "dtype": "float32"}, {"name": "Q8H2B4", "dtype": "float32"}, {"name": "I2CM56", "dtype": "float32"}, {"name": "A0A5Q0QRJ3", "dtype": "float32"}, {"name": "Q94JS8", "dtype": "float32"}, {"name": "A0A1Z3GC64", "dtype": "float32"}, {"name": "P95999", "dtype": "float32"}, {"name": "A0A177DNJ5", "dtype": "float32"}, {"name": "P0DI76", "dtype": "float32"}, {"name": "Q49SP6", "dtype": "float32"}, {"name": "H2VFR7", "dtype": "float32"}, {"name": "O81191", "dtype": "float32"}, {"name": "P49085", "dtype": "float32"}, {"name": "Q5SBP4", "dtype": "float32"}, {"name": "A0A6M6CCF6", "dtype": "float32"}, {"name": "P9WKH1", "dtype": "float32"}, {"name": "I1ZHA5", "dtype": "float32"}, {"name": "G8H5N2", "dtype": "float32"}, {"name": "E4V6I8", "dtype": "float32"}, {"name": "A0A6C0QES1", "dtype": "float32"}, {"name": "Q6ET36", "dtype": "float32"}, {"name": "A0A343W969", "dtype": "float32"}, {"name": "Q6BE25", "dtype": "float32"}, {"name": "A0A0H5BB10", "dtype": "float32"}, {"name": "R9QMW5", "dtype": "float32"}, {"name": "Q8W3Z0", "dtype": "float32"}, {"name": "O65435", "dtype": "float32"}, {"name": "F2XFA6", "dtype": "float32"}, {"name": "Q6E7D7", "dtype": "float32"}, {"name": "Q9ACU1", "dtype": "float32"}, {"name": "Q8L5J7", "dtype": "float32"}, {"name": "Q93X23", "dtype": "float32"}, {"name": "F1CKI8", "dtype": "float32"}, {"name": "Q5SBP2", "dtype": "float32"}, {"name": "R4I3I0", "dtype": "float32"}, {"name": "B1B1U3", "dtype": "float32"}, {"name": "Q9FR95", "dtype": "float32"}, {"name": "A0A4Y5QVX6", "dtype": "float32"}, {"name": "A0A6C0TL59", "dtype": "float32"}, {"name": "D8RNZ9", "dtype": "float32"}, {"name": "SptA", "dtype": "float32"}, {"name": "P78589", "dtype": "float32"}, {"name": "Q5UB07", "dtype": "float32"}, {"name": "A0A3G9EAS3", "dtype": "float32"}, {"name": "A0A6B8MS60", "dtype": "float32"}, {"name": "G5CV48", "dtype": "float32"}, {"name": "D4IIJ0", "dtype": "float32"}, {"name": "Q0JF02", "dtype": "float32"}, {"name": "Q7LJR6", "dtype": "float32"}, {"name": "A4FVP2", "dtype": "float32"}, {"name": "A8C980", "dtype": "float32"}, {"name": "Q9K499", "dtype": "float32"}, {"name": "F2XFA5", "dtype": "float32"}, {"name": "A7IZZ2", "dtype": "float32"}, {"name": "Q764T8", "dtype": "float32"}, {"name": "D2X8G0", "dtype": "float32"}, {"name": "Q9AR86", "dtype": "float32"}, {"name": "O24474", "dtype": "float32"}, {"name": "R9QMR3", "dtype": "float32"}, {"name": "G1DGI7", "dtype": "float32"}, {"name": "B2DBF1", "dtype": "float32"}, {"name": "F2XFA1", "dtype": "float32"}, {"name": "A0A1P8AVI0", "dtype": "float32"}, {"name": "Q9FI37", "dtype": "float32"}, {"name": "Q9SPN1", "dtype": "float32"}, {"name": "A0A2Z6E967", "dtype": "float32"}, {"name": "E7DN63", "dtype": "float32"}, {"name": "Q8L5K4", "dtype": "float32"}, {"name": "O82139", "dtype": "float32"}, {"name": "Q6ZH94", "dtype": "float32"}, {"name": "A0A0U4CDK4", "dtype": "float32"}, {"name": "Q9FV72", "dtype": "float32"}, {"name": "P0DL13", "dtype": "float32"}, {"name": "B0Y565", "dtype": "float32"}, {"name": "A0A1W6GW32", "dtype": "float32"}, {"name": "A0A076GAU9", "dtype": "float32"}, {"name": "A0A142BX74", "dtype": "float32"}, {"name": "D8RLD3", "dtype": "float32"}, {"name": "A0A1Z3GCD1", "dtype": "float32"}, {"name": "A0A290U6P6", "dtype": "float32"}, {"name": "C7E5V9", "dtype": "float32"}, {"name": "Q32W37", "dtype": "float32"}, {"name": "Q55012", "dtype": "float32"}, {"name": "A0A167V661", "dtype": "float32"}, {"name": "Q8VWY4", "dtype": "float32"}, {"name": "Q6Z5J6", "dtype": "float32"}, {"name": "Q8W3Z3", "dtype": "float32"}, {"name": "A1JH12", "dtype": "float32"}, {"name": "A0A142BX71", "dtype": "float32"}, {"name": "G8GJ96", "dtype": "float32"}, {"name": "Q675L0", "dtype": "float32"}, {"name": "R9QMQ9", "dtype": "float32"}, {"name": "A0A0H4U9R8", "dtype": "float32"}, {"name": "Q5SBP6", "dtype": "float32"}, {"name": "W6Q4Q9", "dtype": "float32"}, {"name": "A0A8F4PNJ7", "dtype": "float32"}, {"name": "B4YYR2", "dtype": "float32"}, {"name": "G2P5T1", "dtype": "float32"}, {"name": "A0A482IC14", "dtype": "float32"}, {"name": "Q9LUE0", "dtype": "float32"}, {"name": "R9UPX9", "dtype": "float32"}, {"name": "P93665", "dtype": "float32"}, {"name": "A0A290U6M0", "dtype": "float32"}, {"name": "H9C6R1", "dtype": "float32"}, {"name": "B5H7H3", "dtype": "float32"}, {"name": "Q8K9A0", "dtype": "float32"}, {"name": "Q9P885", "dtype": "float32"}, {"name": "Q0JEZ8", "dtype": "float32"}, {"name": "A0A1V0E492", "dtype": "float32"}, {"name": "Q40577", "dtype": "float32"}, {"name": "P49352", "dtype": "float32"}, {"name": "A8R7G3", "dtype": "float32"}, {"name": "C3RSF5", "dtype": "float32"}, {"name": "Q9LUE2", "dtype": "float32"}, {"name": "O22340", "dtype": "float32"}, {"name": "O06728", "dtype": "float32"}, {"name": "Q94G53", "dtype": "float32"}, {"name": "F1CKJ1", "dtype": "float32"}, {"name": "F0ZL92", "dtype": "float32"}, {"name": "A0A8F4SK83", "dtype": "float32"}, {"name": "A0A0E3KJK7", "dtype": "float32"}, {"name": "P27679", "dtype": "float32"}, {"name": "A0A0S2IHL6", "dtype": "float32"}, {"name": "Q9LRH8", "dtype": "float32"}, {"name": "GenBank.WDE20677.1", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 11048960, "num_examples": 2560}], "download_size": 11876226, "dataset_size": 11048960}}
|
2023-05-13T02:59:53+00:00
|
|
d2ae03d04f768d0198c75229d12f7c61192874ba
|
yyu/review_corpus
|
[
"license:mit",
"region:us"
] |
2023-05-13T02:47:28+00:00
|
{"license": "mit"}
|
2023-05-13T23:36:21+00:00
|
|
b8b331401cb895a781c96726be93c9c529fe7ef2
|
yyu/wiki_corpus
|
[
"license:mit",
"region:us"
] |
2023-05-13T02:48:31+00:00
|
{"license": "mit"}
|
2023-05-13T23:17:30+00:00
|
|
235d5cb4b7c19ff436513c90acd6ba7681d6e2a3
|
yyu/news_corpus
|
[
"license:mit",
"region:us"
] |
2023-05-13T02:48:47+00:00
|
{"license": "mit"}
|
2023-05-13T05:14:55+00:00
|
|
57191aed07626d16eb0a83f5ee30e7d0d60818bd
|
HMinions/moosshuju
|
[
"license:afl-3.0",
"region:us"
] |
2023-05-13T03:35:29+00:00
|
{"license": "afl-3.0"}
|
2023-05-13T03:35:29+00:00
|
|
eaaa6cc6c8dcdefba3d29c04e8434ff9524842b7
|
# Dataset Card for "UN_PDF_SUBSET_PREPROCESSED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bot-yaya/UN_PDF_SUBSET_PREPROCESSED
|
[
"region:us"
] |
2023-05-13T04:10:37+00:00
|
{"dataset_info": {"features": [{"name": "zh", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "fr", "dtype": "string"}, {"name": "es", "dtype": "string"}, {"name": "ru", "dtype": "string"}, {"name": "record", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 589332110, "num_examples": 2950}], "download_size": 279887483, "dataset_size": 589332110}}
|
2023-05-13T04:14:22+00:00
|
629321e6db45881c0cbe021d62e9dba991b702e4
|
# 25K Unsplash Images for Search
This is a derivative work based on two existing datasets.
