Datasets:
ftopal
/
huggingface-datasets-raw

Dataset card Data Studio Files Community
sha
stringlengths
40
40
text
stringlengths
0
13.4M
id
stringlengths
2
117
tags
list
created_at
stringlengths
25
25
metadata
stringlengths
2
31.7M
last_modified
stringlengths
25
25
8c3129dae77798b5f4de1f8d6e9bb7ff4494cb16
Shootertrex/Kagari-Atsuko
[ "license:wtfpl", "region:us" ]
2022-12-30T03:48:08+00:00
{"license": "wtfpl"}
2022-12-30T03:49:18+00:00
84ae8c39c5e9c128cb599310caf439dc9d347284
MuraliGanesan/LayoutLM_Training_dataset
[ "license:afl-3.0", "region:us" ]
2022-12-30T04:35:38+00:00
{"license": "afl-3.0"}
2022-12-30T04:38:20+00:00
88c67d93f2665c1158e6506f9b1c16264c1c4bff
<div align="center"> <img width="640" alt="keremberke/clash-of-clans-object-detection" src="https://huggingface.co/datasets/keremberke/clash-of-clans-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['ad', 'airsweeper', 'bombtower', 'canon', 'clancastle', 'eagle', 'inferno', 'kingpad', 'mortar', 'queenpad', 'rcpad', 'scattershot', 'th13', 'wardenpad', 'wizztower', 'xbow'] ``` ### Number of Images ```json {'train': 88, 'test': 13, 'valid': 24} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/clash-of-clans-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5](https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y/dataset/5?ref=roboflow2huggingface?ref=roboflow2huggingface) ### Citation ``` @misc{ clash-of-clans-vop4y_dataset, title = { Clash of Clans Dataset }, type = { Open Source Dataset }, author = { Find This Base }, howpublished = { \\url{ https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y } }, url = { https://universe.roboflow.com/find-this-base/clash-of-clans-vop4y }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { feb }, note = { visited on 2023-01-18 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on March 30, 2022 at 4:31 PM GMT It includes 125 images. CoC are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 1920x1920 (Fit (black edges)) No image augmentation techniques were applied.
keremberke/clash-of-clans-object-detection
[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "Gaming", "region:us" ]
2022-12-30T05:14:59+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Gaming"]}
2023-01-29T12:38:03+00:00
0fff4b199b05bc873629304f05dd529028261449
rodo1985/montserrat_mountain_dataset
[ "license:other", "region:us" ]
2022-12-30T08:39:16+00:00
{"license": "other"}
2022-12-30T08:46:05+00:00
e2d790297be5d87417a993f3b1c733abed31b906
# Dataset Card for "portraits-512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
conorcl/portraits-512
[ "region:us" ]
2022-12-30T09:03:33+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 83939067.61, "num_examples": 2917}], "download_size": 83808019, "dataset_size": 83939067.61}}
2022-12-30T09:04:11+00:00
e6fbaebf9a321e063345318d5afc305afb6ea187
# Dataset Card for "dataset_nautical" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
alvarochelo/dataset_nautical
[ "region:us" ]
2022-12-30T09:22:55+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 908645884.0, "num_examples": 239}], "download_size": 875628182, "dataset_size": 908645884.0}}
2023-03-06T23:03:46+00:00
1efd45608bff8f2d9a8fffcc4baebc201f499375
# Dataset Card for "results_valid_100rows_2022-12-30" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
joddy/results_valid_100rows_2022-12-30
[ "region:us" ]
2022-12-30T09:45:54+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": "without_pos_stable-diffusion-v1-5", "dtype": "image"}, {"name": "NL_stable-diffusion-v1-5", "dtype": "image"}, {"name": "bbox_stable-diffusion-v1-5", "dtype": "image"}, {"name": "center_stable-diffusion-v1-5", "dtype": "image"}, {"name": "without_pos_NL_text_TextENC_off", "dtype": "image"}, {"name": "NL_text_TextENC_off", "dtype": "image"}, {"name": "without_pos_bbox_text_TextENC_off", "dtype": "image"}, {"name": "bbox_text_TextENC_off", "dtype": "image"}, {"name": "without_pos_center_text_TextENC_off", "dtype": "image"}, {"name": "center_text_TextENC_off", "dtype": "image"}, {"name": "without_pos_NL_text_TextENC_on", "dtype": "image"}, {"name": "NL_text_TextENC_on", "dtype": "image"}, {"name": "without_pos_bbox_text_TextENC_on", "dtype": "image"}, {"name": "bbox_text_TextENC_on", "dtype": "image"}, {"name": "without_pos_center_text_TextENC_on", "dtype": "image"}, {"name": "center_text_TextENC_on", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 792511070.0, "num_examples": 100}], "download_size": 784909101, "dataset_size": 792511070.0}}
2022-12-30T09:55:44+00:00
409e22daa73895d32b243768fc4015cb72a2c476
# Dataset Card for "dreambooth-hackathon-losie" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
misza222/dreambooth-hackathon-losie
[ "region:us" ]
2022-12-30T10:00:39+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 435582.0, "num_examples": 5}], "download_size": 436362, "dataset_size": 435582.0}}
2022-12-30T10:00:46+00:00
4dbc4d772167de2dad8d9e5026808d0dc7933447
<div align="center"> <img width="640" alt="keremberke/nfl-object-detection" src="https://huggingface.co/datasets/keremberke/nfl-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline'] ``` ### Number of Images ```json {'valid': 1989, 'train': 6963, 'test': 995} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/nfl-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1](https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1?ref=roboflow2huggingface?ref=roboflow2huggingface) ### Citation ``` @misc{ nfl-competition_dataset, title = { NFL-competition Dataset }, type = { Open Source Dataset }, author = { home }, howpublished = { \\url{ https://universe.roboflow.com/home-mxzv1/nfl-competition } }, url = { https://universe.roboflow.com/home-mxzv1/nfl-competition }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { sep }, note = { visited on 2023-01-18 }, } ``` ### License Public Domain ### Dataset Summary This dataset was exported via roboflow.com on December 29, 2022 at 8:12 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 9947 images. Helmets are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 1280x720 (Stretch) No image augmentation techniques were applied.
keremberke/nfl-object-detection
[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "region:us" ]
2022-12-30T10:37:59+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface"]}
2023-01-29T12:37:17+00:00
89ebf3499f500a90d8820d2296432a6e0595faa1
ๅธฆๆ ‡ๆณจ็š„ๆŠฝ่ฑกๆ•ฐๆฎ้›† ่ฎญ็ปƒdiffsingerไฝฟ็”จ tts่ฎญ็ปƒๆญฃๅœจๅˆถไฝœ
funnymdzz/diffsinger-chuansao258
[ "license:cc-by-sa-4.0", "region:us" ]
2022-12-30T13:53:15+00:00
{"license": "cc-by-sa-4.0"}
2023-04-27T15:55:20+00:00
113bf5b61fbf4685b48fb9392ffdbf911426f5ae
this might be only funk. when I say codes I mean musenet encoding. i make all of my datasets by hand.
breadlicker45/big-midi-codes
[ "license:other", "region:us" ]
2022-12-30T14:09:50+00:00
{"license": "other"}
2023-01-14T22:52:51+00:00
8b21561b807a5a88dfd86b06c05ec9bce0dac69f
MaxP/agro_riego
[ "license:unknown", "region:us" ]
2022-12-30T14:52:55+00:00
{"license": "unknown"}
2023-03-13T18:27:31+00:00
ba25faa3e1fbb728f95d23c4015d47728d935839
# Dataset Card for "jurassic-coast" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
harveymannering/jurassic-coast
[ "region:us" ]
2022-12-30T15:57:45+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 32984479.0, "num_examples": 14}], "download_size": 32973035, "dataset_size": 32984479.0}}
2022-12-30T15:57:52+00:00
07abf67aa6f1b3f053327405bbada4ac9d85a3bb
# test
nayanah/os_names
[ "region:us" ]
2022-12-30T16:50:34+00:00
{}
2022-12-30T16:53:00+00:00
7e90f0f342569b35213445f809cfaf3b91f9964f
# Dataset Card for [EDGAR-CORPUS] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [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) - [Licensing Information](#licensing-information) - [References](#references) - [Contributions](#contributions) ## Dataset Description - **Point of Contact: Lefteris Loukas** ### Dataset Summary This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop. This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings. There is supported functionality to load a specific year. Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits. If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler. ## Citation If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic): ``` @inproceedings{loukas-etal-2021-edgar, title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round", author = "Loukas, Lefteris and Fergadiotis, Manos and Androutsopoulos, Ion and Malakasiotis, Prodromos", booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.econlp-1.2", pages = "13--18", } ``` ### Supported Tasks This is a raw dataset/corpus for financial NLP. As such, there are no annotations or labels. ### Languages The EDGAR Filings are in English. ## Dataset Structure ### Data Instances Refer to the dataset preview. ### Data Fields **filename**: Name of file on EDGAR from which the report was extracted.<br> **cik**: EDGAR identifier for a firm.<br> **year**: Year of report.<br> **section_1**: Corressponding section of the Annual Report.<br> **section_1A**: Corressponding section of the Annual Report.<br> **section_1B**: Corressponding section of the Annual Report.<br> **section_2**: Corressponding section of the Annual Report.<br> **section_3**: Corressponding section of the Annual Report.<br> **section_4**: Corressponding section of the Annual Report.<br> **section_5**: Corressponding section of the Annual Report.<br> **section_6**: Corressponding section of the Annual Report.<br> **section_7**: Corressponding section of the Annual Report.<br> **section_7A**: Corressponding section of the Annual Report.<br> **section_8**: Corressponding section of the Annual Report.<br> **section_9**: Corressponding section of the Annual Report.<br> **section_9A**: Corressponding section of the Annual Report.<br> **section_9B**: Corressponding section of the Annual Report.<br> **section_10**: Corressponding section of the Annual Report.<br> **section_11**: Corressponding section of the Annual Report.<br> **section_12**: Corressponding section of the Annual Report.<br> **section_13**: Corressponding section of the Annual Report.<br> **section_14**: Corressponding section of the Annual Report.<br> **section_15**: Corressponding section of the Annual Report.<br> ```python import datasets # Load the entire dataset raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full") # Load a specific year and split year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train") ``` ### Data Splits | Config | Training | Validation | Test | | --------- | -------- | ---------- | ------ | | full | 176,289 | 22,050 | 22,036 | | year_1993 | 1,060 | 133 | 133 | | year_1994 | 2,083 | 261 | 260 | | year_1995 | 4,110 | 514 | 514 | | year_1996 | 7,589 | 949 | 949 | | year_1997 | 8,084 | 1,011 | 1,011 | | year_1998 | 8,040 | 1,006 | 1,005 | | year_1999 | 7,864 | 984 | 983 | | year_2000 | 7,589 | 949 | 949 | | year_2001 | 7,181 | 898 | 898 | | year_2002 | 6,636 | 830 | 829 | | year_2003 | 6,672 | 834 | 834 | | year_2004 | 7,111 | 889 | 889 | | year_2005 | 7,113 | 890 | 889 | | year_2006 | 7,064 | 883 | 883 | | year_2007 | 6,683 | 836 | 835 | | year_2008 | 7,408 | 927 | 926 | | year_2009 | 7,336 | 917 | 917 | | year_2010 | 7,013 | 877 | 877 | | year_2011 | 6,724 | 841 | 840 | | year_2012 | 6,479 | 810 | 810 | | year_2013 | 6,372 | 797 | 796 | | year_2014 | 6,261 | 783 | 783 | | year_2015 | 6,028 | 754 | 753 | | year_2016 | 5,812 | 727 | 727 | | year_2017 | 5,635 | 705 | 704 | | year_2018 | 5,508 | 689 | 688 | | year_2019 | 5,354 | 670 | 669 | | year_2020 | 5,480 | 686 | 685 | ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**. #### Who are the source language producers? Public firms filing with the SEC. ### Annotations #### Annotation process NA #### Who are the annotators? NA ### Personal and Sensitive Information The dataset contains public filings data from SEC. ## Considerations for Using the Data ### Social Impact of Dataset Low to none. ### Discussion of Biases The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature. ### Other Known Limitations The dataset needs further cleaning for improved performance. ## Additional Information ### Licensing Information EDGAR data is publicly available. ### Shoutout Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup! ### References - [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021. - [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021) - [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021) - [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021) - [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022)
eloukas/edgar-corpus
[ "task_categories:other", "annotations_creators:no-annotation", "language_creators:other", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:extended|other", "language:en", "license:apache-2.0", "research papers", "edgar", "sec", "finance", "financial", "filings", "10K", "10-K", "nlp", "research", "econlp", "economics", "business", "arxiv:2109.14394", "region:us" ]
2022-12-30T16:55:08+00:00
