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0eeb3025db29bfa69a9a5ab177252ee5ecab35c0 | proteinea/nearest_neighbor_search | [
"license:mit",
"doi:10.57967/hf/1106",
"region:us"
]
| 2022-12-12T16:26:59+00:00 | {"license": "mit"} | 2022-12-12T16:28:52+00:00 |
|
427334845f2c695958827e2144beb1d41ad8b3e8 | # Dataset Card for "frames"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shipmaal/frames | [
"region:us"
]
| 2022-12-12T18:17:19+00:00 | {"dataset_info": {"features": [{"name": "spectral_frame", "sequence": "float32"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 905704193.018, "num_examples": 18958}], "download_size": 752493076, "dataset_size": 905704193.018}} | 2022-12-12T18:17:56+00:00 |
243586554c3f2dcc4081dd612f3d02c518b04ee1 | # Dataset Card for "pl-text-images-5000-whole"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zombely/pl-text-images-5000-whole | [
"region:us"
]
| 2022-12-12T18:44:04+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2538550896.71, "num_examples": 4046}, {"name": "test", "num_bytes": 309157048.0, "num_examples": 472}, {"name": "validation", "num_bytes": 311530101.0, "num_examples": 482}], "download_size": 3161688642, "dataset_size": 3159238045.71}} | 2022-12-12T18:49:45+00:00 |
c0a9de1d1bd5e2fb110a224910a9dda4600320ed |
# Grug Dataset
This is content pulled from various archives to create a "grugbot" or sorts using GPT-J.
Really, just a dumb joke I made with some friends. | DarwinAnim8or/grug | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"language:en",
"license:unknown",
"grug",
"internet",
"greentext",
"region:us"
]
| 2022-12-12T19:02:59+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": [], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Grug Dataset\n\nThis is content pulled from various archives to create a \"grugbot\" or sorts using GPT-J. ", "tags": ["grug", "internet", "greentext"]} | 2023-07-25T20:00:48+00:00 |
21e4e142159e2153706c23a3a02e55cec5591cea |
# Objaverse
Objaverse is a Massive Dataset with 800K+ Annotated 3D Objects.
More documentation is coming soon. In the meantime, please see our [paper](https://arxiv.org/abs/2212.08051) and [website](https://objaverse.allenai.org/) for additional details.
# License
The use of the dataset as a whole is licensed under the [ODC-By v1.0](https://opendatacommons.org/licenses/by/1-0/) license. Individual objects in Objaverse are all licensed as creative commons distributable objects, and may be under the following licenses:
- [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/) - 721K objects
- [CC-BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) - 25K objects
- [CC-BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) - 52K objects
- [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) - 16K objects
- [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/) - 3.5K objects
The metadata will provide the license for each object.
# Citation
To cite Objaverse, please use the following BibTeX entry:
```bibtex
@article{objaverse,
title={Objaverse: A Universe of Annotated 3D Objects},
author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs and
Oscar Michel and Eli VanderBilt and Ludwig Schmidt and
Kiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},
journal={arXiv preprint arXiv:2212.08051},
year={2022}
}
``` | allenai/objaverse | [
"language:en",
"license:odc-by",
"arxiv:2212.08051",
"region:us"
]
| 2022-12-12T19:06:33+00:00 | {"language": ["en"], "license": "odc-by", "viewer": false} | 2023-03-31T10:05:57+00:00 |
3ffcbe4d03eefcacfbb24640a6fbd0339ea9aa65 | Jamal811/10-BBC | [
"license:other",
"region:us"
]
| 2022-12-12T19:46:54+00:00 | {"license": "other"} | 2022-12-12T19:48:48+00:00 |
|
26425380ff55b935d72876eaabafd7aa8b08d722 | Vavavoom/Datafreezer2008 | [
"license:gpl-3.0",
"region:us"
]
| 2022-12-12T19:55:53+00:00 | {"license": "gpl-3.0"} | 2022-12-12T19:55:53+00:00 |
|
f71eb7a326d8225e21fce7e7923795079ef40e34 | # Dataset Card for "tib_transcripts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gigant/tib_transcripts | [
"region:us"
]
| 2022-12-12T21:18:55+00:00 | {"dataset_info": {"features": [{"name": "doi", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "abstract", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 251058543, "num_examples": 8481}], "download_size": 130991914, "dataset_size": 251058543}} | 2023-01-21T13:54:23+00:00 |
bba16fd936413689429850d2cc3c60fcd6443edd |
Census dataset
| HuggingFaceTamil/census | [
"license:openrail",
"region:us"
]
| 2022-12-12T21:22:35+00:00 | {"license": "openrail"} | 2022-12-12T21:29:43+00:00 |
5b5da1e9ff02c41a6a9300da1f227fabd6c41410 | An Inspirations images library for use with the Automatic1111 webui Inspirations extension.
Made with Elysium anime V2 for impact on anime-themed images.
Extract to `stable-diffusion-webui-inspiration\inspiration` folder
Inspirations Extension - https://github.com/yfszzx/stable-diffusion-webui-inspiration
Elysium Model - https://huggingface.co/hesw23168/SD-Elysium-Model | EfaceD/ElysiumInspirations | [
"region:us"
]
| 2022-12-12T21:27:03+00:00 | {} | 2022-12-14T07:10:26+00:00 |
86c17541f78440e5ff2ef55740a3a503b2716d3a | # Dataset Card for "lexFridmanPodcast-transcript-audio"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
### Dataset Summary
This dataset is created by applying whisper to the videos of the Youtube channel [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman). The dataset was created a medium size whisper model.
### Languages
- **Language**: English
## Dataset Structure
The dataset contains all the transcripts plus the audio of the different videos of Lex Fridman Podcast.
### Data Fields
The dataset is composed by:
- **id**: Id of the youtube video.
- **channel**: Name of the channel.
- **channel\_id**: Id of the youtube channel.
- **title**: Title given to the video.
- **categories**: Category of the video.
- **description**: Description added by the author.
- **text**: Whole transcript of the video.
- **segments**: A list with the time and transcription of the video.
- **start**: When started the trancription.
- **end**: When the transcription ends.
- **text**: The text of the transcription.
- **audio**: the extracted audio of the video in ogg format.
### Data Splits
- Train split.
## Dataset Creation
### Source Data
The transcriptions are from the videos of [Lex Fridman Podcast](https://www.youtube.com/watch?v=FhfmGM6hswI&list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4&ab_channel=LexFridman)
### Contributions
Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset. | Whispering-GPT/lex-fridman-podcast-transcript-audio | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"medium",
"region:us"
]
| 2022-12-12T22:36:18+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "channel", "dtype": "string"}, {"name": "channel_id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "categories", "sequence": "string"}, {"name": "tags", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "segments", "list": [{"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}, {"name": "text", "dtype": "string"}]}, {"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 65356108140.0, "num_examples": 333}], "download_size": 64386861854, "dataset_size": 65356108140.0}, "tags": ["whisper", "whispering", "medium"]} | 2022-12-18T17:32:28+00:00 |
378d1517374931acddb0cf5faadd73c678871ec8 | # Dataset Card for "pororo_storyviz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dhruvrnaik/pororo_storyviz | [
"region:us"
]
| 2022-12-12T23:00:18+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "followings", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 273488067.011, "num_examples": 10191}, {"name": "test", "num_bytes": 62374283.312, "num_examples": 2208}, {"name": "validation", "num_bytes": 64563192.696, "num_examples": 2334}], "download_size": 410950911, "dataset_size": 400425543.01899993}} | 2022-12-12T23:00:51+00:00 |
7ed97aa2f711010d471687e9881ae91a8f5ea341 | # Dataset Card for "wikipedia_august_october_diff"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tristan/wikipedia_august_october_diff | [
"region:us"
]
| 2022-12-12T23:35:44+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "crawl_timestamp", "dtype": "int64"}, {"name": "reward", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17185592921.0, "num_examples": 2831474}], "download_size": 10059329653, "dataset_size": 17185592921.0}} | 2022-12-12T23:42:06+00:00 |
1e0a7706867a52ec2df1a541af7ea5658cd35cfb | kmeng/km | [
"license:openrail",
"region:us"
]
| 2022-12-13T00:00:14+00:00 | {"license": "openrail"} | 2022-12-13T00:00:30+00:00 |
|
4f41f921561483d9e4272e3e2073d8300ca2abea | # Dataset Card for "wikipedia-august-october-line-diff-1000-char-threshold"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tristan/wikipedia-august-october-line-diff-1000-char-threshold | [
"region:us"
]
| 2022-12-13T01:36:49+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "crawl_timestamp", "dtype": "int64"}, {"name": "reward", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 403299007, "num_examples": 285657}], "download_size": 161874884, "dataset_size": 403299007}} | 2022-12-13T01:36:57+00:00 |
1fbcb6dc50c513997b51abc94f2ed89314fb6c75 | # Dataset Card for "wikipedia-august-october-line-diff-1000-char-threshold-1000-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tristan/wikipedia-august-october-line-diff-1000-char-threshold-1000-sample | [
"region:us"
]
| 2022-12-13T03:20:27+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "crawl_timestamp", "dtype": "int64"}, {"name": "reward", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1431232, "num_examples": 1000}], "download_size": 567286, "dataset_size": 1431232}} | 2022-12-13T03:20:32+00:00 |
d806a2406f6b089c7649e060af5520739af7afa1 | # Dataset Card for "unet-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dahoas/unet-cifar10-32 | [
"region:us"
]
| 2022-12-13T04:24:16+00:00 | {"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 7110656, "num_examples": 2048}], "download_size": 6350172, "dataset_size": 7110656}} | 2022-12-13T04:24:24+00:00 |
e55f20ae9f4694b81a7b48294f9dc030f1ee52cc |
# Dataset Card for "squad_v2"
## Table of Contents
- [Dataset Card for "squad_v2"](#dataset-card-for-squad_v2)
- [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)
- [squad_v2](#squad_v2)
- [Data Fields](#data-fields)
- [squad_v2](#squad_v2-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 44.34 MB
- **Size of the generated dataset:** 122.57 MB
- **Total amount of disk used:** 166.91 MB
### Dataset Summary
combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
also determine when no answer is supported by the paragraph and abstain from answering.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### squad_v2
- **Size of downloaded dataset files:** 44.34 MB
- **Size of the generated dataset:** 122.57 MB
- **Total amount of disk used:** 166.91 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [94, 87, 94, 94],
"text": ["10th and 11th centuries", "in the 10th and 11th centuries", "10th and 11th centuries", "10th and 11th centuries"]
},
"context": "\"The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) were the people who in the 10th and 11th centuries gave thei...",
"id": "56ddde6b9a695914005b9629",
"question": "When were the Normans in Normandy?",
"title": "Normans"
}
```
### Data Fields
The data fields are the same among all splits.