- `images.csv` metadata from [Unsplash](https://github.com/unsplash/datasets), sorted and converted to CSV.
- `images/` in 250x250 resolution by [kaggle/@jettchentt](https://www.kaggle.com/datasets/jettchentt/unsplash-dataset-images-downloaded-250x250).
- `images.fbin` is a binary file with UForm image embeddings.
- `images.usearch` is a binary file with a serialized USearch index.
The original `images.tsv` from Unsplash has been filtered to avoid missing images.
The embeddings and the index can be reconstructed with the `main.py` script.
On the Apple M2 Pro CPU:
- Image vectorization takes 100ms/image, or 10 inferences/second.
- Indexing vectors one-by-one happens at 700 vectors/second speed.
To rebuild the indexes:
```sh
./main.py
```
|
unum-cloud/ann-unsplash-25k
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-13T04:25:41+00:00
|
{"license": "apache-2.0"}
|
2023-08-16T09:29:10+00:00
|
6e929995592a22240193847adf07cbbeb43e8ad9
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_T_A_C_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_T_A_C_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-05-13T04:26:46+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 1142226, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 1123889, "num_examples": 1000}], "download_size": 403394, "dataset_size": 2266115}}
|
2023-05-13T04:36:10+00:00
|
1d9d063c245eb3543eeb462d07f236ae1d6e0c12
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-05-13T04:33:54+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 9989438, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_text", "num_bytes": 9985757, "num_examples": 1000}], "download_size": 3297008, "dataset_size": 19975195}}
|
2023-05-13T04:43:12+00:00
|
f10d3e4fbe6b76f3b0d9a3c193fd133b4ba5708a
|
In dieser Datenbank werden alle AWMF Leitlinien hinterlegt, die die Deutsche Gesellschaft für Orthopädie und Unfallchirurgie (DGOU) erstellt hat.