{"annotations_creators": ["no-annotation"], "language_creators": ["other"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["extended|other"], "task_categories": ["other"], "task_ids": [], "pretty_name": "EDGAR-CORPUS (10-K Filings from 1999 to 2020)", "dataset_info": [{"config_name": ".", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 40306320885, "num_examples": 220375}], "download_size": 10734208660, "dataset_size": 40306320885}, {"config_name": "full", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32237457024, "num_examples": 176289}, {"name": "validation", "num_bytes": 4023129683, "num_examples": 22050}, {"name": "test", "num_bytes": 4045734178, "num_examples": 22036}], "download_size": 40699852536, "dataset_size": 40306320885}, {"config_name": "year_1993", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 112714537, "num_examples": 1060}, {"name": "validation", "num_bytes": 13584432, "num_examples": 133}, {"name": "test", "num_bytes": 14520566, "num_examples": 133}], "download_size": 141862572, "dataset_size": 140819535}, {"config_name": "year_1994", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 198955093, "num_examples": 2083}, {"name": "validation", "num_bytes": 23432307, "num_examples": 261}, {"name": "test", "num_bytes": 26115768, "num_examples": 260}], "download_size": 250411041, "dataset_size": 248503168}, {"config_name": "year_1995", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 356959049, "num_examples": 4110}, {"name": "validation", "num_bytes": 42781161, "num_examples": 514}, {"name": "test", "num_bytes": 45275568, "num_examples": 514}], "download_size": 448617549, "dataset_size": 445015778}, {"config_name": "year_1996", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 738506135, "num_examples": 7589}, {"name": "validation", "num_bytes": 89873905, "num_examples": 949}, {"name": "test", "num_bytes": 91248882, "num_examples": 949}], "download_size": 926536700, "dataset_size": 919628922}, {"config_name": "year_1997", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 854201733, "num_examples": 8084}, {"name": "validation", "num_bytes": 103167272, "num_examples": 1011}, {"name": "test", "num_bytes": 106843950, "num_examples": 1011}], "download_size": 1071898139, "dataset_size": 1064212955}, {"config_name": "year_1998", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 904075497, "num_examples": 8040}, {"name": "validation", "num_bytes": 112630658, "num_examples": 1006}, {"name": "test", "num_bytes": 113308750, "num_examples": 1005}], "download_size": 1137887615, "dataset_size": 1130014905}, {"config_name": "year_1999", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 911374885, "num_examples": 7864}, {"name": "validation", "num_bytes": 118614261, "num_examples": 984}, {"name": "test", "num_bytes": 116706581, "num_examples": 983}], "download_size": 1154736765, "dataset_size": 1146695727}, {"config_name": "year_2000", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 926444625, "num_examples": 7589}, {"name": "validation", "num_bytes": 113264749, "num_examples": 949}, {"name": "test", "num_bytes": 114605470, "num_examples": 949}], "download_size": 1162526814, "dataset_size": 1154314844}, {"config_name": "year_2001", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 964631161, "num_examples": 7181}, {"name": "validation", "num_bytes": 117509010, "num_examples": 898}, {"name": "test", "num_bytes": 116141097, "num_examples": 898}], "download_size": 1207790205, "dataset_size": 1198281268}, {"config_name": "year_2002", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1049271720, "num_examples": 6636}, {"name": "validation", "num_bytes": 128339491, "num_examples": 830}, {"name": "test", "num_bytes": 128444184, "num_examples": 829}], "download_size": 1317817728, "dataset_size": 1306055395}, {"config_name": "year_2003", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1027557690, "num_examples": 6672}, {"name": "validation", "num_bytes": 126684704, "num_examples": 834}, {"name": "test", "num_bytes": 130672979, "num_examples": 834}], "download_size": 1297227566, "dataset_size": 1284915373}, {"config_name": "year_2004", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1129657843, "num_examples": 7111}, {"name": "validation", "num_bytes": 147499772, "num_examples": 889}, {"name": "test", "num_bytes": 147890092, "num_examples": 889}], "download_size": 1439663100, "dataset_size": 1425047707}, {"config_name": "year_2005", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1200714441, "num_examples": 7113}, {"name": "validation", "num_bytes": 161003977, "num_examples": 890}, {"name": "test", "num_bytes": 160727195, "num_examples": 889}], "download_size": 1538876195, "dataset_size": 1522445613}, {"config_name": "year_2006", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1286566049, "num_examples": 7064}, {"name": "validation", "num_bytes": 160843494, "num_examples": 883}, {"name": "test", "num_bytes": 163270601, "num_examples": 883}], "download_size": 1628452618, "dataset_size": 1610680144}, {"config_name": "year_2007", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1296737173, "num_examples": 6683}, {"name": "validation", "num_bytes": 166735560, "num_examples": 836}, {"name": "test", "num_bytes": 156399535, "num_examples": 835}], "download_size": 1637502176, "dataset_size": 1619872268}, {"config_name": "year_2008", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1525698198, "num_examples": 7408}, {"name": "validation", "num_bytes": 190034435, "num_examples": 927}, {"name": "test", "num_bytes": 187659976, "num_examples": 926}], "download_size": 1924164839, "dataset_size": 1903392609}, {"config_name": "year_2009", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1547816260, "num_examples": 7336}, {"name": "validation", "num_bytes": 188897783, "num_examples": 917}, {"name": "test", "num_bytes": 196463897, "num_examples": 917}], "download_size": 1954076983, "dataset_size": 1933177940}, {"config_name": "year_2010", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1493505900, "num_examples": 7013}, {"name": "validation", "num_bytes": 192695567, "num_examples": 877}, {"name": "test", "num_bytes": 191482640, "num_examples": 877}], "download_size": 1897687327, "dataset_size": 1877684107}, {"config_name": "year_2011", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1481486551, "num_examples": 6724}, {"name": "validation", "num_bytes": 190781558, "num_examples": 841}, {"name": "test", "num_bytes": 185869151, "num_examples": 840}], "download_size": 1877396421, "dataset_size": 1858137260}, {"config_name": "year_2012", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1463496224, "num_examples": 6479}, {"name": "validation", "num_bytes": 186247306, "num_examples": 810}, {"name": "test", "num_bytes": 185923601, "num_examples": 810}], "download_size": 1854377191, "dataset_size": 1835667131}, {"config_name": "year_2013", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1468172419, "num_examples": 6372}, {"name": "validation", "num_bytes": 183570866, "num_examples": 797}, {"name": "test", "num_bytes": 182495750, "num_examples": 796}], "download_size": 1852839009, "dataset_size": 1834239035}, {"config_name": "year_2014", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1499451593, "num_examples": 6261}, {"name": "validation", "num_bytes": 181568907, "num_examples": 783}, {"name": "test", "num_bytes": 181046535, "num_examples": 783}], "download_size": 1880963095, "dataset_size": 1862067035}, {"config_name": "year_2015", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1472346721, "num_examples": 6028}, {"name": "validation", "num_bytes": 180128910, "num_examples": 754}, {"name": "test", "num_bytes": 189210252, "num_examples": 753}], "download_size": 1860303134, "dataset_size": 1841685883}, {"config_name": "year_2016", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1471605426, "num_examples": 5812}, {"name": "validation", "num_bytes": 178310005, "num_examples": 727}, {"name": "test", "num_bytes": 177481471, "num_examples": 727}], "download_size": 1845967492, "dataset_size": 1827396902}, {"config_name": "year_2017", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1459021126, "num_examples": 5635}, {"name": "validation", "num_bytes": 174360913, "num_examples": 705}, {"name": "test", "num_bytes": 184398250, "num_examples": 704}], "download_size": 1836306408, "dataset_size": 1817780289}, {"config_name": "year_2018", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1433409319, "num_examples": 5508}, {"name": "validation", "num_bytes": 181466460, "num_examples": 689}, {"name": "test", "num_bytes": 182594965, "num_examples": 688}], "download_size": 1815810567, "dataset_size": 1797470744}, {"config_name": "year_2019", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1421232269, "num_examples": 5354}, {"name": "validation", "num_bytes": 175603562, "num_examples": 670}, {"name": "test", "num_bytes": 176336174, "num_examples": 669}], "download_size": 1791237155, "dataset_size": 1773172005}, {"config_name": "year_2020", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1541847387, "num_examples": 5480}, {"name": "validation", "num_bytes": 193498658, "num_examples": 686}, {"name": "test", "num_bytes": 192600298, "num_examples": 685}], "download_size": 1946916132, "dataset_size": 1927946343}], "tags": ["research papers", "edgar", "sec", "finance", "financial", "filings", "10K", "10-K", "nlp", "research", "econlp", "economics", "business"]}
2023-07-14T06:17:12+00:00
a320fae2cd84f04610a1b57ac83c2a2f674b8bcd
CarperAI/openai_summarize_comparisons
[ "region:us" ]
2022-12-30T17:28:16+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 143018505, "num_examples": 83629}, {"name": "train", "num_bytes": 157425966, "num_examples": 92534}, {"name": "valid1", "num_bytes": 56686271, "num_examples": 33082}, {"name": "valid2", "num_bytes": 86396487, "num_examples": 50715}], "download_size": 20257716, "dataset_size": 443527229}}
2023-02-27T16:29:07+00:00
6ecf4b7cec4f3b5de2c59e2a7053cb08a650cb83
ultracreate/uldata
[ "license:mit", "region:us" ]
2022-12-30T17:45:41+00:00
{"license": "mit"}
2022-12-30T17:45:41+00:00
851e8810714890d615ecd10e0fc225e2db0e127c
# emo
Hosswin/Self
[ "region:us" ]
2022-12-30T17:50:28+00:00
{}
2022-12-30T17:54:39+00:00
a6ea0aadd1bb2f929384443c672f45ac16ed7b04
# Dataset Card for Genshin Voice ## Dataset Description ### Dataset Summary The Genshin Voice dataset is a text-to-voice dataset of different Genshin Impact characters unpacked from the game. ### Languages The text in the dataset is in Mandarin. ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization The data was obtained by unpacking the [Genshin Impact](https://genshin.hoyoverse.com/) game. #### Who are the source language producers? The language producers are the employee of [Hoyoverse](https://hoyoverse.com/) and contractors from [EchoSky Studio](http://qx.asiacu.com/). ### Annotations The dataset contains official annotations from the game, including ingame speaker name and transcripts. ## Additional Information ### Dataset Curators The dataset was created by [w4123](https://github.com/w4123) initially in his [GitHub repository](https://github.com/w4123/GenshinVoice). ### Licensing Information Copyright ยฉ COGNOSPHERE. All Rights Reserved.
hanamizuki-ai/genshin-voice-v3.3-mandarin
[ "task_categories:text-to-speech", "task_categories:automatic-speech-recognition", "multilinguality:monolingual", "source_datasets:original", "language:zh", "region:us" ]
2022-12-30T18:13:13+00:00
{"language": ["zh"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-to-speech", "automatic-speech-recognition"], "pretty_name": "Genshin Voice", "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "language", "dtype": "string"}, {"name": "npcName", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 36412736429.25, "num_examples": 75033}], "download_size": 18251937481, "dataset_size": 36412736429.25}}
2022-12-31T05:01:47+00:00
e4c525ff05693d2b7c9cc3ffd6c25e820bec7b66
a
nayanah/os_cat_2
[ "region:us" ]
2022-12-30T18:39:58+00:00
{}
2022-12-30T18:40:28+00:00
2cb865a138cb7251be9e3841beeee14d296db950
# Dataset Card for "artist-lyrics" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
BhavyaMuni/artist-lyrics
[ "region:us" ]
2022-12-30T19:06:53+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "beyonce", "num_bytes": 593257, "num_examples": 17188}, {"name": "sia", "num_bytes": 290482, "num_examples": 8410}, {"name": "anitta", "num_bytes": 52302, "num_examples": 1536}, {"name": "adele", "num_bytes": 123431, "num_examples": 3719}, {"name": "eminem", "num_bytes": 1884010, "num_examples": 43830}, {"name": "ed_sheeran", "num_bytes": 437717, "num_examples": 11731}, {"name": "coldplay", "num_bytes": 229712, "num_examples": 7037}, {"name": "pink", "num_bytes": 338827, "num_examples": 9922}, {"name": "taylor_swift", "num_bytes": 696163, "num_examples": 19203}, {"name": "imagine_dragons", "num_bytes": 213012, "num_examples": 6208}, {"name": "justin_bieber", "num_bytes": 550768, "num_examples": 15086}, {"name": "ludmilla", "num_bytes": 826, "num_examples": 24}, {"name": "the_beatles", "num_bytes": 298451, "num_examples": 8894}, {"name": "maroon_5", "num_bytes": 296992, "num_examples": 8401}, {"name": "bruno_mars", "num_bytes": 241371, "num_examples": 6831}, {"name": "lady_gaga", "num_bytes": 495013, "num_examples": 14949}, {"name": "lana_del_rey", "num_bytes": 518382, "num_examples": 14768}, {"name": "ariana_grande", "num_bytes": 352469, "num_examples": 10024}, {"name": "christina_perri", "num_bytes": 81053, "num_examples": 2358}, {"name": "phil_collins", "num_bytes": 180491, "num_examples": 4718}, {"name": "rihanna", "num_bytes": 524927, "num_examples": 15505}, {"name": "camila_cabello", "num_bytes": 147677, "num_examples": 4137}, {"name": "bon_jovi", "num_bytes": 550018, "num_examples": 15139}, {"name": "elton_john", "num_bytes": 656548, "num_examples": 17599}, {"name": "john_legend", "num_bytes": 266362, "num_examples": 7744}, {"name": "john_lennon", "num_bytes": 128386, "num_examples": 3685}, {"name": "pink_floyd", "num_bytes": 164745, "num_examples": 4588}, {"name": "scorpions", "num_bytes": 293093, "num_examples": 8990}, {"name": "red_hot_chili_peppers", "num_bytes": 365278, "num_examples": 11353}, {"name": "50_cent", "num_bytes": 1371989, "num_examples": 32353}, {"name": "nirvana", "num_bytes": 103195, "num_examples": 3345}, {"name": "queen", "num_bytes": 271145, "num_examples": 8132}, {"name": "katy_perry", "num_bytes": 348706, "num_examples": 10383}, {"name": "alok", "num_bytes": 67991, "num_examples": 2115}, {"name": "u2", "num_bytes": 402969, "num_examples": 12790}, {"name": "black_eyed_peas", "num_bytes": 445727, "num_examples": 12127}, {"name": "michael_jackson", "num_bytes": 529153, "num_examples": 16749}, {"name": "jason_mraz", "num_bytes": 381834, "num_examples": 10153}, {"name": "guns_n_roses", "num_bytes": 177135, "num_examples": 5120}, {"name": "alicia_keys", "num_bytes": 330863, "num_examples": 9934}, {"name": "rammstein", "num_bytes": 56457, "num_examples": 1973}, {"name": "shawn_mendes", "num_bytes": 156939, "num_examples": 4398}, {"name": "linkin_park", "num_bytes": 331637, "num_examples": 9580}, {"name": "shakira", "num_bytes": 136600, "num_examples": 4227}], "download_size": 7993813, "dataset_size": 16104173}}
2023-01-02T00:29:03+00:00
e4f15c21606feeca3c7e74f036973deee48a77f6
# Dataset Card for "bookcorpus_small_compact_512_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_small_compact_512_meta
[ "region:us" ]
2022-12-30T20:09:23+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 208307299, "num_examples": 3109}], "download_size": 0, "dataset_size": 208307299}}
2023-01-21T13:53:58+00:00
33d0001c61dcf56673e4aeaa7a871de5d4032123
# Dataset Card for "test-squad" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
susnato/test-squad
[ "region:us" ]
2022-12-30T20:09:59+00:00
{"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"}]}], "splits": [{"name": "train", "num_bytes": 79346108, "num_examples": 87599}], "download_size": 0, "dataset_size": 79346108}}
2022-12-30T20:13:38+00:00
176a3f11b7ef453947b486c1de843068d108acef
# Dataset Card for LegalCaseDocumentSummarization ## 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:** [GitHub](https://github.com/Law-AI/summarization) - **Repository:** [Zenodo](https://zenodo.org/record/7152317#.Y69PkeKZODW) - **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 [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
joelniklaus/legal_case_document_summarization
[ "region:us" ]
2022-12-30T20:54:10+00:00
{}
2023-02-02T23:52:54+00:00
7c48587c8ed03edde3184cf7e8dc55b271bf1a90
# Dataset Card for PlainEnglishContractsSummarization ## 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:** [GitHub](https://github.com/lauramanor/legal_summarization) - **Repository:** - **Paper:** [ACL Anthology](https://aclanthology.org/W19-2201/) - **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 [@JoelNiklaus](https://github.com/JoelNiklaus) for adding this dataset.