#### squad_v2
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | validation |
| -------- | -----: | ---------: |
| squad_v2 | 130319 | 11873 |
| weijiang2009/AlgmonQuestioningAnsweringDataset | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2022-12-13T05:49:38+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["open-domain-qa", "extractive-qa"], "paperswithcode_id": "squad", "pretty_name": "SQuAD2.0", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}], "config_name": "squad_v2", "splits": [{"name": "train", "num_bytes": 116699950, "num_examples": 130319}, {"name": "validation", "num_bytes": 11660302, "num_examples": 11873}], "download_size": 46494161, "dataset_size": 128360252}, "train-eval-index": [{"config": "squad_v2", "task": "question-answering", "task_id": "extractive_question_answering", "splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"question": "question", "context": "context", "answers": {"text": "text", "answer_start": "answer_start"}}, "metrics": [{"type": "squad_v2", "name": "SQuAD v2"}]}]} | 2022-12-13T05:54:11+00:00 |
eb9d2950c2b4eaae5417c4d7861a0431a02a3cab | premky85/test-textual-inversion | [
"license:afl-3.0",
"region:us"
]
| 2022-12-13T06:10:51+00:00 | {"license": "afl-3.0"} | 2022-12-13T06:12:16+00:00 |
|
45c83f1c3f07f5051b82d6964c8360e6d219a5a3 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: henryu-lin/t5-large-samsum-deepspeed
* Dataset: samsum
* Config: samsum
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@uunicee](https://huggingface.co/uunicee) for evaluating this model. | autoevaluate/autoeval-eval-samsum-samsum-0cab72-2447375877 | [
"autotrain",
"evaluation",
"region:us"
]
| 2022-12-13T06:39:27+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "henryu-lin/t5-large-samsum-deepspeed", "metrics": ["squad_v2"], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "train", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2022-12-13T08:21:03+00:00 |
a8d7999e7dbec0fa361c9dd532b51c03512a03a7 | ViperYX/re-imagen-dog | [
"license:bigscience-openrail-m",
"region:us"
]
| 2022-12-13T07:06:47+00:00 | {"license": "bigscience-openrail-m"} | 2022-12-13T07:06:47+00:00 |
|
69a32e19243b03c6c410206548b444011b82da59 | # Dataset Card for "trdg_random_words_en_text_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | priyank-m/trdg_dict_random_words_en_text_recognition | [
"region:us"
]
| 2022-12-13T08:44:24+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3435853878.0, "num_examples": 115000}], "download_size": 3436541480, "dataset_size": 3435853878.0}} | 2022-12-14T01:25:22+00:00 |
362e16c8c8a6d6079dfa1ce5b95f99278f38c36d | # Dataset Card for "trdg_random_single_words_en_text_recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | priyank-m/trdg_random_single_words_en_text_recognition | [
"region:us"
]
| 2022-12-13T09:23:08+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2595486075.0, "num_examples": 155000}], "download_size": 2596520034, "dataset_size": 2595486075.0}} | 2022-12-13T20:54:58+00:00 |
5a8ac754a624e6ce0ab7a0a3251b4cdd6b8326fc | # AutoTrain Dataset for project: massive-4-catalan
## Dataset Description
This dataset has been automatically processed by AutoTrain for project massive-4-catalan.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"feat_id": "1",
"feat_locale": "ca-ES",
"feat_partition": "train",
"feat_scenario": 0,
"target": 2,
"text": "desperta'm a les nou a. m. del divendres",
"feat_annot_utt": "desperta'm a les [time : nou a. m.] del [date : divendres]",
"feat_worker_id": "42",
"feat_slot_method.slot": [
"time",
"date"
],
"feat_slot_method.method": [
"translation",
"translation"
],
"feat_judgments.worker_id": [
"42",
"30",
"3"
],
"feat_judgments.intent_score": [
1,
1,
1
],
"feat_judgments.slots_score": [
1,
1,
1
],
"feat_judgments.grammar_score": [
4,
3,
4
],
"feat_judgments.spelling_score": [
2,
2,
2
],
"feat_judgments.language_identification": [
"target",
"target|english",
"target"
]
},
{
"feat_id": "2",
"feat_locale": "ca-ES",
"feat_partition": "train",
"feat_scenario": 0,
"target": 2,
"text": "posa una alarma per d\u2019aqu\u00ed a dues hores",
"feat_annot_utt": "posa una alarma per [time : d\u2019aqu\u00ed a dues hores]",
"feat_worker_id": "15",
"feat_slot_method.slot": [
"time"
],
"feat_slot_method.method": [
"translation"
],
"feat_judgments.worker_id": [
"42",
"30",
"24"
],
"feat_judgments.intent_score": [
1,
1,
1
],
"feat_judgments.slots_score": [
1,
1,
1
],
"feat_judgments.grammar_score": [
4,
4,
4
],
"feat_judgments.spelling_score": [
2,
2,
2
],
"feat_judgments.language_identification": [
"target",
"target",
"target"
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_id": "Value(dtype='string', id=None)",
"feat_locale": "Value(dtype='string', id=None)",
"feat_partition": "Value(dtype='string', id=None)",
"feat_scenario": "ClassLabel(num_classes=18, names=['alarm', 'audio', 'calendar', 'cooking', 'datetime', 'email', 'general', 'iot', 'lists', 'music', 'news', 'play', 'qa', 'recommendation', 'social', 'takeaway', 'transport', 'weather'], id=None)",
"target": "ClassLabel(num_classes=60, names=['alarm_query', 'alarm_remove', 'alarm_set', 'audio_volume_down', 'audio_volume_mute', 'audio_volume_other', 'audio_volume_up', 'calendar_query', 'calendar_remove', 'calendar_set', 'cooking_query', 'cooking_recipe', 'datetime_convert', 'datetime_query', 'email_addcontact', 'email_query', 'email_querycontact', 'email_sendemail', 'general_greet', 'general_joke', 'general_quirky', 'iot_cleaning', 'iot_coffee', 'iot_hue_lightchange', 'iot_hue_lightdim', 'iot_hue_lightoff', 'iot_hue_lighton', 'iot_hue_lightup', 'iot_wemo_off', 'iot_wemo_on', 'lists_createoradd', 'lists_query', 'lists_remove', 'music_dislikeness', 'music_likeness', 'music_query', 'music_settings', 'news_query', 'play_audiobook', 'play_game', 'play_music', 'play_podcasts', 'play_radio', 'qa_currency', 'qa_definition', 'qa_factoid', 'qa_maths', 'qa_stock', 'recommendation_events', 'recommendation_locations', 'recommendation_movies', 'social_post', 'social_query', 'takeaway_order', 'takeaway_query', 'transport_query', 'transport_taxi', 'transport_ticket', 'transport_traffic', 'weather_query'], id=None)",
"text": "Value(dtype='string', id=None)",
"feat_annot_utt": "Value(dtype='string', id=None)",
"feat_worker_id": "Value(dtype='string', id=None)",
"feat_slot_method.slot": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"feat_slot_method.method": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"feat_judgments.worker_id": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"feat_judgments.intent_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
"feat_judgments.slots_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
"feat_judgments.grammar_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
"feat_judgments.spelling_score": "Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None)",
"feat_judgments.language_identification": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 11514 |
| valid | 2033 |
| crodri/autotrain-data-massive-4-catalan | [
"task_categories:text-classification",
"region:us"
]
| 2022-12-13T11:44:59+00:00 | {"task_categories": ["text-classification"]} | 2022-12-13T11:51:02+00:00 |
8b0fcd050ac5dc29cd30c0fc3d66a92b58b261cf | # Dataset Card for "common_voice_11_0_dummy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sanchit-gandhi/common_voice_11_0_dummy | [
"region:us"
]
| 2022-12-13T12:00:28+00:00 | {"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1012, "num_examples": 10}, {"name": "validation", "num_bytes": 592, "num_examples": 5}], "download_size": 3199, "dataset_size": 1604}} | 2022-12-13T12:00:34+00:00 |
41ebfc4ccef59325b2a904f0788007267b05184a |
## Dataset Description
This is the Python, Java and JavaScript subsets of The Stack (v1.1) after cleaning* and agressive deduplication from [stack-dedup-alt-decontaminate](https://huggingface.co/datasets/bigcode/stack-dedup-alt-decontaminate) with
filtering on [comment to code ratio](https://github.com/bigcode-project/bigcode-dataset/tree/main/preprocessing) with minimum of 0.01 and maximum of 0.8.
The additional comments filtering removes 26.5% of the dataset's volume which goes from 215GB of text to 170GB.
(*) cleaning: near deduplication + PII redaction + line length & percentage of alphanumeric characters filtering + data decontamination
| bigcode/stack-dedup-alt-comments | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"region:us"
]
| 2022-12-13T12:32:10+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced"], "language": ["code"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "task_ids": ["language-modeling"], "extra_gated_prompt": "## Terms of Use for The Stack\n\nThe Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. The Stack is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset\u2019s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | 2023-05-13T05:17:27+00:00 |
5ad57e73741afe157262119ff9a785a52c82ad83 | polinaeterna/audio_configs | [
"region:us"
]
| 2022-12-13T12:44:13+00:00 | {"configs_kwargs": [{"config_name": "v1", "data_dir": "v1", "drop_labels": true}, {"config_name": "v2", "data_dir": "v2", "drop_labels": false}]} | 2022-12-13T12:57:53+00:00 |
|
9b03c49b48da54b4d91a644e6a9ec295065b4e9b | I collected a few antonym pairs from several sources for creating interpretable embeddings. This file also contains the variance computed on quantized (english) fasttext embedings. | KnutJaegersberg/antonym_dataset_fasttext_variances | [
"license:mit",
"region:us"
]
| 2022-12-13T12:51:29+00:00 | {"license": "mit"} | 2022-12-13T12:56:39+00:00 |
dbc9fe670691e6c828c788afbc2a649e79811d0a | These direction vectors of antonyms can be used to calculate fasttext interpretable embeddings on the fly, solving the OOV problem of other interpretable embeddings.