|
Nille1991/Leitliniendatenbank
|
[
"size_categories:n<1K",
"language:de",
"license:bigscience-openrail-m",
"region:us"
] |
2023-05-13T05:22:34+00:00
|
{"language": ["de"], "license": "bigscience-openrail-m", "size_categories": ["n<1K"]}
|
2023-05-13T08:54:45+00:00
|
3b230ce6b0821ac265b77e50d776a039ec617060
|
# Dataset Card for "wikipedia_en_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/wikipedia_en_tokenized
|
[
"region:us"
] |
2023-05-13T05:24:14+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "targets", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 56050416752, "num_examples": 9110926}], "download_size": 19330142269, "dataset_size": 56050416752}}
|
2023-05-13T05:45:00+00:00
|
30a49ad01f7b6492ceaafcb07e4d5e87c637e911
|
# Dataset Card for "Downsampled_imbd_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Arthuerwang/Downsampled_imbd_dataset
|
[
"region:us"
] |
2023-05-13T05:44:54+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 16760000.0, "num_examples": 10000}, {"name": "test", "num_bytes": 1676000.0, "num_examples": 1000}], "download_size": 0, "dataset_size": 18436000.0}}
|
2023-05-13T06:22:52+00:00
|
8475468c46ef257a6cf4a0e0ed68e8671d097b26
|
biu-nlp/QAmden-pretraining
|
[
"license:apache-2.0",
"region:us"
] |
2023-05-13T06:09:35+00:00
|
{"license": "apache-2.0"}
|
2023-05-13T07:39:02+00:00
|
|
902fd667a2e2532736eb7fb9adae60e74e4fdd79
|
# Dataset Card for "wikipedia_en_tokenized_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/wikipedia_en_tokenized_1
|
[
"region:us"
] |
2023-05-13T06:16:38+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "targets", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 30760000000, "num_examples": 5000000}], "download_size": 10631130515, "dataset_size": 30760000000}}
|
2023-05-13T06:27:47+00:00
|
da64e75aae6d50da339b6d783d6d00b1c1d334ff
|
# Dataset Card for "wikipedia_en_tokenized_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andersonbcdefg/wikipedia_en_tokenized_2
|
[
"region:us"
] |
2023-05-13T06:28:10+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "targets", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 25290416752, "num_examples": 4110926}], "download_size": 8697978435, "dataset_size": 25290416752}}
|
2023-05-13T06:37:55+00:00
|
5b20630fc5fe08d8bd829e7f66c8a217becc8e72
|
This is a interleaved wiki dataset.
Processed from wiki articles dump files:
ja: jawiki-20230301-pages-articles-multistream1.xml-p1p114794
With following format in parquet file
columns: 'xml', 'markdown', 'html', 'pairs'
example:
- 'xml':
- [[ 新潮社\u3000Foresight(フォーサイト)}}</ref>。\ ... [[シテ島]]のこと）とも呼ばれていた。\n\n== 地理 ==\n{{clear}}\n'],
- [ ###img#0### ],
- [''thumb|none|220px|フランスの道路の原点を象徴する ... 一級の収蔵物が並ぶ。\n\n→[[#主な観光名所]]\n\n=== 主な観光名所 ===\n',
- [ ###img#1### ],
- ...]
- 'markdown': [...]
- 'html': [...]
- 'pairs':
- [['###img#0###', 'https://upload.wikimedia.org/wikipedia/commons/e/e7/Revenus_%C3%A0_Paris_et_Petite_Couronne.JPG'],
- [['###img#1###', 'https://upload.wikimedia.org/wikipedia/commons/c/c1/Bois_de_Boulogne_%2880%29.jpg'],
- ...]
|
lfsm/wiki_interleave
|
[
"region:us"
] |
2023-05-13T06:38:19+00:00
|
{}
|
2023-05-13T06:58:23+00:00
|
7ec8c33ec0508e7cf177f3be09c5c0f6981cbed6
|
amongglue/youtube_subtitles
|
[
"license:mit",
"region:us"
] |
2023-05-13T06:52:20+00:00
|
{"license": "mit"}
|
2023-05-13T07:22:03+00:00
|
|
4d5f2541018055c9b87b7be016804cba79a27e79
|
# Dataset Card for "HaeRae_Bench"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
amphora/haerae_bench
|
[
"region:us"
] |
2023-05-13T06:52:21+00:00
|
{"configs": [{"config_name": "General Knowledge", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-KGK.csv"}]}, {"config_name": "History", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-HI.csv"}]}, {"config_name": "Loan Words", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-LW.csv"}]}, {"config_name": "Reading Comprehension", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-RC.csv"}]}, {"config_name": "Rare Words", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-RW.csv"}]}, {"config_name": "Standard Nomenclature", "data_files": [{"split": "test", "path": "data/HAERAE-Bench-v1-SN.csv"}]}]}
|
2023-12-20T08:36:58+00:00
|
52ea05e173105514a01eed7cd8677293da200552
|
It is a simple dataset of 64 screenshots from https://fancaps.net/
example: 
|
airest/kyoshi
|
[
"license:openrail",
"art",
"region:us"
] |
2023-05-13T06:54:37+00:00
|
{"license": "openrail", "tags": ["art"]}
|
2023-05-13T07:33:50+00:00
|
51e9a1a31d4c65c5dd627191bb74b5144efc6870
|
# MInDS-14
## Dataset Description
- **Fine-Tuning script:** [pytorch/audio-classification](https://github.com/huggingface/transformers/tree/main/examples/pytorch/audio-classification)
- **Paper:** [Multilingual and Cross-Lingual Intent Detection from Spoken Data](https://arxiv.org/abs/2104.08524)
- **Total amount of disk used:** ca. 500 MB
MINDS-14 is training and evaluation resource for intent detection task with spoken data. It covers 14
intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties.