joelniklaus/plain_english_contracts_summarization
[ "region:us" ]
2022-12-30T22:17:07+00:00
{}
2022-12-30T22:18:13+00:00
1d8cf8814f8fdb9b08470405566ba5b3ae34ee28
# Dataset Card for "OxfordPets_embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/OxfordPets_embeddings
[ "region:us" ]
2022-12-30T22:23:44+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "vision_embeddings", "sequence": "float32"}], "splits": [{"name": "test", "num_bytes": 420471647.375, "num_examples": 3669}], "download_size": 0, "dataset_size": 420471647.375}}
2022-12-30T22:27:08+00:00
e54d18c59edf439d42ce70ebfa60758f12c1d964
Andregomes/nlpC
[ "license:bigscience-openrail-m", "region:us" ]
2022-12-31T01:32:20+00:00
{"license": "bigscience-openrail-m"}
2022-12-31T01:33:10+00:00
45cdf492a3856e2af2c3b5338d1aa9b6e1664ee0
abross/channel-metadata
[ "license:afl-3.0", "region:us" ]
2022-12-31T01:46:47+00:00
{"license": "afl-3.0"}
2023-03-15T21:08:00+00:00
b341471b36df0ce4a390d10cecc2c07292576853
syzym/muc
[ "license:apache-2.0", "region:us" ]
2022-12-31T04:11:37+00:00
{"license": "apache-2.0"}
2022-12-31T05:05:28+00:00
4b619bc005616bb21a040001500f322fc37582e1
memray/kp20k
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T04:55:49+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T05:02:43+00:00
abb0cc0417dba1d665a16f439dd2ad7fd3dd672d
memray/kptimes
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T05:19:09+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T05:28:03+00:00
1d44757edfca943fe9a220fe032ca083f91a83de
xhxhkxh/test
[ "license:cc0-1.0", "region:us" ]
2022-12-31T05:39:42+00:00
{"license": "cc0-1.0"}
2022-12-31T06:48:34+00:00
ce8adbb0ec3a280260a3940ebfc35c684dd8d4f7
memray/openkp
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T05:57:58+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:04:30+00:00
ec922df510368d1bab299d0ec8ba1611aae282bc
memray/inspec
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:11:50+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:12:06+00:00
32d1d7d90e3f848c0457cd24c2664d7f24c73657
memray/duc
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:12:22+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:12:38+00:00
fe87f812b9a00aec590b6ec98726d5357db07c73
memray/krapivin
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:13:03+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:14:07+00:00
53b90ea8af7313b1807141b626dd21b178903418
memray/nus
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:15:13+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:15:39+00:00
b14b2a355d25bdcb6dbecb7426f0b2a75605a55c
memray/semeval
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:15:55+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:16:14+00:00
37d8cb641f8575877b0c8c3f290c012788c0cf41
memray/jptimes
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:17:30+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:18:02+00:00
8730cb9c6b4943897aea1bca47ec138bb22fd468
memray/stackexchange
[ "license:cc-by-nc-sa-4.0", "region:us" ]
2022-12-31T06:21:28+00:00
{"license": "cc-by-nc-sa-4.0"}
2022-12-31T06:28:23+00:00
7d7e8cb67255109f01cdcea65a2c2a39fc3d985e
Glac1er/MyStuffs
[ "license:unknown", "region:us" ]
2022-12-31T06:39:15+00:00
{"license": "unknown"}
2023-07-10T03:30:58+00:00
11a0d126e2b4b2eb2160ba23fda768af1866354b
fmattera/belize-blue-sofa
[ "region:us" ]
2022-12-31T10:21:23+00:00
{}
2022-12-31T10:21:50+00:00
1abc2bdfb7483ae1ae130b3ad770855f8b558621
annotations_creators: - machine-generated language: - en - ar language_creators: - machine-generated license: [] multilinguality: - translation pretty_name: Arabic_English Corpus size_categories: - 1M<n<10M source_datasets: [] tags: - translation task_categories: - translation task_ids: []
NadiaHassan/ar-en
[ "region:us" ]
2022-12-31T11:33:04+00:00
{}
2022-12-31T11:38:47+00:00
751c312fb6d7ab22cdca047299e43ffafe1d8f80
# Dataset Card for "pochita_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Arch4ngel/pochita_v2
[ "region:us" ]
2022-12-31T14:35:38+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 67970413.0, "num_examples": 15}], "download_size": 67840616, "dataset_size": 67970413.0}}
2022-12-31T14:35:44+00:00
f897da373d352da035f31042189338fc6ff36538
# Dataset Card for "bookcorpus_small_compact_256_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_small_compact_256_meta
[ "region:us" ]
2022-12-31T15:07:16+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 213919213, "num_examples": 6104}], "download_size": 45654115, "dataset_size": 213919213}}
2023-01-19T09:05:29+00:00
85d076ab1b8c03532c344c35243833ea8181899c
# Hyperpartisan news detection This dataset has the hyperpartisan new dataset, processed and split exactly as it was for [longformer](https://arxiv.org/abs/2004.05150) experiments. Code for processing was found at [here](https://github.com/allenai/longformer/blob/master/scripts/hp_preprocess.py).
jonathanli/hyperpartisan-longformer-split
[ "arxiv:2004.05150", "region:us" ]
2022-12-31T15:56:50+00:00
{}
2022-12-31T16:08:16+00:00
2642a7288f0d213452e8398ded8a975900c29d91
# Dataset Card for "bookcorpus_small_compact_1024_meta" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
saibo/bookcorpus_small_compact_1024_meta
[ "region:us" ]
2022-12-31T17:19:28+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 192026469, "num_examples": 1571}], "download_size": 0, "dataset_size": 192026469}}
2023-01-25T17:23:03+00:00
95cc957003d83e71729dacd8230acb1d27f7a13f
Railguner34/Yest
[ "license:openrail", "region:us" ]
2022-12-31T17:26:32+00:00
{"license": "openrail"}
2022-12-31T17:26:32+00:00
d93709f79aa79691708f7635be30eaac4fbf631d
damilojohn/Personal_Playlist_Generator
[ "license:mit", "region:us" ]
2022-12-31T18:22:54+00:00
{"license": "mit"}
2023-02-18T15:11:09+00:00
c40df9780a6b5864435f4294b0790024ff610a60
annotations_creators: - no-annotation language: [] language_creators: - other license: - afl-3.0 multilinguality: [] pretty_name: pishi size_categories: - n<1K source_datasets: - original tags: - '''cat''' task_categories: - text-to-image task_ids: []
Atallahw/pishi
[ "region:us" ]
2022-12-31T18:53:11+00:00
{}
2022-12-31T19:05:45+00:00
6b231df0229cd49fd2005f0244b55cfb1e7f76e7
THIS DATASET BASED ON THIS SOURCE: [winvoker/turkish-sentiment-analysis-dataset](https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset)
W4nkel/turkish-sentiment-dataset
[ "license:cc-by-sa-4.0", "region:us" ]
2022-12-31T22:37:06+00:00
{"license": "cc-by-sa-4.0"}
2023-01-01T18:07:08+00:00
d021cf90899b09c36291efaacad5cc1ef61ace36
EduardoPacheco/BATB-Videos
[ "task_categories:video-classification", "license:mit", "doi:10.57967/hf/0261", "region:us" ]
2022-12-31T22:56:38+00:00
{"license": "mit", "task_categories": ["video-classification"]}
2023-01-09T21:55:50+00:00
d165b1842aea1598b21c0e19fa2a05a8bb418ace
<div align="center"> <img width="640" alt="keremberke/blood-cell-object-detection" src="https://huggingface.co/datasets/keremberke/blood-cell-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['platelets', 'rbc', 'wbc'] ``` ### Number of Images ```json {'train': 255, 'test': 36, 'valid': 73} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/blood-cell-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3](https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu/dataset/3?ref=roboflow2huggingface) ### Citation ``` @misc{ blood-cell-detection-1ekwu_dataset, title = { Blood Cell Detection Dataset }, type = { Open Source Dataset }, author = { Team Roboflow }, howpublished = { \\url{ https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu } }, url = { https://universe.roboflow.com/team-roboflow/blood-cell-detection-1ekwu }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-01-18 }, } ``` ### License Public Domain ### Dataset Summary This dataset was exported via roboflow.com on November 4, 2022 at 7:46 PM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 364 images. Cells are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) No image augmentation techniques were applied.
keremberke/blood-cell-object-detection
[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "Biology", "region:us" ]
2022-12-31T22:57:22+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Biology"]}
2023-01-18T20:37:18+00:00
a06615c419dc4629dbfd94b75f8dec705cc64055
Robo0890/RosiePosy-Diffusion-Dataset
[ "region:us" ]
2023-01-01T00:47:52+00:00
{}
2023-01-01T01:06:14+00:00
a091c5851343be9017e85c33cf8d09b2a65c6cfb
Sangmun/wiki_doc_preprocessed
[ "license:other", "region:us" ]
2023-01-01T01:47:46+00:00
{"license": "other"}
2023-01-01T01:49:00+00:00
740f9dd32e0c5b023e63cb5293d08f792cd71835
Sangmun/wiki_doc_preprocessed_withtitle
[ "license:other", "region:us" ]
2023-01-01T01:49:03+00:00
{"license": "other"}
2023-01-01T01:49:30+00:00
0850ff20751c5bd59fa838e9f8cdd6210df37b53
Sangmun/wiki_doc_preprocessed_withmaxlength
[ "license:other", "region:us" ]
2023-01-01T01:49:34+00:00
{"license": "other"}
2023-01-01T01:50:00+00:00
a51194c739991abb50ac8afe14704aa99a66cf51
<div align="center"> <img width="640" alt="keremberke/license-plate-object-detection" src="https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['license_plate'] ``` ### Number of Images ```json {'train': 6176, 'valid': 1765, 'test': 882} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/license-plate-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1](https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ vehicle-registration-plates-trudk_dataset, title = { Vehicle Registration Plates Dataset }, type = { Open Source Dataset }, author = { Augmented Startups }, howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } }, url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { jun }, note = { visited on 2023-01-18 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on January 13, 2022 at 5:20 PM GMT It includes 8823 images. VRP are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
keremberke/license-plate-object-detection
[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "Self Driving", "Anpr", "region:us" ]
2023-01-01T02:32:07+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Self Driving", "Anpr"]}
2023-01-18T20:37:51+00:00
fcc2a7a45c9badccca65c464f5d85f59cd1502da
adamwatters/roblox-guy
[ "license:openrail", "region:us" ]
2023-01-01T02:41:41+00:00
{"license": "openrail"}
2023-01-01T03:38:08+00:00
35f65b603b004e57d5eea1c5a4b90bff1f34e290
# Dataset Card for "whisper-small-hindi" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shields/whisper-small-hindi
[ "region:us" ]
2023-01-01T03:28:10+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 172188480.0, "num_examples": 6540}, {"name": "test", "num_bytes": 90338189.0, "num_examples": 2894}], "download_size": 0, "dataset_size": 262526669.0}}
2023-01-01T04:00:50+00:00
e42a0b8b3344d73d1ecf9165b90abcdeb9b87b62
# Dataset Card for "catalan_commonvoice" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
shields/catalan_commonvoice
[ "region:us" ]
2023-01-01T04:47:06+00:00
{"dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34635950777.0, "num_examples": 905243}, {"name": "validation", "num_bytes": 652519005.0, "num_examples": 16340}, {"name": "test", "num_bytes": 625225219.0, "num_examples": 16340}], "download_size": 34496947979, "dataset_size": 35913695001.0}}
2023-01-01T05:12:56+00:00
e3a1bb21fa63a502c27d8b0a10d096a064e0fa9f
stuheart86/imageclassification
[ "license:creativeml-openrail-m", "region:us" ]
2023-01-01T06:12:30+00:00
{"license": "creativeml-openrail-m"}
2023-01-01T06:12:31+00:00
2f760f5a232b842b788e58adfb533dbf205a8b31
# Dataset Card for "microsoft-fluentui-emoji-512-whitebg" [svg and their file names were converted to images and text from Microsoft's fluentui-emoji repo](https://github.com/microsoft/fluentui-emoji)
Norod78/microsoft-fluentui-emoji-512-whitebg
[ "task_categories:unconditional-image-generation", "task_categories:text-to-image", "size_categories:n<10K", "language:en", "license:mit", "emoji", "fluentui", "region:us" ]
2023-01-01T09:03:35+00:00
{"language": "en", "license": "mit", "size_categories": ["n<10K"], "task_categories": ["unconditional-image-generation", "text-to-image"], "pretty_name": "Microsoft FluentUI Emoji 512x512 White Background", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 329173985.708, "num_examples": 7564}], "download_size": 338676474, "dataset_size": 329173985.708}, "tags": ["emoji", "fluentui"]}
2023-07-16T11:12:01+00:00
8e8c373f3a2601b4ed440389466a7c230c5fabca
# Dataset Card for "microsoft-fluentui-emoji-768" [svg and their file names were converted to images and text from Microsoft's fluentui-emoji repo](https://github.com/microsoft/fluentui-emoji)
Norod78/microsoft-fluentui-emoji-768
[ "task_categories:text-to-image", "size_categories:n<10K", "language:en", "license:mit", "emoji", "fluentui", "region:us" ]
2023-01-01T09:35:07+00:00
{"language": "en", "license": "mit", "size_categories": ["n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Microsoft FluentUI Emoji 768x768", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 679617796.94, "num_examples": 7564}], "download_size": 704564297, "dataset_size": 679617796.94}, "tags": ["emoji", "fluentui"]}
2023-07-16T11:13:07+00:00
d8ad1490da2686d7af5c6a7f3d8844f0f9542b0f
### Roboflow Dataset Page [https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2](https://universe.roboflow.com/material-identification/garbage-classification-3/dataset/2?ref=roboflow2huggingface) ### Dataset Labels ``` ['biodegradable', 'cardboard', 'glass', 'metal', 'paper', 'plastic'] ``` ### Citation ``` @misc{ garbage-classification-3_dataset, title = { GARBAGE CLASSIFICATION 3 Dataset }, type = { Open Source Dataset }, author = { Material Identification }, howpublished = { \\url{ https://universe.roboflow.com/material-identification/garbage-classification-3 } }, url = { https://universe.roboflow.com/material-identification/garbage-classification-3 }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { mar }, note = { visited on 2023-01-02 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.com on July 27, 2022 at 5:44 AM GMT Roboflow is an end-to-end computer vision platform that helps you * collaborate with your team on computer vision projects * collect & organize images * understand unstructured image data * annotate, and create datasets * export, train, and deploy computer vision models * use active learning to improve your dataset over time It includes 10464 images. GARBAGE-GARBAGE-CLASSIFICATION are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) * Resize to 416x416 (Stretch) The following augmentation was applied to create 1 versions of each source image: * 50% probability of horizontal flip * 50% probability of vertical flip * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise, upside-down
keremberke/garbage-object-detection
[ "task_categories:object-detection", "roboflow", "region:us" ]
2023-01-01T09:38:12+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow"]}
2023-01-05T11:30:08+00:00
2c0f4e8e9085f3079b80137e780aab0e54936cfc
<div align="center"> <img width="640" alt="keremberke/forklift-object-detection" src="https://huggingface.co/datasets/keremberke/forklift-object-detection/resolve/main/thumbnail.jpg"> </div> ### Dataset Labels ``` ['forklift', 'person'] ``` ### Number of Images ```json {'test': 42, 'valid': 84, 'train': 295} ``` ### How to Use - Install [datasets](https://pypi.org/project/datasets/): ```bash pip install datasets ``` - Load the dataset: ```python from datasets import load_dataset ds = load_dataset("keremberke/forklift-object-detection", name="full") example = ds['train'][0] ``` ### Roboflow Dataset Page [https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1](https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv/dataset/1?ref=roboflow2huggingface) ### Citation ``` @misc{ forklift-dsitv_dataset, title = { Forklift Dataset }, type = { Open Source Dataset }, author = { Mohamed Traore }, howpublished = { \\url{ https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv } }, url = { https://universe.roboflow.com/mohamed-traore-2ekkp/forklift-dsitv }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { mar }, note = { visited on 2023-01-15 }, } ``` ### License CC BY 4.0 ### Dataset Summary This dataset was exported via roboflow.ai on April 3, 2022 at 9:01 PM GMT It includes 421 images. Forklift are annotated in COCO format. The following pre-processing was applied to each image: * Auto-orientation of pixel data (with EXIF-orientation stripping) No image augmentation techniques were applied.