Simply calculate cosine similarity for each row. | KnutJaegersberg/direction_vectors_ftq_en | [
"license:mit",
"region:us"
]
| 2022-12-13T12:59:41+00:00 | {"license": "mit"} | 2022-12-13T13:02:41+00:00 |
e7a07029cfd7378548c2b26028a6b38b233876d5 | # Dataset Card for "sbbdata_snr_none"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_none | [
"region:us"
]
| 2022-12-13T13:54:38+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499888925.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 508647230, "dataset_size": 625699284.0}} | 2023-01-05T16:21:55+00:00 |
c6256d9e3af82bf35156a2b760be423e139d63a3 | # Dataset Card for "sbbdata_snr_neg10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_neg10 | [
"region:us"
]
| 2022-12-13T13:55:22+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 623869547, "dataset_size": 625591668.0}} | 2023-01-05T16:22:50+00:00 |
6c3d66e636ed9c415c2178872928e792be93e205 | # Dataset Card for "sbbdata_snr_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_0 | [
"region:us"
]
| 2022-12-13T13:56:10+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 620800482, "dataset_size": 625591668.0}} | 2023-01-05T16:23:43+00:00 |
27f92337a769f905244c77c0eb02e5599c578785 | # Dataset Card for "sbbdata_snr_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_10 | [
"region:us"
]
| 2022-12-13T13:56:40+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 605948787, "dataset_size": 625591668.0}} | 2023-01-05T16:24:36+00:00 |
bfe239d14be1d0da7ca5ffa53f48d0c1dca923ff | # Dataset Card for "sbbdata_snr_20"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_20 | [
"region:us"
]
| 2022-12-13T13:57:18+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 569514379, "dataset_size": 625591668.0}} | 2023-01-05T16:25:28+00:00 |
cdde0d0a76a48224b786ca2aeda76462ca470131 | # Dataset Card for "sbbdata_snr_30"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_30 | [
"region:us"
]
| 2022-12-13T13:57:51+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 532753840, "dataset_size": 625591668.0}} | 2023-01-05T16:26:20+00:00 |
a09fe834115f722720b975b51df749fe9c118101 | # Dataset Card for "sbbdata_snr_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_40 | [
"region:us"
]
| 2022-12-13T13:58:25+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 515470146, "dataset_size": 625591668.0}} | 2023-01-05T16:27:10+00:00 |
490bd49c6b930452080f8287e8b8b47619cee8f2 | # Dataset Card for "sbbdata_snr_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marccgrau/sbbdata_snr_random | [
"region:us"
]
| 2022-12-13T13:59:00+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 499781309.0, "num_examples": 1121}, {"name": "test", "num_bytes": 63201474.0, "num_examples": 142}, {"name": "val", "num_bytes": 62608885.0, "num_examples": 141}], "download_size": 581004500, "dataset_size": 625591668.0}} | 2023-01-05T16:28:03+00:00 |
6a2e8b30d790d75a7d56707d34893c1d413e5027 | # Dataset Card for "butterflies_names"
Processed version of https://huggingface.co/datasets/huggan/inat_butterflies_top10k -- taking the description and extracting the scientific name in parentheses. | sasha/butterflies_names | [
"region:us"
]
| 2022-12-13T14:03:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "sim_score", "dtype": "float64"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 369545620.0, "num_examples": 10000}], "download_size": 367769662, "dataset_size": 369545620.0}} | 2022-12-13T14:27:56+00:00 |
8fa24feeba8bd2d09356f6bfeb9f080d2a179df3 |
# KPWr | clarin-knext/kpwr | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:18K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-sa-4.0",
"region:us"
]
| 2022-12-13T14:10:39+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pl"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["18K", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "KPWr 1.27"} | 2023-08-18T07:51:36+00:00 |
7916a8abcaa48c17c3e0b56f554b3fe6ee717864 |
# CEN | clarin-knext/cen | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:18K",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:cc-by-3.0",
"region:us"
]
| 2022-12-13T14:10:55+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["pl"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["18K", "10K<n<100K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "KPWr 1.27"} | 2022-12-13T15:48:02+00:00 |
9c855709eeba68d1e295f7c1bee5cbf61a2a5315 | TornikeO/pepe-v1 | [
"license:mit",
"region:us"
]
| 2022-12-13T15:11:07+00:00 | {"license": "mit"} | 2022-12-13T15:12:47+00:00 |
|
5c9996aa94c7be1858ef0a0585312a6d6903048e | # Dataset Card for "ptf-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hts98/ptf-dataset | [
"region:us"
]
| 2022-12-13T17:46:07+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 19917891520, "num_examples": 20736}, {"name": "test", "num_bytes": 4980426456, "num_examples": 5185}], "download_size": 4001768209, "dataset_size": 24898317976}} | 2022-12-14T01:10:44+00:00 |
81c84bee7cd20f3fb55882090971c92db9ac6069 | daydrill/dddd | [
"license:afl-3.0",
"region:us"
]
| 2022-12-13T19:25:36+00:00 | {"license": "afl-3.0"} | 2022-12-13T19:25:36+00:00 |
|
708644840762499432ed64fb7a60c1bd00e98ee8 | Jyoticaa/embeddings | [
"license:mit",
"region:us"
]
| 2022-12-13T20:44:33+00:00 | {"license": "mit"} | 2022-12-13T20:45:45+00:00 |
|
4f0d18f023c32aa613c5dc9ed6f8e39bf38f60f2 | MennaHalim/Donate_a_Cry__ | [
"license:other",
"region:us"
]
| 2022-12-13T20:50:36+00:00 | {"license": "other"} | 2022-12-20T09:44:03+00:00 |
|
b8e586d619c8a594582fb13febb1e38bdbae7ff2 | # Dataset Card for "lcbsi-wbc-ap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | polejowska/lcbsi-wbc-ap | [
"region:us"
]
| 2022-12-13T20:51:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "basophil", "1": "eosinophil", "2": "lymphocyte", "3": "monocyte", "4": "neutrophil"}}}}], "splits": [{"name": "train", "num_bytes": 25369707.0, "num_examples": 3500}, {"name": "test", "num_bytes": 5540002.0, "num_examples": 750}, {"name": "valid", "num_bytes": 5488683.0, "num_examples": 750}], "download_size": 36231350, "dataset_size": 36398392.0}} | 2022-12-13T20:52:22+00:00 |
489805c28b95433743218bab33f59f0b52d75bde |
### Dataset Curators
The original data is maintained by [ArXiv](https://arxiv.org/)
### Licensing Information
The data is under the [Creative Commons CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@misc{clement2019arxiv,
title={On the Use of ArXiv as a Dataset},
author={Colin B. Clement and Matthew Bierbaum and Kevin P. O'Keeffe and Alexander A. Alemi},
year={2019},
eprint={1905.00075},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` | zeroshot/arxiv-biology | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"arxiv:1905.00075",
"region:us"
]
| 2022-12-13T20:55:38+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["cc0-1.0"], "multilinguality": ["monolingual"]} | 2023-01-05T15:43:07+00:00 |
7baf3f8a5f3d38acc585d42d12193b27baf8cf79 | <p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
This is a Dataset Repository of **Awesome ChatGPT Prompts**
**[View All Prompts on GitHub](https://github.com/f/awesome-chatgpt-prompts)**
# License
CC-0
| fka/awesome-chatgpt-prompts | [
"license:cc0-1.0",
"ChatGPT",
"region:us"
]
| 2022-12-13T23:47:45+00:00 | {"license": "cc0-1.0", "tags": ["ChatGPT"]} | 2023-03-07T10:04:18+00:00 |
cd9eb1cdef9af92b13fe33204276187652c29bf8 | Mayaru01/SD-NAI-ANIMESTYLEMODELS | [
"license:openrail",
"region:us"
]
| 2022-12-13T23:58:07+00:00 | {"license": "openrail"} | 2022-12-13T23:58:07+00:00 |
|
673c69941d7cc168ff29437cad24f80840a6838e | https://github.com/entangledloops/slidingpuzzle/
Each JSON file contains a list of board and solution pairs. These are the optimal solutions as found through Breadth-First Search.
For example, here is a random snapshot from inside `examples_3x3.json`:
```
...
[[[8, 4, 5], [3, 7, 1], [0, 2, 6]], [2, 7, 3, 8, 4, 3, 8, 2, 7, 8, 1, 5, 3, 1, 2, 4, 1, 2, 5, 6]],
[[[8, 4, 5], [3, 7, 1], [2, 6, 0]], [6, 7, 3, 8, 4, 3, 8, 2, 7, 8, 1, 5, 3, 1, 2, 4, 1, 2, 5, 6]],
[[[8, 4, 5], [2, 3, 1], [7, 6, 0]], [6, 7, 2, 8, 4, 3, 8, 2, 7, 8, 1, 5, 3, 1, 2, 4, 1, 2, 5, 6]],
...
```
The first example represents the following board,
```
8 4 5
3 7 1
2 6
```
where 0 indicates the blank. The solution is to first move the 2, then 7, 3, 8, etc.
The final solved board state would be:
```
1 2 3
4 5 6
7 8
```
To use the data, it can be parsed:
```python
import json
with open("examples_3x3.json", "rt") as fp:
db = json.load(fp)
board, solution = db[0] # inspect first example
print(board, solution)
``` | entangledloops/slidingpuzzle | [
"license:apache-2.0",
"region:us"
]
| 2022-12-14T00:06:46+00:00 | {"license": "apache-2.0"} | 2023-02-08T20:04:44+00:00 |
d1ac4d1e0d323add5ca4c935e6926c91b863acdf |
### CORAA V1 - Dataset
CORAA is a publicly available dataset for Automatic Speech Recognition (ASR) in the Brazilian Portuguese language containing 290.77 hours of audios and their respective transcriptions (400k+ segmented audios). The dataset is composed of audios of 5 original projects:
* ALIP (Gonçalves, 2019)
* C-ORAL Brazil (Raso and Mello, 2012)
* NURC-Recife (Oliviera Jr., 2016)
* SP-2010 (Mendes and Oushiro, 2012)
* TEDx talks (talks in Portuguese)
The audios were either validated by annotators or transcripted for the first time aiming at the ASR task.
<br>
### References
* Gonçalves SCL (2019) Projeto ALIP (amostra linguística do interior paulista) e banco de dados iboruna: 10 anos de contribuição com a descrição do Português Brasileiro. Revista Estudos Linguísticos 48(1):276–297.
* Raso T, Mello H, Mittmann MM (2012) The C-ORAL-BRASIL I: Reference corpus for spoken Brazilian Portuguese. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12), European Language Resources Association (ELRA), Istanbul, Turkey, pp 106–113, URL http://www.lrec-conf.org/proceedings/lrec2012/pdf/624_Paper.pdf
* Oliviera Jr M (2016) Nurc digital um protocolo para a digitalização, anotação, arquivamento e disseminação do material do projeto da norma urbana linguística culta (NURC). CHIMERA: Revista de Corpus de Lenguas Romances y Estudios Linguísticos 3(2):149–174, URL https://revistas.uam.es/chimera/article/view/6519
* Mendes RB, Oushiro L (2012) Mapping Paulistano Portuguese: the SP2010 Project. In: Proceedings of the VIIth GSCP International Conference: Speech and Corpora, Fizenze University Press, Firenze, Italy, pp 459–463. | Felipe474/nilc-coraa-v1 | [
"license:other",
"region:us"
]
| 2022-12-14T01:34:10+00:00 | {"license": "other"} | 2022-12-18T20:47:33+00:00 |
52689e1c86fdbf8a43c3df0a0f9422d78552dd30 | # Dataset Card for "local-unet-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dahoas/local-unet-cifar10-32 | [
"region:us"
]
| 2022-12-14T02:30:22+00:00 | {"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 635009024, "num_examples": 50048}], "download_size": 647505353, "dataset_size": 635009024}} | 2022-12-14T22:42:18+00:00 |
2022593136289388299e92c17462907c0e27b2d8 | videoprojector/omelette | [
"license:cc0-1.0",
"region:us"
]
| 2022-12-14T02:54:01+00:00 | {"license": "cc0-1.0"} | 2022-12-14T02:54:01+00:00 |
|
9d43e2a79b70cde5b95c9c61b268bb20442c6421 | # Dataset Card for Capstone
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
Id
MSSubClass
MSZoning
LotFrontage
LotArea
Street
Alley
LotShape
LandContour
Utilities
LotConfig
LandSlope
Neighborhood
Condition1
Condition2
BldgType
HouseStyle
OverallQual
OverallCond
YearBuilt
YearRemodAdd
RoofStyle
RoofMatl
Exterior1st
Exterior2nd
MasVnrType
MasVnrArea
ExterQual
ExterCond
Foundation
BsmtQual
BsmtCond
BsmtExposure
BsmtFinType1
BsmtFinSF1
BsmtFinType2
BsmtFinSF2
BsmtUnfSF
TotalBsmtSF
Heating
HeatingQC
CentralAir
Electrical
1stFlrSF
2ndFlrSF
LowQualFinSF
GrLivArea
BsmtFullBath
BsmtHalfBath
FullBath
HalfBath
BedroomAbvGr
KitchenAbvGr
KitchenQual
TotRmsAbvGrd
Functional
Fireplaces
FireplaceQu
GarageType
GarageYrBlt
GarageFinish
GarageCars
GarageArea
GarageQual
GarageCond
PavedDrive
WoodDeckSF
OpenPorchSF
EnclosedPorch
3SsnPorch
ScreenPorch
PoolArea
PoolQC
Fence
MiscFeature
MiscVal
MoSold
YrSold
SaleType
SaleCondition
### Data Splits
train
test
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] | deancgarcia/capstone4650 | [
"region:us"
]
| 2022-12-14T03:59:17+00:00 | {} | 2022-12-14T04:08:10+00:00 |
0be7321c9e4094061619e14b1ce281668f3be40b | Asmit/QNA | [
"region:us"
]
| 2022-12-14T05:32:56+00:00 | {} | 2022-12-14T05:34:15+00:00 |
|
7d35ef5c1d493102b67adf21120a1483927c738f | # Dataset Card for "yannic-kilcher-transcript"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Whispering-GPT](https://github.com/matallanas/whisper_gpt_pipeline)
- **Repository:** [whisper_gpt_pipeline](https://github.com/matallanas/whisper_gpt_pipeline)
- **Paper:** [whisper](https://cdn.openai.com/papers/whisper.pdf) and [gpt](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
- **Point of Contact:** [Whispering-GPT organization](https://huggingface.co/Whispering-GPT)
### Dataset Summary
This dataset is created by applying whisper to the videos of the Youtube channel [Yannic Kilcher](https://www.youtube.com/yannickilcher). The dataset was created a medium size whisper model.