## Example
MInDS-14 can be downloaded and used as follows:
```py
from datasets import load_dataset
minds_14 = load_dataset("PolyAI/minds14", "fr-FR") # for French
# to download all data for multi-lingual fine-tuning uncomment following line
# minds_14 = load_dataset("PolyAI/all", "all")
# see structure
print(minds_14)
# load audio sample on the fly
audio_input = minds_14["train"][0]["audio"] # first decoded audio sample
intent_class = minds_14["train"][0]["intent_class"] # first transcription
intent = minds_14["train"].features["intent_class"].names[intent_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
## Dataset Structure
We show detailed information the example configurations `fr-FR` of the dataset.
All other configurations have the same structure.
### Data Instances
**fr-FR**
- Size of downloaded dataset files: 471 MB
- Size of the generated dataset: 300 KB
- Total amount of disk used: 471 MB
An example of a datainstance of the config `fr-FR` looks as follows:
```
{
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"audio": {
"path": "/home/patrick/.cache/huggingface/datasets/downloads/extracted/3ebe2265b2f102203be5e64fa8e533e0c6742e72268772c8ac1834c5a1a921e3/fr-FR~ADDRESS/response_4.wav",
"array": array(
[0.0, 0.0, 0.0, ..., 0.0, 0.00048828, -0.00024414], dtype=float32
),
"sampling_rate": 8000,
},
"transcription": "je souhaite changer mon adresse",
"english_transcription": "I want to change my address",
"intent_class": 1,
"lang_id": 6,
}
```
### Data Fields
The data fields are the same among all splits.
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **transcription** (str): Transcription of the audio file
- **english_transcription** (str): English transcription of the audio file
- **intent_class** (int): Class id of intent
- **lang_id** (int): Id of language
### Data Splits
Every config only has the `"train"` split containing of *ca.* 600 examples.
## Dataset Creation
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
```
@article{DBLP:journals/corr/abs-2104-08524,
author = {Daniela Gerz and
Pei{-}Hao Su and
Razvan Kusztos and
Avishek Mondal and
Michal Lis and
Eshan Singhal and
Nikola Mrksic and
Tsung{-}Hsien Wen and
Ivan Vulic},
title = {Multilingual and Cross-Lingual Intent Detection from Spoken Data},
journal = {CoRR},
volume = {abs/2104.08524},
year = {2021},
url = {https://arxiv.org/abs/2104.08524},
eprinttype = {arXiv},
eprint = {2104.08524},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08524.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset
|
a6kme/minds14-mirror
|
[
"task_categories:automatic-speech-recognition",
"task_ids:keyword-spotting",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:en",
"language:fr",
"language:it",
"language:es",
"language:pt",
"language:de",
"language:nl",
"language:ru",
"language:pl",
"language:cs",
"language:ko",
"language:zh",
"license:cc-by-4.0",
"arxiv:2104.08524",
"region:us"
] |
2023-05-13T06:56:01+00:00
|
{"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["en", "fr", "it", "es", "pt", "de", "nl", "ru", "pl", "cs", "ko", "zh"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "task_categories": ["automatic-speech-recognition", "speech-processing"], "task_ids": ["speech-recognition", "keyword-spotting"], "pretty_name": "MInDS-14", "language_bcp47": ["en", "en-GB", "en-US", "en-AU", "fr", "it", "es", "pt", "de", "nl", "ru", "pl", "cs", "ko", "zh"]}
|
2023-05-13T10:42:15+00:00
|
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