keremberke/forklift-object-detection
[ "task_categories:object-detection", "roboflow", "roboflow2huggingface", "Manufacturing", "region:us" ]
2023-01-01T09:57:34+00:00
{"task_categories": ["object-detection"], "tags": ["roboflow", "roboflow2huggingface", "Manufacturing"]}
2023-01-15T14:32:47+00:00
d73f8f05a66e1c7fcaf2b0e8b806d85aefcc642d
# Dataset Card for "phone-recognition" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Nithiwat/phone-recognition
[ "region:us" ]
2023-01-01T11:50:02+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "ipa", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 762470383.96, "num_examples": 3860}], "download_size": 902056545, "dataset_size": 762470383.96}}
2023-01-07T09:48:54+00:00
eaf94e802d241351cf3f12bc2d221d108ff5d8f1
# Dataset Card for "rlhf-reward-datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
yitingxie/rlhf-reward-datasets
[ "region:us" ]
2023-01-01T12:22:09+00:00
{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 6093563, "num_examples": 5103}, {"name": "train", "num_bytes": 90528217, "num_examples": 76256}], "download_size": 57138483, "dataset_size": 96621780}}
2023-01-01T12:23:04+00:00
2a3eb5b1b0c58b395f8a0ce56e56784f17cdfa3d
tensorops/ggml-whisper-medium-th-combined
[ "license:mit", "region:us" ]
2023-01-01T13:24:35+00:00
{"license": "mit"}
2023-01-01T13:28:17+00:00
5afdd93e5167f6e432801f30078a11ea1537c72e
rcrupi/gbm_grb
[ "license:mit", "region:us" ]
2023-01-01T14:23:21+00:00
{"license": "mit"}
2023-01-01T14:23:21+00:00
f7ec397979411ad5e08e1771ea62e978dfec2cfe
# bAbi_nli bAbI tasks recasted as natural language inference. https://github.com/facebookarchive/bAbI-tasks tasksource recasting code: https://colab.research.google.com/drive/1J_RqDSw9iPxJSBvCJu-VRbjXnrEjKVvr?usp=sharing ```bibtex @article{weston2015towards, title={Towards ai-complete question answering: A set of prerequisite toy tasks}, author={Weston, Jason and Bordes, Antoine and Chopra, Sumit and Rush, Alexander M and Van Merri{\"e}nboer, Bart and Joulin, Armand and Mikolov, Tomas}, journal={arXiv preprint arXiv:1502.05698}, year={2015} } ```
tasksource/babi_nli
[ "task_categories:text-classification", "task_ids:natural-language-inference", "annotations_creators:expert-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:original", "language:en", "license:bsd", "logical reasoning", "nli", "natural-language-inference", "reasoning", "logic", "region:us" ]
2023-01-01T14:39:33+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["crowdsourced"], "language": ["en"], "license": "bsd", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"], "pretty_name": "babi_nli", "tags": ["logical reasoning", "nli", "natural-language-inference", "reasoning", "logic"]}
2023-06-05T08:05:59+00:00
16d21e47237942fe49cf1ee7a35f0ef1d35c3176
# Dataset Card for "porkypig" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
matteopilotto/porkypig
[ "region:us" ]
2023-01-01T15:55:29+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2107384.0, "num_examples": 11}], "download_size": 2108606, "dataset_size": 2107384.0}}
2023-01-01T15:55:35+00:00
33e00bf20c361ee26f360c4c603f826f9d055e1c
This dataset contains just under half of the training data used to train [Paint Journey](https://huggingface.co/FredZhang7/Paint-Journey). All 768x768 images were generated using one of Disco Diffusion v3.1, v4.1, and v5.x, but later upscaled then downscaled twice (super resolution) using R-ESRGAN General WDN 4x V3 just before training.
FredZhang7/disco-diffusion
[ "license:mit", "stable-diffusion", "paint-journey", "region:us" ]
2023-01-01T18:57:14+00:00
{"license": "mit", "tags": ["stable-diffusion", "paint-journey"]}
2023-01-02T06:25:07+00:00
050c7efd202ad9b35866b7c671cea78548a89f94
akanametov/minions-dataset
[ "license:mit", "region:us" ]
2023-01-01T19:03:02+00:00
{"license": "mit"}
2023-01-01T19:05:58+00:00
83bd696d6f1611a7ede49f8fe1c68727cf3ce7ae
# Dataset Card for "Terrier-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bobber/Terrier-images
[ "region:us" ]
2023-01-01T19:49:56+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1623322.0, "num_examples": 18}], "download_size": 1624818, "dataset_size": 1623322.0}}
2023-01-01T19:50:00+00:00
616e9c7b2570cd1cfeacb459d862a2e64e8b0e98
# Dataset Card for "twitter_de_ru" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
carexl8/twitter_de_ru
[ "region:us" ]
2023-01-01T21:02:34+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[ns, tz=UTC]"}, {"name": "tokens", "sequence": "string"}, {"name": "language tags", "sequence": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 563616, "num_examples": 688}], "download_size": 0, "dataset_size": 563616}}
2023-04-21T18:41:06+00:00
37fa73c9eb0c081e4a9faf15df7022b0b14d6b79
# Dataset Card for "CV_Eng_train_specialCharsRemoved" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
kabir5297/CV_Eng_train_specialCharsRemoved
[ "region:us" ]
2023-01-01T22:54:57+00:00
{"dataset_info": {"features": [{"name": "filename", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 822619, "num_examples": 11281}], "download_size": 409770, "dataset_size": 822619}}
2023-01-01T22:55:01+00:00
00429b34bce626d58ecd42c2746e8461956f3a60
ContractorQB/aimitz
[ "license:other", "region:us" ]
2023-01-02T00:42:50+00:00
{"license": "other"}
2023-01-02T00:43:20+00:00
5e352bce8912d68ee9bd4ea045542123fdb4cded
# Dataset Card for "w2v2_0" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
mp1704/w2v2_0
[ "region:us" ]
2023-01-02T03:08:30+00:00
{"dataset_info": {"features": [{"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 163223, "num_examples": 1048}, {"name": "test", "num_bytes": 18107, "num_examples": 117}], "download_size": 107863, "dataset_size": 181330}}
2023-01-02T03:08:39+00:00
cf2b63456a57d26cb5261cf9fb3b9dc80bdfa69d
synthseq/automata
[ "license:mit", "region:us" ]
2023-01-02T04:26:28+00:00
{"license": "mit"}
2023-02-11T23:56:10+00:00
b4dfd5d2cc7ab1b3b65140936a82c9d007eb8832
synthseq/circuits
[ "license:mit", "region:us" ]
2023-01-02T04:28:23+00:00
{"license": "mit"}
2023-01-02T04:28:23+00:00
bec8d5d90fc202d4f9c1c0a3cb26df839475ae8f
mio/browndust_tag
[ "license:creativeml-openrail-m", "region:us" ]
2023-01-02T04:34:44+00:00
{"license": "creativeml-openrail-m"}
2023-01-02T04:34:44+00:00
2f6df30d7d5b28dee57c3f02940329d46e326e7d
adamwatters/hosted-images
[ "license:openrail", "region:us" ]
2023-01-02T05:19:26+00:00
{"license": "openrail"}
2023-01-02T05:20:51+00:00
831307f930f53d188e137ccd5d935e4162dd0929
# Dataset Card for "mediumroast-press-releases" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jgoodie/mediumroast-press-releases
[ "region:us" ]
2023-01-02T06:11:17+00:00
{"dataset_info": {"features": [{"name": "Id", "dtype": "string"}, {"name": "Title", "dtype": "string"}, {"name": "Published", "dtype": "string"}, {"name": "Link", "dtype": "string"}, {"name": "Text", "dtype": "string"}, {"name": "Abouts", "struct": [{"name": "About TransVoyant", "dtype": "string"}, {"name": "About Merck Global Health Innovation Fund", "dtype": "string"}, {"name": "About P74 Ventures", "dtype": "string"}, {"name": "About Historic Hotels of America", "dtype": "string"}, {"name": "About First Internet Bancorp", "dtype": "string"}, {"name": "About Mary Kay", "dtype": "string"}, {"name": "About United Nations Development Programme (UNDP)", "dtype": "string"}, {"name": "About China International Center for Economic and Technical Exchanges (CICETE)", "dtype": "string"}, {"name": "About China Women\u2019s Development Foundation (CWDF)", "dtype": "string"}, {"name": "About Ivanti", "dtype": "string"}, {"name": "About Brandon Hall Group", "dtype": "string"}, {"name": "About UBS", "dtype": "string"}, {"name": "About CDP", "dtype": "string"}, {"name": "About The SEAL Awards", "dtype": "string"}, {"name": "About CyrusOne", "dtype": "string"}, {"name": "About Vizient", "dtype": "string"}, {"name": "About KARL STORZ", "dtype": "string"}, {"name": "About Rupert Resources", "dtype": "string"}, {"name": "About Grain Sustainability", "dtype": "string"}, {"name": "About Garmin International, Inc.", "dtype": "string"}, {"name": "About CARFAX Canada", "dtype": "string"}, {"name": "About Edgecore", "dtype": "string"}, {"name": "About Cyware", "dtype": "string"}, {"name": "About CSW", "dtype": "string"}, {"name": "About Euromonitor International", "dtype": "string"}, {"name": "About FICO", "dtype": "string"}, {"name": "About Veritone", "dtype": "string"}, {"name": "About Seagate Technology", "dtype": "string"}, {"name": "About MedVector", "dtype": "string"}, {"name": "About Gain\u00ae", "dtype": "string"}, {"name": "About Procter & Gamble", "dtype": "string"}, {"name": "About SITE Centers Corp.", "dtype": "string"}, {"name": "About Ford Motor Company", "dtype": "string"}, {"name": "About CDK Global, Inc.", "dtype": "string"}, {"name": "About William Blair Investment Banking", "dtype": "string"}, {"name": "About William Blair", "dtype": "string"}, {"name": "About Postmedia Network Inc.", "dtype": "string"}, {"name": "About Sports Venture Holdings and BET99", "dtype": "string"}, {"name": "About Great Western Bank", "dtype": "string"}, {"name": "About Ambient Photonics", "dtype": "string"}, {"name": "About Origis Energy", "dtype": "string"}, {"name": "About Mitsubishi Power Americas, Inc.", "dtype": "string"}, {"name": "About Insider Connected", "dtype": "string"}, {"name": "About Stern Pinball, Inc.", "dtype": "string"}, {"name": "About Garmin:", "dtype": "string"}, {"name": "About Navy Federal Credit Union:", "dtype": "string"}, {"name": "About Carbon Robotics", "dtype": "string"}, {"name": "About Purpose Investments Inc.", "dtype": "string"}, {"name": "About Purpose Financial", "dtype": "string"}, {"name": "About Second Harvest", "dtype": "string"}, {"name": "About Kerrigan Advisors", "dtype": "string"}, {"name": "About InstaSafe", "dtype": "string"}, {"name": "About ZNet Technologies", "dtype": "string"}, {"name": "About RPtech", "dtype": "string"}, {"name": "About Xylem", "dtype": "string"}, {"name": "About Bobbie", "dtype": "string"}, {"name": "About Uber Eats", "dtype": "string"}, {"name": "About ConocoPhillips", "dtype": "string"}, {"name": "About Genius Sports", "dtype": "string"}, {"name": "About Walmart", "dtype": "string"}, {"name": "About", "dtype": "string"}, {"name": "About Historic Hotels Worldwide", "dtype": "string"}, {"name": "About Symetra", "dtype": "string"}, {"name": "About MCR", "dtype": "string"}, {"name": "About Bynder", "dtype": "string"}, {"name": "About Thomas H. Lee Partners, L.P.", "dtype": "string"}, {"name": "About Nickel 28", "dtype": "string"}, {"name": "About Critical Metals Corp.", "dtype": "string"}, {"name": "About European Lithium Ltd", "dtype": "string"}, {"name": "About Historic Hotels Worldwide\u00ae", "dtype": "string"}, {"name": "About KBRA", "dtype": "string"}, {"name": "About Knowles", "dtype": "string"}, {"name": "About Trillbit", "dtype": "string"}, {"name": "About UKG", "dtype": "string"}, {"name": "About Unravel Data", "dtype": "string"}, {"name": "About OmniVision", "dtype": "string"}, {"name": "About Seagate", "dtype": "string"}, {"name": "About Sharp NEC Display Solutions of America, Inc.", "dtype": "string"}, {"name": "About Avery Dennison", "dtype": "string"}, {"name": "About three-quarters plan to use locationing technologies", "dtype": "string"}, {"name": "About 63", "dtype": "string"}, {"name": "About Ocean Prime", "dtype": "string"}, {"name": "About Williams", "dtype": "string"}, {"name": "About Global Learning Systems", "dtype": "string"}, {"name": "About Orthofix", "dtype": "string"}, {"name": "About Laura Wilkinson", "dtype": "string"}, {"name": "About Fiduciary Trust International", "dtype": "string"}, {"name": "About Franklin Templeton", "dtype": "string"}, {"name": "About Momentus", "dtype": "string"}, {"name": "About Jaunt", "dtype": "string"}, {"name": "About the NEO Exchange", "dtype": "string"}, {"name": "About Purpose Investments", "dtype": "string"}, {"name": "About Prasad Corp:", "dtype": "string"}, {"name": "About Cube-Tec