### Languages
- **Language**: English
## Dataset Structure
The dataset contains all the transcripts plus the audio of the different videos of Yannic Kilcher.
### Data Fields
The dataset is composed by:
- **id**: Id of the youtube video.
- **channel**: Name of the channel.
- **channel\_id**: Id of the youtube channel.
- **title**: Title given to the video.
- **categories**: Category of the video.
- **description**: Description added by the author.
- **text**: Whole transcript of the video.
- **segments**: A list with the time and transcription of the video.
- **start**: When started the trancription.
- **end**: When the transcription ends.
- **text**: The text of the transcription.
### Data Splits
- Train split.
## Dataset Creation
### Source Data
The transcriptions are from the videos of [Yannic Kilcher](https://www.youtube.com/yannickilcher)
### Contributions
Thanks to [Whispering-GPT](https://huggingface.co/Whispering-GPT) organization for adding this dataset. | Whispering-GPT/yannic-kilcher-transcript | [
"task_categories:automatic-speech-recognition",
"whisper",
"whispering",
"medium",
"region:us"
]
| 2022-12-14T05:49:51+00:00 | {"task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "channel", "dtype": "string"}, {"name": "channel_id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "categories", "sequence": "string"}, {"name": "tags", "sequence": "string"}, {"name": "description", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "segments", "list": [{"name": "start", "dtype": "float64"}, {"name": "end", "dtype": "float64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 24560830, "num_examples": 370}], "download_size": 12784371, "dataset_size": 24560830}, "tags": ["whisper", "whispering", "medium"]} | 2022-12-20T14:28:30+00:00 |
2a0cd8f71530f40f72fd3b20816644659733a5ae | # Dataset Card for "BDMS02_TH_AR_unchanged_with_quotation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shularp/BDMS02_TH_AR_unchanged_with_quotation | [
"region:us"
]
| 2022-12-14T07:12:54+00:00 | {"dataset_info": {"features": [{"name": "th", "dtype": "string"}, {"name": "ar", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19731, "num_examples": 85}], "download_size": 9903, "dataset_size": 19731}} | 2022-12-14T07:12:59+00:00 |
71062f39b0fc263ce07f556b7a0162b6571a4ca1 | # Dataset Card for "BDMS03_TH_AR_unchanged_without_quotation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shularp/BDMS03_TH_AR_unchanged_without_quotation | [
"region:us"
]
| 2022-12-14T07:17:34+00:00 | {"dataset_info": {"features": [{"name": "th", "dtype": "string"}, {"name": "ar", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19731, "num_examples": 85}], "download_size": 9903, "dataset_size": 19731}} | 2022-12-14T07:17:48+00:00 |
00405940bef76d939edbb1f593ccfcedccc1d17c | # Dataset Card for "BDMS04_TH_AR_rearranged_with_quotation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Shularp/BDMS04_TH_AR_rearranged_with_quotation | [
"region:us"
]
| 2022-12-14T07:18:43+00:00 | {"dataset_info": {"features": [{"name": "th", "dtype": "string"}, {"name": "ar", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19731, "num_examples": 85}], "download_size": 9903, "dataset_size": 19731}} | 2022-12-14T07:18:55+00:00 |
df90f455db0f3578e52c216f393dbc297620e5ef | sayakpaul/bool-masked-pos | [
"license:apache-2.0",
"region:us"
]
| 2022-12-14T07:35:03+00:00 | {"license": "apache-2.0"} | 2022-12-14T07:39:48+00:00 |
|
791a41003a90c57cb91a040167e260561e435e9b | # Dataset Card for "pretrained-unet-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dahoas/pretrained-unet-cifar10-32 | [
"region:us"
]
| 2022-12-14T07:39:06+00:00 | {"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 634400000, "num_examples": 50000}], "download_size": 646502859, "dataset_size": 634400000}} | 2022-12-14T07:39:41+00:00 |
2fa50d957da3f515464030d58083e3938eb64b1d | # Dataset Card for "vnese-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | truongpdd/vnese-dataset | [
"region:us"
]
| 2022-12-14T07:48:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2183248914.938227, "num_examples": 1478292}, {"name": "test", "num_bytes": 242583705.06177297, "num_examples": 164255}, {"name": "validation", "num_bytes": 242583705.06177297, "num_examples": 164255}], "download_size": 1386554788, "dataset_size": 2668416325.061773}} | 2022-12-14T08:07:48+00:00 |
7eb365bf65a27209c58227debe3b13942ff4fe97 | CMarvel/my_dataset | [
"license:unknown",
"region:us"
]
| 2022-12-14T08:13:57+00:00 | {"license": "unknown"} | 2022-12-14T08:17:41+00:00 |
|
9d7764ae4a76a84887d65f5bf7efc05a683c7be4 |
# laion-high-resolution-chinese
## 简介 Brief Introduction
取自Laion5B-high-resolution多语言多模态数据集中的中文部分，一共2.66M个图文对。
A subset from Laion5B-high-resolution (a multimodal dataset), around 2.66M image-text pairs (only Chinese).
## 数据集信息 Dataset Information
大约一共2.66M个中文图文对。大约占用381MB空间（仅仅是url等文本信息，不包含图片）。
- Homepage: [laion-5b](https://laion.ai/blog/laion-5b/)
- Huggingface: [laion/laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution)
## 下载 Download
```bash
mkdir release && cd release
for i in {00000..00015}; do wget https://huggingface.co/datasets/wanng/laion-high-resolution-chinese/resolve/main/data/train-$i-of-00016.parquet; done
cd ..
```
## Lisence
CC-BY-4.0
| wanng/laion-high-resolution-chinese | [
"task_categories:feature-extraction",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"language:zh",
"license:cc-by-4.0",
"region:us"
]
| 2022-12-14T08:42:43+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced"], "language": ["zh"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "task_categories": ["feature-extraction"], "pretty_name": "laion-high-resolution-chinese"} | 2022-12-14T15:11:23+00:00 |
5245775e1f1786e531f8be2fa14a54db21478e7d | QMoskwa/ia_train | [
"license:openrail",
"region:us"
]
| 2022-12-14T09:33:56+00:00 | {"license": "openrail"} | 2022-12-14T09:34:53+00:00 |
|
3d0fc1d50034cea74358ec40e3b9c0a37b8c6a4f | Ravshan/kun_uz_news | [
"region:us"
]
| 2022-12-14T09:59:36+00:00 | {} | 2022-12-14T10:02:43+00:00 |
|
ec1a727cc40b775bffd1ae9d3ace8ce671e9d5cd | ```
BUILDING VOCABULARY
Processed 1754541204 tokens.
Counted 5329509 unique words.
Truncating vocabulary at min count 5.
Using vocabulary of size 1539115.
```
---
# Build the Arabic Corpus
#### Dowload Resources
The arabic corpus {1.9B word} consists of the following resources:
- ShamelaLibrary348.7z [link](https://www.quran.tv/ketab/ShamelaLibrary348.7z) {1.15B}
- UN arabic corpus [mirror1](http://lotus.kuee.kyoto-u.ac.jp/~raj/rajwindroot/corpora_downloads/UN_CORPUS/UNv1.0.6way.ar.txt) [mirror2](http://corpus.leeds.ac.uk/bogdan/resources/UN-corpus/6way/UNv1.0.6way.ar.txt) {0.37B}
- AraCorpus.tar.gz [link](http://aracorpus.e3rab.com/argistestsrv.nmsu.edu/AraCorpus.tar.gz) {0.14B}
- Arabic Wikipedia Latest Articles Dump [link](https://dumps.wikimedia.org/arwiki/latest/arwiki-latest-pages-articles.xml.bz2) {0.11B}
- Tashkeela-arabic-diacritized-text-utf8-0.3.zip [link](https://netix.dl.sourceforge.net/project/tashkeela/) {0.07B}
- Arabic Tweets [link](https://github.com/bakrianoo/Datasets) {0.03B}
- watan-2004.7z [link](https://netix.dl.sourceforge.net/project/arabiccorpus/watan-2004corpus/watan-2004.7z) {0.01B}
#### Build Script: https://github.com/tarekeldeeb/GloVe-Arabic/tree/master/arabic_corpus
---
# Download the dataset
Mirror : https://archive.org/details/arabic_corpus
---
license: Waqf v2 (https://github.com/ojuba-org/waqf/tree/master/2.0) | tarekeldeeb/ArabicCorpus2B | [
"license:other",
"region:us"
]
| 2022-12-14T10:03:09+00:00 | {"license": "other"} | 2022-12-14T11:17:34+00:00 |
cbe3e224f1ae99617e6188679175ff4a9751a1e3 |
# Dataset Card for Visual Spatial Reasoning
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ltl.mmll.cam.ac.uk/
- **Repository:** https://github.com/cambridgeltl/visual-spatial-reasoning
- **Paper:** https://arxiv.org/abs/2205.00363
- **Leaderboard:** https://paperswithcode.com/sota/visual-reasoning-on-vsr
- **Point of Contact:** https://ltl.mmll.cam.ac.uk/
### Dataset Summary
The Visual Spatial Reasoning (VSR) corpus is a collection of caption-image pairs with true/false labels. Each caption describes the spatial relation of two individual objects in the image, and a vision-language model (VLM) needs to judge whether the caption is correctly describing the image (True) or not (False).
### Supported Tasks and Leaderboards
We test three baselines, all supported in huggingface. They are VisualBERT [(Li et al. 2019)](https://arxiv.org/abs/1908.03557), LXMERT [(Tan and Bansal, 2019)](https://arxiv.org/abs/1908.07490) and ViLT [(Kim et al. 2021)](https://arxiv.org/abs/2102.03334). The leaderboard can be checked at [Papers With Code](https://paperswithcode.com/sota/visual-reasoning-on-vsr).
model | random split | zero-shot
:-------------|:-------------:|:-------------:
*human* | *95.4* | *95.4*
VisualBERT | 57.4 | 54.0
LXMERT | **72.5** | **63.2**
ViLT | 71.0 | 62.4
### Languages
The language in the dataset is English as spoken by the annotators. The BCP-47 code for English is en. [`meta_data.csv`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data/data_files/meta_data.jsonl) contains meta data of annotators.
## Dataset Structure
### Data Instances
Each line is an individual data point. Each `jsonl` file is of the following format:
```json
{"image": "000000050403.jpg", "image_link": "http://images.cocodataset.org/train2017/000000050403.jpg", "caption": "The teddy bear is in front of the person.", "label": 1, "relation": "in front of", "annotator_id": 31, "vote_true_validator_id": [2, 6], "vote_false_validator_id": []}
{"image": "000000401552.jpg", "image_link": "http://images.cocodataset.org/train2017/000000401552.jpg", "caption": "The umbrella is far away from the motorcycle.", "label": 0, "relation": "far away from", "annotator_id": 2, "vote_true_validator_id": [], "vote_false_validator_id": [2, 9, 1]}
```
### Data Fields
`image` denotes name of the image in COCO and `image_link` points to the image on the COCO server (so you can also access directly). `caption` is self-explanatory. `label` being `0` and `1` corresponds to False and True respectively. `relation` records the spatial relation used. `annotator_id` points to the annotator who originally wrote the caption. `vote_true_validator_id` and `vote_false_validator_id` are annotators who voted True or False in the second phase validation.
### Data Splits
The VSR corpus, after validation, contains 10,119 data points with high agreement. On top of these, we create two splits (1) random split and (2) zero-shot split. For random split, we randomly split all data points into train, development, and test sets. Zero-shot split makes sure that train, development and test sets have no overlap of concepts (i.e., if *dog* is in test set, it is not used for training and development). Below are some basic statistics of the two splits.
split | train | dev | test | total
:------|:--------:|:--------:|:--------:|:--------:
random | 7,083 | 1,012 | 2,024 | 10,119
zero-shot | 5,440 | 259 | 731 | 6,430
Check out [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for more details.
## Dataset Creation
### Curation Rationale
Understanding spatial relations is fundamental to achieve intelligence. Existing vision-language reasoning datasets are great but they compose multiple types of challenges and can thus conflate different sources of error.
The VSR corpus focuses specifically on spatial relations so we can have accurate diagnosis and maximum interpretability.
### Source Data
#### Initial Data Collection and Normalization
**Image pair sampling.** MS COCO 2017 contains
123,287 images and has labelled the segmentation and classes of 886,284 instances (individual
objects). Leveraging the segmentation, we first
randomly select two concepts, then retrieve all images containing the two concepts in COCO 2017 (train and
validation sets). Then images that contain multiple instances of any of the concept are filtered
out to avoid referencing ambiguity. For the single-instance images, we also filter out any of the images with instance area size < 30, 000, to prevent extremely small instances. After these filtering steps,
we randomly sample a pair in the remaining images.
We repeat such process to obtain a large number of
individual image pairs for caption generation.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
**Fill in the blank: template-based caption generation.** Given a pair of images, the annotator needs to come up with a valid caption that makes it correctly describing one image but incorrect for the other. In this way, the annotator could focus on the key difference of the two images (which should be spatial relation of the two objects of interest) and come up with challenging relation that differentiates the two. Similar paradigms are also used in the annotation of previous vision-language reasoning datasets such as NLVR2 (Suhr et al., 2017,
2019) and MaRVL (Liu et al., 2021). To regularise annotators from writing modifiers and differentiating the image pair with things beyond accurate spatial relations, we opt for a template-based classification task instead of free-form caption writing. Besides, the template-generated dataset can be easily categorised based on relations and their meta-categories.
The caption template has the format of “The
`OBJ1` (is) __ the `OBJ2`.”, and the annotators
are instructed to select a relation from a fixed set
to fill in the slot. The copula “is” can be omitted
for grammaticality. For example, for “contains”,
“consists of”, and “has as a part”, “is” should be
discarded in the template when extracting the final
caption.
The fixed set of spatial relations enable us to obtain the full control of the generation process. The
full list of used relations are listed in the table below. It
contains 71 spatial relations and is adapted from
the summarised relation table of Fagundes et al.
(2021). We made minor changes to filter out clearly
unusable relations, made relation names grammatical under our template, and reduced repeated relations.
In our final dataset, 65 out of the 71 available relations are actually included (the other 6 are
either not selected by annotators or are selected but
the captions did not pass the validation phase).
| Category | Spatial Relations |
|-------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
| Adjacency | Adjacent to, alongside, at the side of, at the right side of, at the left side of, attached to, at the back of, ahead of, against, at the edge of |
| Directional | Off, past, toward, down, deep down*, up*, away from, along, around, from*, into, to*, across, across from, through*, down from |
| Orientation | Facing, facing away from, parallel to, perpendicular to |
| Projective | On top of, beneath, beside, behind, left of, right of, under, in front of, below, above, over, in the middle of |
| Proximity | By, close to, near, far from, far away from |
| Topological | Connected to, detached from, has as a part, part of, contains, within, at, on, in, with, surrounding, among, consists of, out of, between, inside, outside, touching |
| Unallocated | Beyond, next to, opposite to, after*, among, enclosed by |
**Second-round Human Validation.** Every annotated data point is reviewed by at least
two additional human annotators (validators). In
validation, given a data point (consists of an image
and a caption), the validator gives either a True or
False label. We exclude data points that have <
2/3 validators agreeing with the original label.
In the guideline, we communicated to the validators that, for relations such as “left”/“right”, “in
front of”/“behind”, they should tolerate different
reference frame: i.e., if the caption is true from either the object’s or the viewer’s reference, it should
be given a True label. Only
when the caption is incorrect under all reference
frames, a False label is assigned. This adds
difficulty to the models since they could not naively
rely on relative locations of the objects in the images but also need to correctly identify orientations of objects to make the best judgement.
#### Who are the annotators?
Annotators are hired from [prolific.co](https://prolific.co). We
require them (1) have at least a bachelor’s degree,
(2) are fluent in English or native speaker, and (3)
have a >99% historical approval rate on the platform. All annotators are paid with an hourly salary
of 12 GBP. Prolific takes an extra 33% of service
charge and 20% VAT on the service charge.
For caption generation, we release the task with
batches of 200 instances and the annotator is required to finish a batch in 80 minutes. An annotator
cannot take more than one batch per day. In this
way we have a diverse set of annotators and can
also prevent annotators from being fatigued. For
second round validation, we group 500 data points
in one batch and an annotator is asked to label each
batch in 90 minutes.
In total, 24 annotators participated in caption
generation and 26 participated in validation. The
annotators have diverse demographic background:
they were born in 13 different countries; live in 13
different couturiers; and have 14 different nationalities. 57.4% of the annotators identify themselves
as females and 42.6% as males.
### 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
This project is licensed under the [Apache-2.0 License](https://github.com/cambridgeltl/visual-spatial-reasoning/blob/master/LICENSE).
### Citation Information
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={ArXiv},
year={2022},
volume={abs/2205.00363}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset. | albertvillanova/visual-spatial-reasoning | [
"task_categories:image-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2205.00363",
"arxiv:1908.03557",
"arxiv:1908.07490",
"arxiv:2102.03334",
"region:us"
]
| 2022-12-14T11:31:30+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["image-classification"], "task_ids": [], "pretty_name": "Visual Spatial Reasoning", "tags": []} | 2022-12-14T11:55:48+00:00 |
0dbeda6f6c892deaf39027f8088c9b26294db98d |
# FLEURS
## Dataset Description
- **Fine-Tuning script:** [pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)
- **Paper:** [FLEURS: Few-shot Learning Evaluation of
Universal Representations of Speech](https://arxiv.org/abs/2205.12446)
- **Total amount of disk used:** ca. 350 GB
Fleurs is the speech version of the [FLoRes machine translation benchmark](https://arxiv.org/abs/2106.03193).
We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages.
Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is
used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas:
- **Western Europe**: *Asturian, Bosnian, Catalan, Croatian, Danish, Dutch, English, Finnish, French, Galician, German, Greek, Hungarian, Icelandic, Irish, Italian, Kabuverdianu, Luxembourgish, Maltese, Norwegian, Occitan, Portuguese, Spanish, Swedish, Welsh*
- **Eastern Europe**: *Armenian, Belarusian, Bulgarian, Czech, Estonian, Georgian, Latvian, Lithuanian, Macedonian, Polish, Romanian, Russian, Serbian, Slovak, Slovenian, Ukrainian*
- **Central-Asia/Middle-East/North-Africa**: *Arabic, Azerbaijani, Hebrew, Kazakh, Kyrgyz, Mongolian, Pashto, Persian, Sorani-Kurdish, Tajik, Turkish, Uzbek*
- **Sub-Saharan Africa**: *Afrikaans, Amharic, Fula, Ganda, Hausa, Igbo, Kamba, Lingala, Luo, Northern-Sotho, Nyanja, Oromo, Shona, Somali, Swahili, Umbundu, Wolof, Xhosa, Yoruba, Zulu*
- **South-Asia**: *Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Nepali, Oriya, Punjabi, Sindhi, Tamil, Telugu, Urdu*
- **South-East Asia**: *Burmese, Cebuano, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Maori, Thai, Vietnamese*
- **CJK languages**: *Cantonese and Mandarin Chinese, Japanese, Korean*
## Supported Tasks
### 1. Speech Recognition (ASR)
```py
from datasets import load_dataset
fleurs_asr = load_dataset("google/fleurs", "af_za") # for Afrikaans
# to download all data for multi-lingual fine-tuning uncomment following line
# fleurs_asr = load_dataset("google/fleurs", "all")
# see structure
print(fleurs_asr)
# load audio sample on the fly
audio_input = fleurs_asr["train"][0]["audio"] # first decoded audio sample
transcription = fleurs_asr["train"][0]["transcription"] # first transcription
# use `audio_input` and `transcription` to fine-tune your model for ASR
# for analyses see language groups
all_language_groups = fleurs_asr["train"].features["lang_group_id"].names
lang_group_id = fleurs_asr["train"][0]["lang_group_id"]
all_language_groups[lang_group_id]
```
### 2. Language Identification
LangID can often be a domain classification, but in the case of FLEURS-LangID, recordings are done in a similar setting across languages and the utterances correspond to n-way parallel sentences, in the exact same domain, making this task particularly relevant for evaluating LangID. The setting is simple, FLEURS-LangID is splitted in train/valid/test for each language. We simply create a single train/valid/test for LangID by merging all.
```py
from datasets import load_dataset
fleurs_langID = load_dataset("google/fleurs", "all") # to download all data
# see structure
print(fleurs_langID)
# load audio sample on the fly
audio_input = fleurs_langID["train"][0]["audio"] # first decoded audio sample
language_class = fleurs_langID["train"][0]["lang_id"] # first id class
language = fleurs_langID["train"].features["lang_id"].names[language_class]
# use audio_input and language_class to fine-tune your model for audio classification
```
### 3. Retrieval
Retrieval provides n-way parallel speech and text data. Similar to how XTREME for text leverages Tatoeba to evaluate bitext mining a.k.a sentence translation retrieval, we use Retrieval to evaluate the quality of fixed-size representations of speech utterances. Our goal is to incentivize the creation of fixed-size speech encoder for speech retrieval. The system has to retrieve the English "key" utterance corresponding to the speech translation of "queries" in 15 languages. Results have to be reported on the test sets of Retrieval whose utterances are used as queries (and keys for English). We augment the English keys with a large number of utterances to make the task more difficult.
```py
from datasets import load_dataset
fleurs_retrieval = load_dataset("google/fleurs", "af_za") # for Afrikaans
# to download all data for multi-lingual fine-tuning uncomment following line
# fleurs_retrieval = load_dataset("google/fleurs", "all")
# see structure
print(fleurs_retrieval)
# load audio sample on the fly
audio_input = fleurs_retrieval["train"][0]["audio"] # decoded audio sample
text_sample_pos = fleurs_retrieval["train"][0]["transcription"] # positive text sample
text_sample_neg = fleurs_retrieval["train"][1:20]["transcription"] # negative text samples
# use `audio_input`, `text_sample_pos`, and `text_sample_neg` to fine-tune your model for retrieval
```
Users can leverage the training (and dev) sets of FLEURS-Retrieval with a ranking loss to build better cross-lingual fixed-size representations of speech.