International:", "dtype": "string"}, {"name": "About Bowman Consulting Group Ltd. (Bowman):", "dtype": "string"}, {"name": "About Dutch Bros:", "dtype": "string"}, {"name": "About Cascade", "dtype": "string"}, {"name": "About BEF and Change the Course", "dtype": "string"}, {"name": "About GPM", "dtype": "string"}, {"name": "About Tmunity Therapeutics", "dtype": "string"}, {"name": "About Kite", "dtype": "string"}, {"name": "About Gilead Sciences", "dtype": "string"}, {"name": "About Pathway", "dtype": "string"}, {"name": "About Globality \u2013 Option 1", "dtype": "string"}, {"name": "About The AI Journal Global Excellence Awards", "dtype": "string"}, {"name": "About National Geographic Content", "dtype": "string"}, {"name": "About ABC Signature", "dtype": "string"}, {"name": "About Keshet Studios", "dtype": "string"}, {"name": "About Promega Corporation", "dtype": "string"}, {"name": "About Bristol Myers Squibb", "dtype": "string"}, {"name": "About Montgomery County", "dtype": "string"}, {"name": "About Rave Mobile Safety", "dtype": "string"}, {"name": "About Motorola Solutions (NYSE: MSI)", "dtype": "string"}, {"name": "About ARR", "dtype": "string"}, {"name": "About Everygame Poker:", "dtype": "string"}, {"name": "About Graceland and Elvis Presley Enterprises, Inc", "dtype": "string"}, {"name": "About ELVIS", "dtype": "string"}, {"name": "About Abacus", "dtype": "string"}, {"name": "About East Resources Acquisition Company", "dtype": "string"}, {"name": "About INNIO", "dtype": "string"}, {"name": "About Chilmark", "dtype": "string"}, {"name": "About LeanTaaS", "dtype": "string"}, {"name": "About Adobe", "dtype": "string"}, {"name": "About Webhelp", "dtype": "string"}, {"name": "About MassNAELA", "dtype": "string"}, {"name": "About Multiple Sclerosis", "dtype": "string"}, {"name": "About the American Music Therapy Association", "dtype": "string"}, {"name": "About Five9", "dtype": "string"}, {"name": "About ClearSale", "dtype": "string"}, {"name": "About Savills", "dtype": "string"}, {"name": "About Lucy Cavendish College", "dtype": "string"}, {"name": "About Queens College of the City University of New York", "dtype": "string"}, {"name": "About ReferralPoint", "dtype": "string"}, {"name": "About athenahealth Marketplace", "dtype": "string"}, {"name": "About Luxfer Holdings PLC", "dtype": "string"}, {"name": "About CAIRE Inc.", "dtype": "string"}, {"name": "About Know Labs, Inc.", "dtype": "string"}, {"name": "About Business Wire", "dtype": "string"}, {"name": "About ForgeRock", "dtype": "string"}, {"name": "About Ameresco, Inc.", "dtype": "string"}, {"name": "About Gunze", "dtype": "string"}, {"name": "About Motorola Solutions", "dtype": "string"}, {"name": "About NTT Research", "dtype": "string"}, {"name": "About Factspan:", "dtype": "string"}, {"name": "About the Children\u2019s Eye Foundation of AAPOS", "dtype": "string"}, {"name": "About Magik Eye Inc", "dtype": "string"}, {"name": "About Garmin Connect", "dtype": "string"}, {"name": "About Bladder and Urothelial Cancer", "dtype": "string"}, {"name": "About the EV-103/KEYNOTE-869 Trial", "dtype": "string"}, {"name": "About PADCEV", "dtype": "string"}, {"name": "About KEYTRUDA", "dtype": "string"}, {"name": "About Seagen", "dtype": "string"}, {"name": "About Astellas", "dtype": "string"}, {"name": "About Merck", "dtype": "string"}, {"name": "About the Seagen, Astellas and Merck Collaboration", "dtype": "string"}, {"name": "About bluebird bio, Inc.", "dtype": "string"}, {"name": "About Great Western Bancorp, Inc.", "dtype": "string"}, {"name": "About Pebblebrook Hotel Trust", "dtype": "string"}, {"name": "About The Harris Poll", "dtype": "string"}, {"name": "About MITRE", "dtype": "string"}, {"name": "About Nomi Health", "dtype": "string"}, {"name": "About Ganymede", "dtype": "string"}, {"name": "About Apprentice", "dtype": "string"}, {"name": "About Columbia Sussex:", "dtype": "string"}, {"name": "About Marriott Hotels", "dtype": "string"}, {"name": "About Carallel", "dtype": "string"}, {"name": "About Jon Peddie Research", "dtype": "string"}, {"name": "About Semperis", "dtype": "string"}, {"name": "About InspereX", "dtype": "string"}, {"name": "About Allison+Partners", "dtype": "string"}, {"name": "About American Efficient", "dtype": "string"}, {"name": "About See it. Feel it. Seal it.", "dtype": "string"}, {"name": "About NBC News:", "dtype": "string"}, {"name": "About Tyler Technologies, Inc.", "dtype": "string"}, {"name": "About Duo Health:", "dtype": "string"}, {"name": "About Desert Kidney Associates:", "dtype": "string"}, {"name": "About Peachtree Group", "dtype": "string"}, {"name": "About Bona", "dtype": "string"}, {"name": "About UserTesting", "dtype": "string"}, {"name": "About ICR", "dtype": "string"}, {"name": "About ActivTrak", "dtype": "string"}, {"name": "About Historic Hotels of America\u00ae", "dtype": "string"}, {"name": "About NextDecade Corporation", "dtype": "string"}, {"name": "About ElevateBio BaseCamp", "dtype": "string"}, {"name": "About ElevateBio", "dtype": "string"}, {"name": "About WPP", "dtype": "string"}, {"name": "About ExtensisHR", "dtype": "string"}, {"name": "About Pfizer: Breakthroughs That Change Patients\u2019 Lives", "dtype": "string"}, {"name": "About CareWell Health Medical Center", "dtype": "string"}, {"name": "About Upper Crust Food Service:", "dtype": "string"}, {"name": "About College Chefs:", "dtype": "string"}, {"name": "About Lineage Logistics", "dtype": "string"}, {"name": "About Bay Grove", "dtype": "string"}, {"name": "About Bird", "dtype": "string"}, {"name": "About Wish", "dtype": "string"}, {"name": "About Eurora Solutions", "dtype": "string"}, {"name": "About Chatham Lodging Trust", "dtype": "string"}, {"name": "About DESTINY-Breast03", "dtype": "string"}, {"name": "About HER2 Positive Breast Cancer", "dtype": "string"}, {"name": "About ENHERTU", "dtype": "string"}, {"name": "About the ENHERTU Clinical Development Program", "dtype": "string"}, {"name": "About the Daiichi Sankyo and AstraZeneca Collaboration", "dtype": "string"}, {"name": "About Daiichi Sankyo", "dtype": "string"}, {"name": "About ADS-TEC Energy", "dtype": "string"}, {"name": "About TSG Consumer Partners", "dtype": "string"}, {"name": "About what3words", "dtype": "string"}, {"name": "About BeyondNetZero", "dtype": "string"}, {"name": "About General Atlantic", "dtype": "string"}, {"name": "About Angel Oak Mortgage, Inc.", "dtype": "string"}, {"name": "About The Beachbody Company, Inc.", "dtype": "string"}, {"name": "About Quantum-Si Incorporated", "dtype": "string"}, {"name": "About StorONE", "dtype": "string"}, {"name": "About Health Fidelity", "dtype": "string"}, {"name": "About Werner Enterprises", "dtype": "string"}, {"name": "About Endoluxe", "dtype": "string"}, {"name": "About Granite", "dtype": "string"}, {"name": "About KBI Biopharma, Inc.", "dtype": "string"}, {"name": "About Waters Corporation", "dtype": "string"}, {"name": "About Dawn", "dtype": "string"}, {"name": "About Alarm.com", "dtype": "string"}, {"name": "About American Pacific Group", "dtype": "string"}, {"name": "About BioTalent Canada", "dtype": "string"}, {"name": "About FashWire:", "dtype": "string"}, {"name": "About Credit Karma", "dtype": "string"}, {"name": "About Intuit", "dtype": "string"}, {"name": "About SheerID", "dtype": "string"}, {"name": "About Sessions", "dtype": "string"}, {"name": "About RGI-2001", "dtype": "string"}, {"name": "About REGiMMUNE Limited", "dtype": "string"}, {"name": "About Audax Group", "dtype": "string"}, {"name": "About Skillz Inc.", "dtype": "string"}, {"name": "About Talon Cyber Security", "dtype": "string"}, {"name": "About Battery Ventures", "dtype": "string"}, {"name": "About Qognify", "dtype": "string"}, {"name": "About WestRock", "dtype": "string"}, {"name": "About Teucrium Trading LLC", "dtype": "string"}, {"name": "About Turo", "dtype": "string"}, {"name": "About IDT", "dtype": "string"}, {"name": "About PIND", "dtype": "string"}, {"name": "About NDPI", "dtype": "string"}, {"name": "About the Principal Financial Well-Being Index", "dtype": "string"}, {"name": "About Principal Financial Group", "dtype": "string"}, {"name": "About Parallel Bio", "dtype": "string"}, {"name": "About Granite Point Mortgage Trust Inc.", "dtype": "string"}, {"name": "About SambaNova Systems", "dtype": "string"}, {"name": "About the Tech Ascension Awards", "dtype": "string"}, {"name": "About UNCF", "dtype": "string"}, {"name": "About DeVry University", "dtype": "string"}, {"name": "About ISG Provider Lens\u2122 Research", "dtype": "string"}, {"name": "About ISG", "dtype": "string"}, {"name": "About Lp(a)", "dtype": "string"}, {"name": "About Arrowhead Pharmaceuticals", "dtype": "string"}, {"name": "About Cantaloupe, Inc.:", "dtype": "string"}, {"name": "About Amneal", "dtype": "string"}, {"name": "About Smartling", "dtype": "string"}, {"name": "About Oragenics, Inc.", "dtype": "string"}, {"name": "About TD SYNNEX", "dtype": "string"}, {"name": "About AtriCure", "dtype": "string"}, {"name": "About Scale Computing", "dtype": "string"}, {"name": "About Planar", "dtype": "string"}, {"name": "About Pactum", "dtype": "string"}, {"name": "About Millennium Solutions", "dtype": "string"}, {"name": "About FINEOS Corporation", "dtype": "string"}, {"name": "About CrowdStrike", "dtype": "string"}, {"name": "About BitNile Holdings, Inc.", "dtype": "string"}, {"name": "About EIG", "dtype": "string"}, {"name": "About ILOS Projects", "dtype": "string"}, {"name": "About Omnes", "dtype": "string"}, {"name": "About Achievers", "dtype": "string"}, {"name": "About Dasera", "dtype": "string"}, {"name": "About Rapid Dose Therapeutics Corp.", "dtype": "string"}, {"name": "About Deucravacitinib", "dtype": "string"}, {"name": "About the Phase 3 POETYK PSO-1 and POETYK PSO-2 Studies", "dtype": "string"}, {"name": "About Psoriasis", "dtype": "string"}, {"name": "About Enerpac Tool Group", "dtype": "string"}, {"name": "About doxo INSIGHTS", "dtype": "string"}, {"name": "About doxo", "dtype": "string"}, {"name": "About Uncovering TNBC", "dtype": "string"}, {"name": "About Yvonne Orji", "dtype": "string"}, {"name": "About American Water", "dtype": "string"}, {"name": "About The Water Research Foundation", "dtype": "string"}, {"name": "About Stratascale", "dtype": "string"}, {"name": "About the FinOps Foundation", "dtype": "string"}, {"name": "About Native Voice", "dtype": "string"}, {"name": "About iHeartMedia", "dtype": "string"}, {"name": "About Consilio", "dtype": "string"}, {"name": "About Comparably", "dtype": "string"}, {"name": "About Comparably Awards", "dtype": "string"}, {"name": "About Runway Group", "dtype": "string"}, {"name": "About UP.Partners", "dtype": "string"}, {"name": "About Century Housing Corporation", "dtype": "string"}, {"name": "About U.S. Bancorp Community Development Corporation:", "dtype": "string"}, {"name": "About FCPT", "dtype": "string"}, {"name": "About Micronoma", "dtype": "string"}, {"name": "About Samsung Electronics Co., Ltd.", "dtype": "string"}, {"name": "About OppFi", "dtype": "string"}, {"name": "About Searchlight Cyber", "dtype": "string"}, {"name": "About Mighty Buildings", "dtype": "string"}, {"name": "About Jack in the Box", "dtype": "string"}, {"name": "About Western Union", "dtype": "string"}, {"name": "About ADM", "dtype": "string"}, {"name": "About Bona US", "dtype": "string"}, {"name": "About NW Natural", "dtype": "string"}, {"name": "About P&G", "dtype": "string"}, {"name": "About P&G\u2019s 2,021 Acts of Good in 2021", "dtype": "string"}, {"name": "About Grove Collaborative Holdings, Inc.", "dtype": "string"}, {"name": "About PhishFirewall", "dtype": "string"}, {"name": "About Fluree", "dtype": "string"}, {"name": "About Juniper Networks", "dtype": "string"}, {"name": "About Lomiko Metals Inc.", "dtype": "string"}, {"name": "About Quotient", "dtype": "string"}, {"name": "About Supermicro", "dtype": "string"}, {"name": "About Susan G. Komen\u00ae", "dtype": "string"}, {"name": "About CSET", "dtype": "string"}, {"name": "About Snorkel AI", "dtype": "string"}, {"name": "About Inversion6", "dtype": "string"}, {"name": "About Make-A-Wish", "dtype": "string"}, {"name": "About Klick Health", "dtype": "string"}, {"name": "About Klick Group", "dtype": "string"}, {"name": "About Upflex:", "dtype": "string"}, {"name": "About QuantumScape Corporation", "dtype": "string"}, {"name": "About RevBio, Inc.", "dtype": "string"}, {"name": "About Navy Federal Credit Union", "dtype": "string"}, {"name": "About Operation Homefront:", "dtype": "string"}, {"name": "About Jackpot.com", "dtype": "string"}, {"name": "About Vanson Bourne", "dtype": "string"}, {"name": "About Code42", "dtype": "string"}, {"name": "About Decorative Films:", "dtype": "string"}, {"name": "About Appvion:", "dtype": "string"}, {"name": "About Nekoosa:", "dtype": "string"}, {"name": "About Wynnchurch Capital:", "dtype": "string"}, {"name": "About NanOlogy", "dtype": "string"}, {"name": "About Komatsu", "dtype": "string"}, {"name": "About PPM America", "dtype": "string"}, {"name": "About Applied UV", "dtype": "string"}, {"name": "About LED Supply Co.", "dtype": "string"}, {"name": "About PURO UV Disinfection Lighting", "dtype": "string"}, {"name": "About Algolia", "dtype": "string"}, {"name": "About Apple Hospitality REIT, Inc.", "dtype": "string"}, {"name": "About DS Smith", "dtype": "string"}, {"name": "About HC3", "dtype": "string"}, {"name": "About the Potential Home Sales Model", "dtype": "string"}, {"name": "About First American", "dtype": "string"}, {"name": "About Sappi North America, Inc.", "dtype": "string"}, {"name": "About KlariVis", "dtype": "string"}, {"name": "About Nexgrill", "dtype": "string"}, {"name": "About Edgewise Therapeutics", "dtype": "string"}, {"name": "About the new board members", "dtype": "string"}, {"name": "About Grant Thornton LLP", "dtype": "string"}, {"name": "About Geographic Atrophy", "dtype": "string"}, {"name": "About Avacincaptad Pegol", "dtype": "string"}, {"name": "About the GATHER Clinical Trials", "dtype": "string"}, {"name": "About Breakthrough Therapy Designation", "dtype": "string"}, {"name": "About Iveric Bio", "dtype": "string"}, {"name": "About Imbrium Therapeutics L.P.", "dtype": "string"}, {"name": "About the Alice L. Walton Foundation", "dtype": "string"}, {"name": "About Washington Regional Medical System", "dtype": "string"}, {"name": "About Cleveland Clinic", "dtype": "string"}, {"name": "About ShopOne", "dtype": "string"}, {"name": "About Pantheon", "dtype": "string"}, {"name": "About Gamepires", "dtype": "string"}, {"name": "About Jagex", "dtype": "string"}, {"name": "About Everbridge", "dtype": "string"}, {"name": "About Riskalyze", "dtype": "string"}, {"name": "About InvestorCOM Inc.", "dtype": "string"}, {"name": "About AngioDynamics, Inc.", "dtype": "string"}, {"name": "About Intelitek", "dtype": "string"}, {"name": "About First Farmers and Merchants Corporation and First Farmers and Merchants Bank", "dtype": "string"}, {"name": "About UP.Summit", "dtype": "string"}, {"name": "About the Food Network & Cooking Channel South Beach Wine & Food Festival", "dtype": "string"}, {"name": "About Strategic Storage Trust VI, Inc. (SST VI):", "dtype": "string"}, {"name": "About SmartStop Self Storage REIT, Inc. (SmartStop):", "dtype": "string"}, {"name": "About DTEX Systems", "dtype": "string"}, {"name": "About the Call of Duty Endowment", "dtype": "string"}, {"name": "About Experian", "dtype": "string"}, {"name": "About Operation HOPE", "dtype": "string"}, {"name": "About Dermavant\u2019s Phase 3 Program for Tapinarof in Psoriasis", "dtype": "string"}, {"name": "About Dermavant", "dtype": "string"}, {"name": "About Microvast", "dtype": "string"}, {"name": "About the Archer Awards", "dtype": "string"}, {"name": "About TechTarget", "dtype": "string"}, {"name": "About L3Harris Technologies", "dtype": "string"}, {"name": "About Klara", "dtype": "string"}, {"name": "About ModMed", "dtype": "string"}, {"name": "About Business Intelligence Group", "dtype": "string"}, {"name": "About Wisk", "dtype": "string"}, {"name": "About Illinois American Water", "dtype": "string"}, {"name": "About SSG", "dtype": "string"}, {"name": "About FPT Software", "dtype": "string"}, {"name": "About Circle Pharma, Inc.", "dtype": "string"}, {"name": "About NETGEAR, Inc.", "dtype": "string"}, {"name": "About Zurn Elkay Water Solutions", "dtype": "string"}, {"name": "About IDC Trackers", "dtype": "string"}, {"name": "About IDC", "dtype": "string"}, {"name": "About Monroe Capital", "dtype": "string"}, {"name": "About NICE Actimize", "dtype": "string"}, {"name": "About NICE", "dtype": "string"}, {"name": "About AuriNovo\u2122", "dtype": "string"}, {"name": "About the Microtia-Congenital Ear Deformity Institute", "dtype": "string"}, {"name": "About 3DBio Therapeutics", "dtype": "string"}, {"name": "About Oshkosh Corporation", "dtype": "string"}, {"name": "About Bob Harper", "dtype": "string"}, {"name": "About AstraZeneca", "dtype": "string"}, {"name": "About SafePath\u00ae", "dtype": "string"}, {"name": "About Smith Micro Software, Inc.", "dtype": "string"}, {"name": "About Non-GAAP Financial Measures", "dtype": "string"}, {"name": "About Cognyte Software Ltd.", "dtype": "string"}, {"name": "About BJ's Wholesale Club Holdings, Inc.", "dtype": "string"}, {"name": "About Sama", "dtype": "string"}, {"name": "About Evans Transportation Services Inc.", "dtype": "string"}, {"name": "About PowerSchool", "dtype": "string"}, {"name": "About MatSing", "dtype": "string"}, {"name": "About Transaction Network Services", "dtype": "string"}, {"name": "About Cataracts", "dtype": "string"}, {"name": "About Presbyopia", "dtype": "string"}, {"name": "About the AcrySof\u00ae IQ Vivity", "dtype": "string"}, {"name": "About the Outstanding Pole Award", "dtype": "string"}, {"name": "About Pure Wafer", "dtype": "string"}, {"name": "About Braverman Greenspun P.C.", "dtype": "string"}, {"name": "About XPeng Inc.", "dtype": "string"}, {"name": "About Wallarm", "dtype": "string"}, {"name": "About Transaction Network Services (TNS)", "dtype": "string"}, {"name": "About KKR", "dtype": "string"}, {"name": "About IMV", "dtype": "string"}, {"name": "About Cepton", "dtype": "string"}, {"name": "About Coty Inc.", "dtype": "string"}, {"name": "About HUGO BOSS", "dtype": "string"}, {"name": "About Juicy Stakes Casino:", "dtype": "string"}, {"name": "About Susan G. Komen", "dtype": "string"}, {"name": "About Sonio", "dtype": "string"}, {"name": "About Terreal", "dtype": "string"}, {"name": "About BabyQuip", "dtype": "string"}, {"name": "About Sense", "dtype": "string"}, {"name": "About OCC", "dtype": "string"}, {"name": "About International Bird Rescue", "dtype": "string"}, {"name": "About The Marine Mammal Center", "dtype": "string"}, {"name": "About Japan National Tourism Organization", "dtype": "string"}, {"name": "About Wan Bridge", "dtype": "string"}, {"name": "About The Gabelli Dividend & Income Trust", "dtype": "string"}, {"name": "About Hurricane Electric", "dtype": "string"}, {"name": "About NIKE, Inc.", "dtype": "string"}, {"name": "About Emburse", "dtype": "string"}, {"name": "About the IDC MarketScape", "dtype": "string"}, {"name": "About Skechers USA Ltd. and Skechers USA, Inc.", "dtype": "string"}, {"name": "About N-able", "dtype": "string"}, {"name": "About ViewSonic", "dtype": "string"}, {"name": "About NortonLifeLock Inc.", "dtype": "string"}, {"name": "About Airiam", "dtype": "string"}, {"name": "About the Principal Financial Well-Being Index\u2120", "dtype": "string"}, {"name": "About Gail Devers", "dtype": "string"}, {"name": "About Graves\u2019 Disease", "dtype": "string"}, {"name": "About Thyroid Eye Disease", "dtype": "string"}, {"name": "About The Graves\u2019 Disease and Thyroid Foundation", "dtype": "string"}, {"name": "About Prevent Blindness", "dtype": "string"}, {"name": "About Horizon", "dtype": "string"}, {"name": "About Conceal", "dtype": "string"}, {"name": "About Cybin", "dtype": "string"}, {"name": "About Synergis Software", "dtype": "string"}, {"name": "About Aruba, a Hewlett Packard Enterprise company", "dtype": "string"}, {"name": "About Stewart", "dtype": "string"}, {"name": "About Tumble", "dtype": "string"}, {"name": "About GRAIL", "dtype": "string"}, {"name": "About Wheels", "dtype": "string"}, {"name": "About Helbiz", "dtype": "string"}, {"name": "About Regions Financial Corporation", "dtype": "string"}, {"name": "About Match Marketing Group", "dtype": "string"}, {"name": "About Public Label", "dtype": "string"}, {"name": "About Match Retail", "dtype": "string"}, {"name": "About Bushu Pharmaceuticals Ltd.", "dtype": "string"}, {"name": "About The 81 Collection", "dtype": "string"}, {"name": "About Columbia Sussex", "dtype": "string"}, {"name": "About Renaissance Hotels", "dtype": "string"}, {"name": "About Hims & Hers", "dtype": "string"}, {"name": "About eternalHealth:", "dtype": "string"}, {"name": "About Angeles Equity Partners, LLC", "dtype": "string"}, {"name": "About R\u014dBEX", "dtype": "string"}, {"name": "About Vince Tizzio", "dtype": "string"}, {"name": "About Albert Benchimol", "dtype": "string"}, {"name": "About AXIS Capital", "dtype": "string"}, {"name": "About Regional Management Corp.", "dtype": "string"}, {"name": "About Black & Veatch", "dtype": "string"}, {"name": "About NextGen Healthcare, Inc.", "dtype": "string"}, {"name": "About Bridges Health Partners", "dtype": "string"}, {"name": "About Keller Williams", "dtype": "string"}, {"name": "About Board", "dtype": "string"}, {"name": "About Velodyne Lidar", "dtype": "string"}, {"name": "About Clarity AI", "dtype": "string"}, {"name": "About Refinitiv, an LSEG business", "dtype": "string"}, {"name": "About LSEG", "dtype": "string"}, {"name": "About Sterling", "dtype": "string"}, {"name": "About Orelabrutinib", "dtype": "string"}, {"name": "About Tafasitamab", "dtype": "string"}, {"name": "About InnoCare", "dtype": "string"}, {"name": "About ExtraHop", "dtype": "string"}, {"name": "About Mitek Systems, Inc.", "dtype": "string"}, {"name": "About Hamilton Capital Partners Inc. (Hamilton ETFs)", "dtype": "string"}, {"name": "About Benson Hill", "dtype": "string"}, {"name": "About Star Peak Corp II", "dtype": "string"}, {"name": "About Commonwealth Financial Network", "dtype": "string"}, {"name": "About Skyhigh Security:", "dtype": "string"}, {"name": "About Heartland Summit", "dtype": "string"}, {"name": "About Neuromyelitis Optica Spectrum Disorder (NMOSD)", "dtype": "string"}, {"name": "About UPLIZNA (inebilizumab-cdon)", "dtype": "string"}, {"name": "About Eptura\u2122", "dtype": "string"}, {"name": "About Space Perspective", "dtype": "string"}, {"name": "About David Grutman", "dtype": "string"}, {"name": "About Harrods", "dtype": "string"}, {"name": "About ISG Provider Lens\u2122", "dtype": "string"}, {"name": "About Slate Office REIT (TSX: SOT.UN)", "dtype": "string"}, {"name": "About Slate Asset Management", "dtype": "string"}, {"name": "About Gatos Silver", "dtype": "string"}, {"name": "About Outset Medical, Inc.", "dtype": "string"}, {"name": "About I/ITSEC", "dtype": "string"}, {"name": "About RAVE Computer", "dtype": "string"}, {"name": "About Sun-Maid Growers of California", "dtype": "string"}, {"name": "About CIBC Innovation Banking", "dtype": "string"}, {"name": "About Azalea Health", "dtype": "string"}, {"name": "About Great American\u2019s Fidelity / Crime Division", "dtype": "string"}, {"name": "About Great American Insurance Group", "dtype": "string"}, {"name": "About Infobip", "dtype": "string"}, {"name": "About Cantaloupe, Inc.", "dtype": "string"}, {"name": "About Express, Inc.:", "dtype": "string"}, {"name": "About Cooper Tire & Rubber Company", "dtype": "string"}, {"name": "About Alice Cooper", "dtype": "string"}, {"name": "About Evanescence", "dtype": "string"}, {"name": "About THIO", "dtype": "string"}, {"name": "About MAIA Biotechnology, Inc.", "dtype": "string"}, {"name": "About The Mission Continues:", "dtype": "string"}, {"name": "About SmartBear", "dtype": "string"}, {"name": "About DemandScience", "dtype": "string"}, {"name": "About