## Dataset Structure
We show detailed information the example configurations `af_za` of the dataset.
All other configurations have the same structure.
### Data Instances
**af_za**
- Size of downloaded dataset files: 1.47 GB
- Size of the generated dataset: 1 MB
- Total amount of disk used: 1.47 GB
An example of a data instance of the config `af_za` looks as follows:
```
{'id': 91,
'num_samples': 385920,
'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/310a663d52322700b3d3473cbc5af429bd92a23f9bc683594e70bc31232db39e/home/vaxelrod/FLEURS/oss2_obfuscated/af_za/audio/train/17797742076841560615.wav',
'array': array([ 0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ...,
-1.1205673e-04, -8.4638596e-05, -1.2731552e-04], dtype=float32),
'sampling_rate': 16000},
'raw_transcription': 'Dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
'transcription': 'dit is nog nie huidiglik bekend watter aantygings gemaak sal word of wat owerhede na die seun gelei het nie maar jeugmisdaad-verrigtinge het in die federale hof begin',
'gender': 0,
'lang_id': 0,
'language': 'Afrikaans',
'lang_group_id': 3}
```
### Data Fields
The data fields are the same among all splits.
- **id** (int): ID of audio sample
- **num_samples** (int): Number of float values
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **raw_transcription** (str): The non-normalized transcription of the audio file
- **transcription** (str): Transcription of the audio file
- **gender** (int): Class id of gender
- **lang_id** (int): Class id of language
- **lang_group_id** (int): Class id of language group
### Data Splits
Every config only has the `"train"` split containing of *ca.* 1000 examples, and a `"validation"` and `"test"` split each containing of *ca.* 400 examples.
## Dataset Creation
We collect between one and three recordings for each sentence (2.3 on average), and buildnew train-dev-test splits with 1509, 150 and 350 sentences for
train, dev and test respectively.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is meant to encourage the development of speech technology in a lot more languages of the world. One of the goal is to give equal access to technologies like speech recognition or speech translation to everyone, meaning better dubbing or better access to content from the internet (like podcasts, streaming or videos).
### Discussion of Biases
Most datasets have a fair distribution of gender utterances (e.g. the newly introduced FLEURS dataset). While many languages are covered from various regions of the world, the benchmark misses many languages that are all equally important. We believe technology built through FLEURS should generalize to all languages.
### Other Known Limitations
The dataset has a particular focus on read-speech because common evaluation benchmarks like CoVoST-2 or LibriSpeech evaluate on this type of speech. There is sometimes a known mismatch between performance obtained in a read-speech setting and a more noisy setting (in production for instance). Given the big progress that remains to be made on many languages, we believe better performance on FLEURS should still correlate well with actual progress made for speech understanding.
## Additional Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
You can access the FLEURS paper at https://arxiv.org/abs/2205.12446.
Please cite the paper when referencing the FLEURS corpus as:
```
@article{fleurs2022arxiv,
title = {FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech},
author = {Conneau, Alexis and Ma, Min and Khanuja, Simran and Zhang, Yu and Axelrod, Vera and Dalmia, Siddharth and Riesa, Jason and Rivera, Clara and Bapna, Ankur},
journal={arXiv preprint arXiv:2205.12446},
url = {https://arxiv.org/abs/2205.12446},
year = {2022},
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@aconneau](https://github.com/aconneau) for adding this dataset.
| cahya/fleurs | [
"task_categories:automatic-speech-recognition",
"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:afr",
"language:amh",
"language:ara",
"language:asm",
"language:ast",
"language:azj",
"language:bel",
"language:ben",
"language:bos",
"language:cat",
"language:ceb",
"language:cmn",
"language:ces",
"language:cym",
"language:dan",
"language:deu",
"language:ell",
"language:eng",
"language:spa",
"language:est",
"language:fas",
"language:ful",
"language:fin",
"language:tgl",
"language:fra",
"language:gle",
"language:glg",
"language:guj",
"language:hau",
"language:heb",
"language:hin",
"language:hrv",
"language:hun",
"language:hye",
"language:ind",
"language:ibo",
"language:isl",
"language:ita",
"language:jpn",
"language:jav",
"language:kat",
"language:kam",
"language:kea",
"language:kaz",
"language:khm",
"language:kan",
"language:kor",
"language:ckb",
"language:kir",
"language:ltz",
"language:lug",
"language:lin",
"language:lao",
"language:lit",
"language:luo",
"language:lav",
"language:mri",
"language:mkd",
"language:mal",
"language:mon",
"language:mar",
"language:msa",
"language:mlt",
"language:mya",
"language:nob",
"language:npi",
"language:nld",
"language:nso",
"language:nya",
"language:oci",
"language:orm",
"language:ory",
"language:pan",
"language:pol",
"language:pus",
"language:por",
"language:ron",
"language:rus",
"language:bul",
"language:snd",
"language:slk",
"language:slv",
"language:sna",
"language:som",
"language:srp",
"language:swe",
"language:swh",
"language:tam",
"language:tel",
"language:tgk",
"language:tha",
"language:tur",
"language:ukr",
"language:umb",
"language:urd",
"language:uzb",
"language:vie",
"language:wol",
"language:xho",
"language:yor",
"language:yue",
"language:zul",
"license:cc-by-4.0",
"speech-recognition",
"arxiv:2205.12446",
"arxiv:2106.03193",
"region:us"
]
| 2022-12-14T12:00:52+00:00 | {"annotations_creators": ["expert-generated", "crowdsourced", "machine-generated"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["afr", "amh", "ara", "asm", "ast", "azj", "bel", "ben", "bos", "cat", "ceb", "cmn", "ces", "cym", "dan", "deu", "ell", "eng", "spa", "est", "fas", "ful", "fin", "tgl", "fra", "gle", "glg", "guj", "hau", "heb", "hin", "hrv", "hun", "hye", "ind", "ibo", "isl", "ita", "jpn", "jav", "kat", "kam", "kea", "kaz", "khm", "kan", "kor", "ckb", "kir", "ltz", "lug", "lin", "lao", "lit", "luo", "lav", "mri", "mkd", "mal", "mon", "mar", "msa", "mlt", "mya", "nob", "npi", "nld", "nso", "nya", "oci", "orm", "ory", "pan", "pol", "pus", "por", "ron", "rus", "bul", "snd", "slk", "slv", "sna", "som", "srp", "swe", "swh", "tam", "tel", "tgk", "tha", "tur", "ukr", "umb", "urd", "uzb", "vie", "wol", "xho", "yor", "yue", "zul"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10K<n<100K"], "task_categories": ["automatic-speech-recognition"], "task_ids": [], "pretty_name": "The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers 102 languages from 10+ language families, 3 different domains and 4 task families: speech recognition, translation, classification and retrieval.", "tags": ["speech-recognition"]} | 2022-12-18T11:58:34+00:00 |
309d09e16b72f4c7056aa8ada7e2feddd6a7c180 |
GitHub metadata for https://huggingface.co/datasets/bigcode/commitpack | bigcode/commitpackmeta | [
"region:us"
]
| 2022-12-14T12:53:43+00:00 | {} | 2023-07-22T15:05:21+00:00 |
081e921d50cf5c4c6dc0730846268decac1c97aa | TornikeO/nitrosocke-ghibli-diffusion-dreambooth-cache | [
"license:mit",
"region:us"
]
| 2022-12-14T13:38:42+00:00 | {"license": "mit"} | 2022-12-14T13:40:19+00:00 |
|
735ddbadd8720256865e0a77cdb874080d829bc7 | # Dataset Card for "generic_review_detection"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Brian-M-Collins/generic_review_detection | [
"region:us"
]
| 2022-12-14T13:38:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2659807, "num_examples": 3464}, {"name": "test", "num_bytes": 2168768, "num_examples": 1583}], "download_size": 0, "dataset_size": 4828575}} | 2022-12-14T15:35:11+00:00 |
b36b9b7af14b341eba5380b0f5dccb31fce3a922 | # Dataset Card for "Trial2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amosr2002/Trial2 | [
"region:us"
]
| 2022-12-14T15:13:09+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label.annotations", "list": [{"name": "id", "dtype": "int32"}, {"name": "category_id", "dtype": "int32"}]}, {"name": "label.segmentation_bitmap", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1505608.0, "num_examples": 3}], "download_size": 1515722, "dataset_size": 1505608.0}} | 2022-12-14T15:13:21+00:00 |
fee82d7584c6aa279d9e0b0d3c3198e0040f05f6 | # Dataset Card for "text_recognition_en_zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | priyank-m/text_recognition_en_zh | [
"region:us"
]
| 2022-12-14T15:17:56+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 24066869565.924767, "num_examples": 1447547}, {"name": "val", "num_bytes": 49945762.89211632, "num_examples": 3000}, {"name": "test", "num_bytes": 49961758.89211632, "num_examples": 3000}], "download_size": 5513763538, "dataset_size": 24166777087.709}} | 2022-12-14T17:54:49+00:00 |
d15fa37451ed266aa76c3aaea2ecff0913dabf68 | # Dataset Card for "Trial4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amosr2002/Trial4 | [
"region:us"
]
| 2022-12-14T15:18:25+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label.annotations", "list": [{"name": "id", "dtype": "int32"}, {"name": "category_id", "dtype": "int32"}]}, {"name": "label.segmentation_bitmap", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1505608.0, "num_examples": 3}], "download_size": 1515722, "dataset_size": 1505608.0}} | 2022-12-14T15:18:52+00:00 |
762bf8d7db6a91266164abd3ced972e95965f90b | Lonaz/test | [
"license:afl-3.0",
"region:us"
]
| 2022-12-14T16:12:50+00:00 | {"license": "afl-3.0"} | 2022-12-14T16:13:41+00:00 |
|
424732d636dfa1ced68d30ca0b46b2216d016699 | # Dataset Card for "NYC_Data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amosr2002/NYC_Data | [
"region:us"
]
| 2022-12-14T16:29:27+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label.annotations", "list": [{"name": "id", "dtype": "int32"}, {"name": "category_id", "dtype": "int32"}]}, {"name": "label.segmentation_bitmap", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5365744.0, "num_examples": 10}], "download_size": 5375305, "dataset_size": 5365744.0}} | 2022-12-14T16:29:39+00:00 |
c401292d7330c49414c58a6ebefbaee6fb6e9436 | # Dataset Card for "NYCDATA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | amosr2002/NYCDATA | [
"region:us"
]
| 2022-12-14T16:30:31+00:00 | {"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "uuid", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "label.annotations", "list": [{"name": "id", "dtype": "int32"}, {"name": "category_id", "dtype": "int32"}]}, {"name": "label.segmentation_bitmap", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 5365744.0, "num_examples": 10}], "download_size": 0, "dataset_size": 5365744.0}} | 2022-12-14T16:36:21+00:00 |