Guild Mortgage", "dtype": "string"}, {"name": "About Generational Equity", "dtype": "string"}, {"name": "About Lisbon Heritage Hotels", "dtype": "string"}, {"name": "About Bojangles, Inc.", "dtype": "string"}, {"name": "About Ozark Fiber:", "dtype": "string"}, {"name": "About Duravant", "dtype": "string"}, {"name": "About Multiscan Technologies", "dtype": "string"}, {"name": "About Acorda Therapeutics", "dtype": "string"}, {"name": "About HealthCare Royalty", "dtype": "string"}, {"name": "About Atara Biotherapeutics, Inc.", "dtype": "string"}, {"name": "About the Principal Super Savers Study", "dtype": "string"}, {"name": "About Target Date Funds:", "dtype": "string"}, {"name": "About AmTrust Financial Services, Inc.", "dtype": "string"}, {"name": "About Sovereign Wealth Fund Institute", "dtype": "string"}, {"name": "About AG Mortgage Investment Trust, Inc.", "dtype": "string"}, {"name": "About Angelo, Gordon & Co., L.P.", "dtype": "string"}, {"name": "About Lob", "dtype": "string"}, {"name": "About Climate Impact Partners", "dtype": "string"}, {"name": "About CarbonNeutral\u00ae certification", "dtype": "string"}, {"name": "About Edgewater Wireless", "dtype": "string"}, {"name": "About Cincoze", "dtype": "string"}, {"name": "About TransPerfect", "dtype": "string"}, {"name": "About Seso:", "dtype": "string"}, {"name": "About Vyond", "dtype": "string"}, {"name": "About Pliant", "dtype": "string"}, {"name": "About Entegris", "dtype": "string"}, {"name": "About FlexTrade Systems", "dtype": "string"}, {"name": "About UBS Asset Management:", "dtype": "string"}, {"name": "About Immersion", "dtype": "string"}, {"name": "About Faurecia", "dtype": "string"}, {"name": "About BankUnited, Inc.", "dtype": "string"}, {"name": "About Archer", "dtype": "string"}, {"name": "About Northspyre", "dtype": "string"}, {"name": "About Gastric Cancer", "dtype": "string"}, {"name": "About DESTINY-Gastric01", "dtype": "string"}, {"name": "About the Collaboration between Daiichi Sankyo and AstraZeneca", "dtype": "string"}, {"name": "About Lakeview Community Partners Limited", "dtype": "string"}, {"name": "About SBA Communications Corporation", "dtype": "string"}, {"name": "About Basis Theory", "dtype": "string"}, {"name": "About Dassault Syst\u00e8mes", "dtype": "string"}, {"name": "About McPhy", "dtype": "string"}, {"name": "About Visiativ", "dtype": "string"}, {"name": "About Getty", "dtype": "string"}, {"name": "About automatic world generation acceleration", "dtype": "string"}, {"name": "About the publication of the beta version", "dtype": "string"}, {"name": "About MATRIX Inc.", "dtype": "string"}, {"name": "About MATRIX GENESIS LABS (MGL)", "dtype": "string"}, {"name": "About MetaReal Co., Ltd.", "dtype": "string"}, {"name": "About OWC", "dtype": "string"}, {"name": "About Elior Group", "dtype": "string"}, {"name": "About FarEye", "dtype": "string"}, {"name": "About Dole plc", "dtype": "string"}, {"name": "About Forbright Bank:", "dtype": "string"}, {"name": "About Trez Capital", "dtype": "string"}, {"name": "About Sharp/NEC", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 5620857.333333333, "num_examples": 578}, {"name": "test", "num_bytes": 709900.6666666666, "num_examples": 73}, {"name": "valid", "num_bytes": 700176.0, "num_examples": 72}], "download_size": 5767270, "dataset_size": 7030934.0}}
2023-01-02T06:11:37+00:00
6cd515d1fa7d4d480f1cb06223f4abb9e3e765f0
# Dataset Card for "CUB-SD" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) The CUB dataset re-created using Stable Diffusion 2.0 <table> <tr> <td><img alt="Showcase" src="https://huggingface.co/datasets/taesiri/CUB-SD/resolve/main/sample_images/4__Blue%20Jay.png"/></td> <td><img alt="Showcase" src="https://huggingface.co/datasets/taesiri/CUB-SD/resolve/main/sample_images/11__Kentucky%20Warbler.png"/></td> </tr> <tr> <td><img alt="Showcase" src="https://huggingface.co/datasets/taesiri/CUB-SD/resolve/main/sample_images/9__House%20Sparrow.png"/></td> <td><img alt="Showcase" src="https://huggingface.co/datasets/taesiri/CUB-SD/resolve/main/sample_images/23__Scarlet%20Tanager.png"/></td> </tr> </table>
taesiri/CUB-SD
[ "region:us" ]
2023-01-02T06:29:12+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "001.Black_footed_Albatross", "1": "002.Laysan_Albatross", "2": "003.Sooty_Albatross", "3": "004.Groove_billed_Ani", "4": "005.Crested_Auklet", "5": "006.Least_Auklet", "6": "007.Parakeet_Auklet", "7": "008.Rhinoceros_Auklet", "8": "009.Brewer_Blackbird", "9": "010.Red_winged_Blackbird", "10": "011.Rusty_Blackbird", "11": "012.Yellow_headed_Blackbird", "12": "013.Bobolink", "13": "014.Indigo_Bunting", "14": "015.Lazuli_Bunting", "15": "016.Painted_Bunting", "16": "017.Cardinal", "17": "018.Spotted_Catbird", "18": "019.Gray_Catbird", "19": "020.Yellow_breasted_Chat", "20": "021.Eastern_Towhee", "21": "022.Chuck_will_Widow", "22": "023.Brandt_Cormorant", "23": "024.Red_faced_Cormorant", "24": "025.Pelagic_Cormorant", "25": "026.Bronzed_Cowbird", "26": "027.Shiny_Cowbird", "27": "028.Brown_Creeper", "28": "029.American_Crow", "29": "030.Fish_Crow", "30": "031.Black_billed_Cuckoo", "31": "032.Mangrove_Cuckoo", "32": "033.Yellow_billed_Cuckoo", "33": "034.Gray_crowned_Rosy_Finch", "34": "035.Purple_Finch", "35": "036.Northern_Flicker", "36": "037.Acadian_Flycatcher", "37": "038.Great_Crested_Flycatcher", "38": "039.Least_Flycatcher", "39": "040.Olive_sided_Flycatcher", "40": "041.Scissor_tailed_Flycatcher", "41": "042.Vermilion_Flycatcher", "42": "043.Yellow_bellied_Flycatcher", "43": "044.Frigatebird", "44": "045.Northern_Fulmar", "45": "046.Gadwall", "46": "047.American_Goldfinch", "47": "048.European_Goldfinch", "48": "049.Boat_tailed_Grackle", "49": "050.Eared_Grebe", "50": "051.Horned_Grebe", "51": "052.Pied_billed_Grebe", "52": "053.Western_Grebe", "53": "054.Blue_Grosbeak", "54": "055.Evening_Grosbeak", "55": "056.Pine_Grosbeak", "56": "057.Rose_breasted_Grosbeak", "57": "058.Pigeon_Guillemot", "58": "059.California_Gull", "59": "060.Glaucous_winged_Gull", "60": "061.Heermann_Gull", "61": "062.Herring_Gull", "62": "063.Ivory_Gull", "63": "064.Ring_billed_Gull", "64": "065.Slaty_backed_Gull", "65": "066.Western_Gull", "66": "067.Anna_Hummingbird", "67": "068.Ruby_throated_Hummingbird", "68": "069.Rufous_Hummingbird", "69": "070.Green_Violetear", "70": "071.Long_tailed_Jaeger", "71": "072.Pomarine_Jaeger", "72": "073.Blue_Jay", "73": "074.Florida_Jay", "74": "075.Green_Jay", "75": "076.Dark_eyed_Junco", "76": "077.Tropical_Kingbird", "77": "078.Gray_Kingbird", "78": "079.Belted_Kingfisher", "79": "080.Green_Kingfisher", "80": "081.Pied_Kingfisher", "81": "082.Ringed_Kingfisher", "82": "083.White_breasted_Kingfisher", "83": "084.Red_legged_Kittiwake", "84": "085.Horned_Lark", "85": "086.Pacific_Loon", "86": "087.Mallard", "87": "088.Western_Meadowlark", "88": "089.Hooded_Merganser", "89": "090.Red_breasted_Merganser", "90": "091.Mockingbird", "91": "092.Nighthawk", "92": "093.Clark_Nutcracker", "93": "094.White_breasted_Nuthatch", "94": "095.Baltimore_Oriole", "95": "096.Hooded_Oriole", "96": "097.Orchard_Oriole", "97": "098.Scott_Oriole", "98": "099.Ovenbird", "99": "100.Brown_Pelican", "100": "101.White_Pelican", "101": "102.Western_Wood_Pewee", "102": "103.Sayornis", "103": "104.American_Pipit", "104": "105.Whip_poor_Will", "105": "106.Horned_Puffin", "106": "107.Common_Raven", "107": "108.White_necked_Raven", "108": "109.American_Redstart", "109": "110.Geococcyx", "110": "111.Loggerhead_Shrike", "111": "112.Great_Grey_Shrike", "112": "113.Baird_Sparrow", "113": "114.Black_throated_Sparrow", "114": "115.Brewer_Sparrow", "115": "116.Chipping_Sparrow", "116": "117.Clay_colored_Sparrow", "117": "118.House_Sparrow", "118": "119.Field_Sparrow", "119": "120.Fox_Sparrow", "120": "121.Grasshopper_Sparrow", "121": "122.Harris_Sparrow", "122": "123.Henslow_Sparrow", "123": "124.Le_Conte_Sparrow", "124": "125.Lincoln_Sparrow", "125": "126.Nelson_Sharp_tailed_Sparrow", "126": "127.Savannah_Sparrow", "127": "128.Seaside_Sparrow", "128": "129.Song_Sparrow", "129": "130.Tree_Sparrow", "130": "131.Vesper_Sparrow", "131": "132.White_crowned_Sparrow", "132": "133.White_throated_Sparrow", "133": "134.Cape_Glossy_Starling", "134": "135.Bank_Swallow", "135": "136.Barn_Swallow", "136": "137.Cliff_Swallow", "137": "138.Tree_Swallow", "138": "139.Scarlet_Tanager", "139": "140.Summer_Tanager", "140": "141.Artic_Tern", "141": "142.Black_Tern", "142": "143.Caspian_Tern", "143": "144.Common_Tern", "144": "145.Elegant_Tern", "145": "146.Forsters_Tern", "146": "147.Least_Tern", "147": "148.Green_tailed_Towhee", "148": "149.Brown_Thrasher", "149": "150.Sage_Thrasher", "150": "151.Black_capped_Vireo", "151": "152.Blue_headed_Vireo", "152": "153.Philadelphia_Vireo", "153": "154.Red_eyed_Vireo", "154": "155.Warbling_Vireo", "155": "156.White_eyed_Vireo", "156": "157.Yellow_throated_Vireo", "157": "158.Bay_breasted_Warbler", "158": "159.Black_and_white_Warbler", "159": "160.Black_throated_Blue_Warbler", "160": "161.Blue_winged_Warbler", "161": "162.Canada_Warbler", "162": "163.Cape_May_Warbler", "163": "164.Cerulean_Warbler", "164": "165.Chestnut_sided_Warbler", "165": "166.Golden_winged_Warbler", "166": "167.Hooded_Warbler", "167": "168.Kentucky_Warbler", "168": "169.Magnolia_Warbler", "169": "170.Mourning_Warbler", "170": "171.Myrtle_Warbler", "171": "172.Nashville_Warbler", "172": "173.Orange_crowned_Warbler", "173": "174.Palm_Warbler", "174": "175.Pine_Warbler", "175": "176.Prairie_Warbler", "176": "177.Prothonotary_Warbler", "177": "178.Swainson_Warbler", "178": "179.Tennessee_Warbler", "179": "180.Wilson_Warbler", "180": "181.Worm_eating_Warbler", "181": "182.Yellow_Warbler", "182": "183.Northern_Waterthrush", "183": "184.Louisiana_Waterthrush", "184": "185.Bohemian_Waxwing", "185": "186.Cedar_Waxwing", "186": "187.American_Three_toed_Woodpecker", "187": "188.Pileated_Woodpecker", "188": "189.Red_bellied_Woodpecker", "189": "190.Red_cockaded_Woodpecker", "190": "191.Red_headed_Woodpecker", "191": "192.Downy_Woodpecker", "192": "193.Bewick_Wren", "193": "194.Cactus_Wren", "194": "195.Carolina_Wren", "195": "196.House_Wren", "196": "197.Marsh_Wren", "197": "198.Rock_Wren", "198": "199.Winter_Wren", "199": "200.Common_Yellowthroat"}}}}], "splits": [{"name": "test", "num_bytes": 5095337196.0, "num_examples": 6000}], "download_size": 5009478428, "dataset_size": 5095337196.0}}
2023-01-02T06:48:12+00:00
d0088e8b34e22d9473ede9c5b75abdf57915f56d
kabir5297/EngASRwithCVWav
[ "license:apache-2.0", "region:us" ]
2023-01-02T08:42:54+00:00
{"license": "apache-2.0"}
2023-01-02T09:21:44+00:00
4d25d534c42edc1a204468be223ab359828c8e29
# AutoTrain Dataset for project: fine_tune_table_tm2 ## Dataset Description This dataset has been automatically processed by AutoTrain for project fine_tune_table_tm2. ### 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 [ { "text": "List all PO headers with a valid vendor record in database", "target": "select * from RETAILBUYER_POHEADER P inner join RETAILBUYER_VENDOR V\non P.VENDOR_ID = V.VENDOR_ID" }, { "text": "List all details of PO headers which have a vendor in vendor table", "target": "select * from RETAILBUYER_POHEADER P inner join RETAILBUYER_VENDOR V\non P.VENDOR_ID = V.VENDOR_ID" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', 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 | 32 | | valid | 17 |
Aman6917/autotrain-data-fine_tune_table_tm2
[ "task_categories:summarization", "region:us" ]
2023-01-02T11:48:55+00:00
{"task_categories": ["summarization"]}
2023-01-03T12:38:25+00:00
78ed19793e07692925fba02292dc6d58b2d46404