c035cb071fd2dad469792c2d6351cf1c9e8f760f | Syrinx11/CelestinaGlasses | [
"license:cc",
"region:us"
]
| 2022-12-14T16:55:50+00:00 | {"license": "cc"} | 2022-12-14T16:56:42+00:00 |
|
a1d1197bd51c734339d8eedc1aeb6ed062a4fed3 | # Dataset Card for "diachronia-ocr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zombely/diachronia-ocr-test-A | [
"region:us"
]
| 2022-12-14T17:21:21+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 62457501.0, "num_examples": 81}], "download_size": 62461147, "dataset_size": 62457501.0}} | 2022-12-14T17:21:46+00:00 |
86176eaf110c34c7f362265da2bbaa11af01125d |
# Dataset Card for Universal Dependencies Treebank
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Universal Dependencies](https://universaldependencies.org/)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@jplu](https://github.com/jplu) for adding this dataset. | albertvillanova/universal_dependencies | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:af",
"language:aii",
"language:ajp",
"language:akk",
"language:am",
"language:apu",
"language:aqz",
"language:ar",
"language:be",
"language:bg",
"language:bho",
"language:bm",
"language:br",
"language:bxr",
"language:ca",
"language:ckt",
"language:cop",
"language:cs",
"language:cu",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fo",
"language:fr",
"language:fro",
"language:ga",
"language:gd",
"language:gl",
"language:got",
"language:grc",
"language:gsw",
"language:gun",
"language:gv",
"language:he",
"language:hi",
"language:hr",
"language:hsb",
"language:hu",
"language:hy",
"language:id",
"language:is",
"language:it",
"language:ja",
"language:kfm",
"language:kk",
"language:kmr",
"language:ko",
"language:koi",
"language:kpv",
"language:krl",
"language:la",
"language:lt",
"language:lv",
"language:lzh",
"language:mdf",
"language:mr",
"language:mt",
"language:myu",
"language:myv",
"language:nl",
"language:no",
"language:nyq",
"language:olo",
"language:orv",
"language:otk",
"language:pcm",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sa",
"language:sk",
"language:sl",
"language:sme",
"language:sms",
"language:soj",
"language:sq",
"language:sr",
"language:sv",
"language:swl",
"language:ta",
"language:te",
"language:th",
"language:tl",
"language:tpn",
"language:tr",
"language:ug",
"language:uk",
"language:ur",
"language:vi",
"language:wbp",
"language:wo",
"language:yo",
"language:yue",
"language:zh",
"license:unknown",
"constituency-parsing",
"dependency-parsing",
"region:us"
]
| 2022-12-14T17:34:02+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["af", "aii", "ajp", "akk", "am", "apu", "aqz", "ar", "be", "bg", "bho", "bm", "br", "bxr", "ca", "ckt", "cop", "cs", "cu", "cy", "da", "de", "el", "en", "es", "et", "eu", "fa", "fi", "fo", "fr", "fro", "ga", "gd", "gl", "got", "grc", "gsw", "gun", "gv", "he", "hi", "hr", "hsb", "hu", "hy", "id", "is", "it", "ja", "kfm", "kk", "kmr", "ko", "koi", "kpv", "krl", "la", "lt", "lv", "lzh", "mdf", "mr", "mt", "myu", "myv", "nl", "no", "nyq", "olo", "orv", "otk", "pcm", "pl", "pt", "ro", "ru", "sa", "sk", "sl", "sme", "sms", "soj", "sq", "sr", "sv", "swl", "ta", "te", "th", "tl", "tpn", "tr", "ug", "uk", "ur", "vi", "wbp", "wo", "yo", "yue", "zh"], "license": ["unknown"], "multilinguality": ["multilingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["parsing"], "paperswithcode_id": 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"hsb_ufal", "hu_szeged", "hy_armtdp", "id_csui", "id_gsd", "id_pud", "is_icepahc", "is_pud", "it_isdt", "it_partut", "it_postwita", "it_pud", "it_twittiro", "it_vit", "ja_bccwj", "ja_gsd", "ja_modern", "ja_pud", "kfm_aha", "kk_ktb", "kmr_mg", "ko_gsd", "ko_kaist", "ko_pud", "koi_uh", "kpv_ikdp", "kpv_lattice", "krl_kkpp", "la_ittb", "la_llct", "la_perseus", "la_proiel", "lt_alksnis", "lt_hse", "lv_lvtb", "lzh_kyoto", "mdf_jr", "mr_ufal", "mt_mudt", "myu_tudet", "myv_jr", "nl_alpino", "nl_lassysmall", "no_bokmaal", "no_nynorsk", "no_nynorsklia", "nyq_aha", "olo_kkpp", "orv_rnc", "orv_torot", "otk_tonqq", "pcm_nsc", "pl_lfg", "pl_pdb", "pl_pud", "pt_bosque", "pt_gsd", "pt_pud", "qhe_hiencs", "qtd_sagt", "ro_nonstandard", "ro_rrt", "ro_simonero", "ru_gsd", "ru_pud", "ru_syntagrus", "ru_taiga", "sa_ufal", "sa_vedic", "sk_snk", "sl_ssj", "sl_sst", "sme_giella", "sms_giellagas", "soj_aha", "sq_tsa", "sr_set", "sv_lines", "sv_pud", "sv_talbanken", "swl_sslc", "ta_mwtt", "ta_ttb", "te_mtg", "th_pud", "tl_trg", "tl_ugnayan", "tpn_tudet", "tr_boun", "tr_gb", "tr_imst", "tr_pud", "ug_udt", "uk_iu", "ur_udtb", "vi_vtb", "wbp_ufal", "wo_wtb", "yo_ytb", "yue_hk", "zh_cfl", "zh_gsd", "zh_gsdsimp", "zh_hk", "zh_pud"], "tags": ["constituency-parsing", "dependency-parsing"], "dataset_info": [{"config_name": "af_afribooms", "features": [{"name": "idx", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "upos", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "_", "14": "ADV", "15": "INTJ", "16": "VERB", "17": "AUX"}}}}, {"name": "xpos", "sequence": "string"}, {"name": "feats", "sequence": "string"}, {"name": "head", "sequence": "string"}, {"name": "deprel", "sequence": "string"}, {"name": "deps", "sequence": "string"}, 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{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "upos", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "_", "14": "ADV", "15": "INTJ", "16": "VERB", "17": "AUX"}}}}, {"name": "xpos", "sequence": "string"}, {"name": "feats", "sequence": "string"}, {"name": "head", "sequence": "string"}, {"name": "deprel", "sequence": "string"}, {"name": "deps", "sequence": "string"}, {"name": "misc", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 2781883, "num_examples": 1188}, {"name": "validation", "num_bytes": 1204839, "num_examples": 449}, {"name": "test", "num_bytes": 1227124, "num_examples": 470}], "download_size": 3042699, "dataset_size": 5213846}, {"config_name": "yo_ytb", "features": [{"name": "idx", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "lemmas", "sequence": "string"}, {"name": "upos", "sequence": {"class_label": {"names": {"0": "NOUN", "1": "PUNCT", "2": "ADP", "3": "NUM", "4": "SYM", "5": "SCONJ", "6": "ADJ", "7": "PART", "8": "DET", "9": "CCONJ", "10": "PROPN", "11": "PRON", "12": "X", "13": "_", "14": "ADV", "15": "INTJ", "16": "VERB", "17": "AUX"}}}}, {"name": "xpos", "sequence": "string"}, {"name": "feats", "sequence": "string"}, {"name": "head", "sequence": "string"}, {"name": "deprel", "sequence": "string"}, {"name": "deps", "sequence": "string"}, {"name": "misc", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 905766, "num_examples": 318}], "download_size": 567955, "dataset_size": 905766}]} | 2023-11-24T13:31:54+00:00 |
1694abf4b171946c969c81e372ea993d95305ef8 | ### This is the v3 of my Sideview embedding,
here you can download all steps saved.
Personlly I recommend going in 1000 steps up from 2000, depending on if you want more style or less.
*REMEMBER:*
to use the embedding it will need to be in you Auto1111 embeddings folder and you will need to use the name in your prompt, see civitai page for more info.
some example prompts to use:
a man with a mohawk and a yellow scarf on his head and a yellow background with a black and yellow design, art by flonixsdviewv3
a man with a mask on his face and a city in the background with blue lines and a orange background with a circle, art by flonixsdviewv3
a man with dreadlocks and a gas mask on his face, with a red and black background, art by flonixsdviewv3
### More Images on the Civit.ai page https://civitai.com/models/1373/flonixs-side-view
https://civitai.com/models/1373/flonixs-side-view
<img src="https://s3.amazonaws.com/moonup/production/uploads/1671040720337-63383cdec6295341204b2ade.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1671040772203-63383cdec6295341204b2ade.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1671040828365-63383cdec6295341204b2ade.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1671040891116-63383cdec6295341204b2ade.png" width="100%"/>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1671040930692-63383cdec6295341204b2ade.png" width="100%"/> | Flonixcorn/SVEmbed | [
"license:cc0-1.0",
"region:us"
]
| 2022-12-14T17:43:58+00:00 | {"license": "cc0-1.0"} | 2022-12-28T21:08:09+00:00 |
44b2d131cccda18ce31a97ddb0a46aec4546ad96 | # Dataset Card for "nsc_batches"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dlproject/nsc_batches | [
"region:us"
]
| 2022-12-14T17:48:01+00:00 | {"dataset_info": {"features": [{"name": "input_values", "sequence": {"sequence": {"sequence": "float32"}}}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2000882211, "num_examples": 10000}], "download_size": 1765862751, "dataset_size": 2000882211}} | 2022-12-14T17:51:02+00:00 |
e17e52f87ec517cf2820afae90a91f1f50da5ff6 | # Dataset Card for "dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | my-projects/dataset1 | [
"region:us"
]
| 2022-12-14T19:15:35+00:00 | {"dataset_info": {"features": [{"name": "solution", "sequence": "int64"}, {"name": "coefficients", "sequence": "int64"}, {"name": "discriminant", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4814334, "num_examples": 80000}, {"name": "test", "num_bytes": 1204417, "num_examples": 20000}], "download_size": 1554124, "dataset_size": 6018751}} | 2022-12-14T19:15:42+00:00 |
ba2bc6a0c8b5334815871c8d0adeaa590cd40757 | Mai321/ZeroTwo-PlugSuit | [
"license:openrail",
"region:us"
]
| 2022-12-14T19:42:56+00:00 | {"license": "openrail"} | 2022-12-14T19:45:33+00:00 |
|
b2283b65ba312361f6d46cdb63db19dd4808161a |
# ROSE 🌹
This repo contiains the RoSE benchmark of our paper "Revisiting the Gold Standard:
Grounding Summarization Evaluation with Robust Human Evaluation".
Please visit [here](https://yale-lily.github.io/ROSE/) for a demo page of this project.
### ACU Annotations
RoSE benchmark contains system outputs annotated with our ACU protocol.
It contains four parts:
- CNNDM, test set annotations
- CNNDM, validation set annotations
- XSum, test set annotations
- SamSum, test set annotations
We summarize the statistics below.
| Dataset | Split | #Doc. | #Sys. | #Total Summ. | HF Name
| --- | --- | --- | --- | --- | --- |
| CNNDM | Test | 500 | 12 | 6000 | `cnndm_test` |
| CNNDM | Validation | 1000 | 8 | 8000 | `cnndm_validation` |
| XSum | Test | 500 | 8 | 4000 | `xsum` |
| SamSum | Test | 500 | 8 | 4000 | `samsum` |
### Human Annotations with Different Evaluation Protocols
We have system outputs annotated with four different human evaluation protocols in total.
We summarize them below.
| Protocol | w/ Input Document | w/ Reference Summary | Fine-grained |
| --- | --- | --- | --- |
| Prior | ✗ | ✗ | ✗ |
| Ref-free | ✓ | ✗ | ✗ |
| Ref-based | ✗ | ✓ | ✗ |
| ACU | ✗ | ✓ | ✓ |
We annotated two sets of system summaries.
1. Summaries of 12 fine-tuned systems. The huggingface data split name is `cnndm_protocol`.
2. Zero-shot summaries from large langauge models (GPT3, T0), together with summaries from BRIO and BART. The huggingface data split name is `cnndm_protocol_gpt3`.
| Salesforce/rose | [
"language:en",
"region:us"
]
| 2022-12-14T20:13:26+00:00 | {"language": ["en"]} | 2023-06-07T20:00:52+00:00 |
b8abaa3360b28cfb3e9b7105cab76004ea433a0e | # Dataset Card for "butterflies_names_multiple"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sasha/butterflies_names_multiple | [
"region:us"
]
| 2022-12-14T20:36:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "description", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3477011364.593742, "num_examples": 43459}], "download_size": 3486703583, "dataset_size": 3477011364.593742}} | 2022-12-14T20:41:21+00:00 |
3b0d44f06c8d64e36d074c6fd1322f34124d138b | # Dataset Card for "ev-skins-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jubba/ev-skins-1 | [
"region:us"
]
| 2022-12-14T21:01:07+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9106912.0, "num_examples": 117}], "download_size": 9098056, "dataset_size": 9106912.0}} | 2022-12-14T21:01:13+00:00 |
4d0dfd4a3712a80e7b8498ed76e778f7cea9d21c |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://cocodataset.org/](https://cocodataset.org/)
- **Repository:**
- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MS COCO is a large-scale object detection, segmentation, and captioning dataset.
COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
As of now, there is only the 2014 subset (with Karpathy annotations and splits), but feel free to contribute the 2017 subset of COCO!
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7F69C1BA8550>,
'filepath': 'COCO_val2014_000000522418.jpg',
'sentids': [681330, 686718, 688839, 693159, 693204],
'filename': 'COCO_val2014_000000522418.jpg',
'imgid': 1,
'split': 'restval',
'sentences': {
'tokens': ['a', 'woman', 'wearing', 'a', 'net', 'on', 'her', 'head', 'cutting', 'a', 'cake'],
'raw': 'A woman wearing a net on her head cutting a cake. ',
'imgid': 1,
'sentid': 681330
},
'cocoid': 522418
}
```
### 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
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
| HuggingFaceM4/COCO | [
"license:cc-by-4.0",
"arxiv:1405.0312",
"region:us"
]
| 2022-12-14T21:13:57+00:00 | {"license": "cc-by-4.0"} | 2022-12-15T15:51:03+00:00 |
e85f9ef4d629cd2ea2e4a36867ccac6943259f4e | ```
@inproceedings{larson-etal-2019-evaluation,
title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
author = "Larson, Stefan and
Mahendran, Anish and
Peper, Joseph J. and
Clarke, Christopher and
Lee, Andrew and
Hill, Parker and
Kummerfeld, Jonathan K. and
Leach, Kevin and
Laurenzano, Michael A. and
Tang, Lingjia and
Mars, Jason",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
year = "2019",
url = "https://www.aclweb.org/anthology/D19-1131"
}
``` | fathyshalab/clinic-meta | [
"region:us"
]
| 2022-12-14T21:47:49+00:00 | {"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 65857.4, "num_examples": 1050}, {"name": "test", "num_bytes": 28224.6, "num_examples": 450}], "download_size": 0, "dataset_size": 94082.0}} | 2023-05-15T07:49:00+00:00 |
0f7c6fd10117019eba99feb9cad5422aa315fb51 |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://google.github.io/localized-narratives/(https://google.github.io/localized-narratives/)
- **Repository:**: [https://github.com/google/localized-narratives](https://github.com/google/localized-narratives)
- **Paper:** [Connecting Vision and Language with Localized Narratives](https://arxiv.org/pdf/1912.03098.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Localized Narratives, a new form of multimodal image annotations connecting vision and language.
We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing.
Since the voice and the mouse pointer are synchronized, we can localize every single word in the description.
This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data.
We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available.
As of now, there is only the `OpenImages` subset, but feel free to contribute the other subset of Localized Narratives!
`OpenImages_captions` is similar to the `OpenImages` subset. The differences are that captions are groupped per image (images can have multiple captions). For this subset, `timed_caption`, `traces` and `voice_recording` are not available.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
dataset_id: 'mscoco_val2017',
image_id: '137576',
annotator_id: 93,
caption: 'In this image there are group of cows standing and eating th...',
timed_caption: [{'utterance': 'In this', 'start_time': 0.0, 'end_time': 0.4}, ...],
traces: [[{'x': 0.2086, 'y': -0.0533, 't': 0.022}, ...], ...],
voice_recording: 'coco_val/coco_val_137576_93.ogg'
}
```
### Data Fields
Each line represents one Localized Narrative annotation on one image by one annotator and has the following fields:
- `dataset_id`: String identifying the dataset and split where the image belongs, e.g. mscoco_val2017.
- `image_id` String identifier of the image, as specified on each dataset.
- `annotator_id` Integer number uniquely identifying each annotator.
- `caption` Image caption as a string of characters.
- `timed_caption` List of timed utterances, i.e. {utterance, start_time, end_time} where utterance is a word (or group of words) and (start_time, end_time) is the time during which it was spoken, with respect to the start of the recording.
- `traces` List of trace segments, one between each time the mouse pointer enters the image and goes away from it. Each trace segment is represented as a list of timed points, i.e. {x, y, t}, where x and y are the normalized image coordinates (with origin at the top-left corner of the image) and t is the time in seconds since the start of the recording. Please note that the coordinates can go a bit beyond the image, i.e. <0 or >1, as we recorded the mouse traces including a small band around the image.
- `voice_recording` Relative URL path with respect to https://storage.googleapis.com/localized-narratives/voice-recordings where to find the voice recording (in OGG format) for that particular image.
### 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
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
| HuggingFaceM4/LocalizedNarratives | [
"license:cc-by-4.0",
"arxiv:1912.03098",
"region:us"
]
| 2022-12-14T21:53:31+00:00 | {"license": "cc-by-4.0"} | 2022-12-15T23:12:48+00:00 |
ec857e7cff86f1c3069a682ac4be42072cd6c135 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: lewtun/autotrain-acronym-identification-7324788
* Dataset: acronym_identification
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@wjenkins](https://huggingface.co/wjenkins) for evaluating this model. | autoevaluate/autoeval-eval-acronym_identification-default-01d2b7-2476976473 | [
"autotrain",
"evaluation",
"region:us"
]
| 2022-12-14T22:06:49+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["acronym_identification"], "eval_info": {"task": "entity_extraction", "model": "lewtun/autotrain-acronym-identification-7324788", "metrics": ["bertscore", "angelina-wang/directional_bias_amplification"], "dataset_name": "acronym_identification", "dataset_config": "default", "dataset_split": "validation", "col_mapping": {"tokens": "tokens", "tags": "labels"}}} | 2022-12-14T22:07:37+00:00 |
0dc0afa0510cf30247a121840f47beeb65506260 | hassan4830/reviewclassification | [
"license:afl-3.0",
"region:us"
]
| 2022-12-14T22:42:08+00:00 | {"license": "afl-3.0"} | 2022-12-14T22:42:08+00:00 |
|
11bb592cc69d61a07715a9dccbc2b9271607c6cc | # Dataset Card for "fno-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dahoas/fno-cifar10-32 | [
"region:us"
]
| 2022-12-15T01:38:57+00:00 | {"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 635009024, "num_examples": 50048}], "download_size": 647482139, "dataset_size": 635009024}} | 2022-12-15T01:39:28+00:00 |
023ab9693161136e7689ce1cbd03ccd346dcb7c5 | The data is originally source from (Sun et al,2021). (Liu et al, 2023) processed the data to make it a dataset vis huggingface api with taining/validation/testing splitting
**Please cite:**
```
@misc{liu2023enhancing,
title={Enhancing Long-form Text Generation in Mental Health with Task-adaptive Tokenization},
author={Siyang Liu and Naihao Deng and Sahand Sabour and Yilin Jia and Minlie Huang and Rada Mihalcea},
year={2023},
eprint={2310.05317},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@inproceedings{sun2021psyqa,
title={PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support},
author={Sun, Hao and Lin, Zhenru and Zheng, Chujie and Liu, Siyang and Huang, Minlie},
booktitle={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021},
pages={1489--1503},
year={2021}
}
``` | lsy641/PsyQA | [
"license:mit",
"arxiv:2310.05317",
"region:us"
]
| 2022-12-15T01:55:23+00:00 | {"license": "mit"} | 2023-10-23T09:00:46+00:00 |
a0034cf69869dcd317ccfa67e8ec47ea1a2a54f8 | # Dataset Card for "dual-fno-cifar10-32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Dahoas/dual-fno-cifar10-32 | [
"region:us"
]
| 2022-12-15T02:02:00+00:00 | {"dataset_info": {"features": [{"name": "images", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 635009024, "num_examples": 50048}], "download_size": 647528662, "dataset_size": 635009024}} | 2022-12-15T02:02:31+00:00 |
21377edbe610bf92d12f3cc4f945a5c218c9e450 | JJJJKKKKK/MMMMMM | [
"license:afl-3.0",
"region:us"
]
| 2022-12-15T02:03:13+00:00 | {"license": "afl-3.0"} | 2022-12-15T02:03:13+00:00 |
|
d71d866febb1f5864ea1379cc3e9524f59cb0b85 | 737-Lab/WebUiImprove | [
"license:openrail",
"region:us"
]
| 2022-12-15T02:40:37+00:00 | {"license": "openrail"} | 2022-12-15T02:40:37+00:00 |
|
8eb6dce316a8cfbbd80a162685e77a9f19fbb07d | # Dataset Card for "results_valid_10rows_2022-12-15"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joddy/results_valid_10rows_2022-12-15 | [
"region:us"
]
| 2022-12-15T03:51:04+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "resolution", "dtype": "int64"}, {"name": "attributes_loc", "dtype": {"class_label": {"names": {"0": "upper left", "1": "upper right", "2": "lower left", "3": "lower right"}}}}, {"name": "NL_text", "dtype": "string"}, {"name": "bbox_text", "dtype": "string"}, {"name": "center_text", "dtype": "string"}, {"name": "normed_object_bbox", "sequence": "int64"}, {"name": "NL_image", "dtype": "image"}, {"name": "bbox_image", "dtype": "image"}, {"name": "center_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 16024442.0, "num_examples": 10}], "download_size": 16030250, "dataset_size": 16024442.0}} | 2022-12-15T03:53:50+00:00 |
f846acd02f8f906b6d88ee6cb63a927294a20b7b | # Dataset Card for "AToMiC-Texts-Dedup"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | justram/AToMiC-Texts-Dedup | [
"region:us"
]
| 2022-12-15T04:56:17+00:00 | {"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "text_id", "dtype": "string"}, {"name": "page_url", "dtype": "string"}, {"name": "page_title", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "hierarchical_section_title", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4768023667.871489, "num_examples": 3220639}, {"name": "validation", "num_bytes": 35066965.650891684, "num_examples": 21466}, {"name": "test", "num_bytes": 26076287.261490725, "num_examples": 16362}], "download_size": 2976408849, "dataset_size": 4829166920.783871}} | 2022-12-15T05:01:31+00:00 |
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