This dataset consists of three CSV files, namely: 'cs.csv', 'ds.csv', and 'p.csv'. Each CSV file includes the data for the questions asked on a Stack Exchange (SE) question-answering community, from the creation of the community until May 2021. - 'cs.csv' --> [Computer Science SE](https://cs.stackexchange.com/) - 'ds.csv' --> [Data Science SE](https://datascience.stackexchange.com/) - 'p.csv' --> [Political Science SE](https://politics.stackexchange.com/) Each CSV file has the following columns: - `id`: the question id - `title`: the title of the question - `body`: the body or text of the question - `tags`: the list of tags assigned to the question - `label`: a label indicating whether the question is resolved or not (0: not resolved; 1: resolved) The dataset was used in these researches: - [A deep learning-based approach for identifying unresolved questions on Stack Exchange Q&A communities through graph-based communication modelling](https://doi.org/10.1007/s41060-023-00454-0) - [Survival analysis for user disengagement prediction: question-and-answering communitiesโ€™ case](https://doi.org/10.1007/s13278-022-00914-8)
habedi/stack-exchange-dataset
[ "task_categories:text-classification", "task_categories:question-answering", "size_categories:10K<n<100K", "language:en", "license:cc", "region:us" ]
2023-01-02T12:13:24+00:00
{"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "question-answering"], "pretty_name": "Stack Exchange -- Question Dataset"}
2023-11-29T06:48:06+00:00
475c56aafb9b1b0e3c5b197ee0990d0511861542
# Dataset Card for "code-review-instruct-critique-revision" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dahoas/code-review-instruct-critique-revision
[ "region:us" ]
2023-01-02T12:21:35+00:00
{"dataset_info": {"features": [{"name": "body", "dtype": "string"}, {"name": "answer", "struct": [{"name": "body", "dtype": "string"}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "meta_data", "struct": [{"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "ParentId", "dtype": "string"}, {"name": "Score", "dtype": "string"}]}]}, {"name": "comments", "list": [{"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "body", "dtype": "string"}]}, {"name": "meta_data", "struct": [{"name": "AcceptedAnswerId", "dtype": "string"}, {"name": "CommentCount", "dtype": "string"}, {"name": "ContentLicense", "dtype": "string"}, {"name": "CreationDate", "dtype": "string"}, {"name": "Id", "dtype": "string"}, {"name": "Score", "dtype": "string"}, {"name": "Tags", "sequence": "string"}, {"name": "Title", "dtype": "string"}]}, {"name": "question_id", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 322516541, "num_examples": 32800}], "download_size": 127604867, "dataset_size": 322516541}}
2023-01-08T15:02:44+00:00
7f934091a4ac75424a9a7ff4b28dd53a962622e4
Glac1er/idktest
[ "license:unknown", "region:us" ]
2023-01-02T13:02:28+00:00
{"license": "unknown"}
2023-01-07T20:03:22+00:00
12399f1eb09fbf1305d580eb9bbebdc5e6d0cb01
# Dataset Card for echr_rational ### Dataset Summary [Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts](https://arxiv.org/pdf/2210.13836.pdf) This work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To mitigate this, we use domain expertise to strategically identify statistically predictive but legally irrelevant information. We adopt adversarial training to prevent the system from relying on it. We evaluate our deconfounded models by employing interpretability techniques and comparing to expert annotations. Quantitative experiments and qualitative analysis show that our deconfounded model consistently aligns better with expert rationales than baselines trained for prediction only. We further contribute a set of reference expert annotations to the validation and testing partitions of an existing benchmark dataset of European Court of Human Rights cases ### Languages English # Citation Information @article{santosh2022deconfounding, title={Deconfounding Legal Judgment Prediction for European Court of Human Rights Cases Towards Better Alignment with Experts}, author={Santosh, TYS and Xu, Shanshan and Ichim, Oana and Grabmair, Matthias}, journal={arXiv preprint arXiv:2210.13836}, year={2022} }
TUMLegalTech/echr_rational
[ "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "language:en", "license:afl-3.0", "arxiv:2210.13836", "region:us" ]
2023-01-02T13:13:23+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": "afl-3.0", "multilinguality": ["monolingual"], "size_categories": [50]}
2023-01-06T14:29:05+00:00
cf4d879b7ffe35b240659a5b541484c3ec0da6ba
Dataset with Prolog code / query pairs and execution results.
alex43219/prolog-dataset-full
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:code", "region:us" ]
2023-01-02T13:30:20+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["code"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["other"], "task_ids": [], "pretty_name": "Prolog dataset", "tags": []}
2023-01-02T16:43:04+00:00
d05d1b5797d4e1d020ab972021f0df669e374e92
![](https://www.selas.ai/assets/logo-selas.86b7b0b6.svg) ### Description Extended dataset infered by the name entity recognition model [en_ner_prompting](https://huggingface.co/teo-sanchez/en_ner_prompting). This model has been trained on hand-annotated prompts from [poloclub/diffusiondb](https://huggingface.co/datasets/poloclub/diffusiondb). This dataset is hence infered by this model and can comprise mistakes, especially on certain categories (cf. model card). The entities comprise 7 main categories and 11 subcategories for a total of 16 categories, extracted from a topic analysis made with [BERTopic](https://maartengr.github.io/BERTopic/index.html). The topic analysis can be explored [the following visualization](https://teo-sanchez.github.io/projects/prompting_map.html). ``` โ”œโ”€โ”€ medium/ โ”‚ โ”œโ”€โ”€ photography โ”‚ โ”œโ”€โ”€ painting โ”‚ โ”œโ”€โ”€ rendering โ”‚ โ””โ”€โ”€ illustration โ”œโ”€โ”€ influence/ โ”‚ โ”œโ”€โ”€ artist โ”‚ โ”œโ”€โ”€ genre โ”‚ โ”œโ”€โ”€ artwork โ”‚ โ””โ”€โ”€ repository โ”œโ”€โ”€ light โ”œโ”€โ”€ color โ”œโ”€โ”€ composition โ”œโ”€โ”€ detail โ””โ”€โ”€ context/ โ”œโ”€โ”€ era โ”œโ”€โ”€ weather โ””โ”€โ”€ emotion ``` ### Label Scheme <details> <summary>View label scheme (16 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `color`, `composition`, `context/emotion`, `context/era`, `context/weather`, `detail`, `influence/artist`, `influence/artwork`, `influence/genre`, `influence/repository`, `light`, `medium/illustration`, `medium/painting`, `medium/photography`, `medium/rendering`, `subject` | </details>
teo-sanchez/diffusiondb_ner
[ "language_creators:found", "multilinguality:monolingual", "size_categories:100M<n<1G", "source_datasets:poloclub/diffusiondb", "language:en", "license:cc-by-3.0", "stable diffusion", "prompt engineering", "prompts", "research paper", "region:us" ]
2023-01-02T13:35:10+00:00
{"language_creators": ["found"], "language": ["en"], "license": ["cc-by-3.0"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1G"], "source_datasets": ["poloclub/diffusiondb"], "pretty_name": "NER-DiffusionDB", "layout": "default", "title": "Name Entity Recognition of DiffusionDB", "nav_order": 1, "has_children": false, "tags": ["stable diffusion", "prompt engineering", "prompts", "research paper"]}
2023-01-02T14:24:35+00:00
ddb0790ab02248267a37192dcbc741258601d758
This dataset was created in https://openreview.net/pdf?id=uDlkiCI5N7Y The original source is here: https://drive.google.com/drive/folders/1VDnwRhmguvhKUCZ0_nv54RMGgqfYHGfz Many thanks to Stefan Larson!
jordyvl/RVL-CDIP-N
[ "license:cc-by-3.0", "region:us" ]
2023-01-02T14:13:33+00:00
{"license": "cc-by-3.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "budget", "1": "email", "2": "form", "3": "handwritten", "4": "invoice", "5": "letter", "6": "memo", "7": "news_article", "8": "questionnaire", "9": "resume", "10": "scientific_publication", "11": "specification"}}}}], "splits": [{"name": "test", "num_bytes": 2272995060.864, "num_examples": 1002}], "download_size": 544832160, "dataset_size": 2272995060.864}}
2023-01-02T14:25:47+00:00
d5876b14c70bd456709f78705de9bca920c87dcf
Dataset with Prolog code / query pairs and execution results.
alex43219/prolog-dataset-small-balanced
[ "task_categories:other", "annotations_creators:machine-generated", "language_creators:crowdsourced", "multilinguality:monolingual", "size_categories:100K<n<1M", "language:code", "region:us" ]
2023-01-02T14:16:52+00:00
{"annotations_creators": ["machine-generated"], "language_creators": ["crowdsourced"], "language": ["code"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": [], "task_categories": ["other"], "task_ids": [], "pretty_name": "Prolog dataset", "tags": []}
2023-01-02T16:42:10+00:00
5da7e3c8b920a586b8c36eecba4aaa0152a59a52
# Dataset Card for [financial-reports-sec] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Configurations](#dataset-configurations) - [Usage](#usage) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Summary Statistics](#dataset-summary-statistics) - [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) - [References](#references) - [Citation Information](#citation-information) ## Dataset Description - **Point of Contact: Aman Khan** ### Dataset Summary The dataset contains the annual report of US public firms filing with the SEC EDGAR system from 1993-2020. Each annual report (**10K filing**) is broken into 20 sections. Each section is split into individual sentences. Sentiment labels are provided on a **per filing basis** from the market reaction around the filing date for 3 different time windows _[t-1, t+1]_, _[t-1, t+5]_ and _[t-1, t+30]_. Additional metadata for each filing is included in the dataset. ### Dataset Configurations **Four** configurations are available: - _**large_lite**_: - Contains only the basic features needed. Extra metadata is ommitted. - Features List: - **cik** - **sentence** - **section** - **labels** - **filingDate** - **docID** - **sentenceID** - **sentenceCount** - _**large_full**_: - All features are included. - Features List (excluding those already in the lite verison above): - **name** - **tickers** - **exchanges** - **entityType** - **sic** - **stateOfIncorporation** - **tickerCount** - **acceptanceDateTime** - **form** - **reportDate** - **returns** - _**small_lite**_: - Same as _**large_lite**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits. - _**small_full**_: - Same as _**large_full**_ version except that only (200,000/20,000/20,000) sentences are loaded for (train/test/validation) splits. ### Usage ```python import datasets # Load the lite configuration of the dataset raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "large_lite") # Load a specific split raw_dataset = datasets.load_dataset("JanosAudran/financial-reports-sec", "small_full", split="train") ``` ### Supported Tasks The tasks the dataset can be used directly for includes: - _Masked Language Modelling_ - A model like BERT can be fine-tuned on this corpus of financial text. - _Sentiment Analysis_ - For each annual report a label ["positive", "negative"] is provided based on the market reaction around the filing date (refer to [Annotations](#annotations)). - _Next Sentence Prediction/Sentence Order Prediction_ - Sentences extracted from the filings are in their original order and as such the dataset can be adapted very easily for either of these tasks. ### Languages All sentences are in English. ## Dataset Structure ### Data Instances Refer to dataset preview. ### Data Fields **Feature Name** - Description - Data type - Example/Structure **cik** - 10 digit identifier used by SEC for a firm. - _string_ - '0000001750' **sentence** - A single sentence from the 10-K filing. - _string_ - 'The finance agreement is secured by a first priority security interest in all insurance policies, all unearned premium, return premiums, dividend payments and loss payments thereof.' **section** - The section of the 10-K filing the sentence is located. - _ClassLabel_ - ```python ClassLabel(names=['section_1', 'section_10', 'section_11', 'section_12', 'section_13', 'section_14', 'section_15', 'section_1A', 'section_1B', 'section_2','section_3', 'section_4', 'section_5', 'section_6', 'section_7', 'section_7A','section_8', 'section_9', 'section_9A', 'section_9B'], id=None) ``` **labels** - The sentiment label for the entire filing (_**positve**_ or _**negative**_) based on different time windows. - _Dict of ClassLables_ - ```python { '1d': ClassLabel(names=['positive', 'negative'], id=None), '5d': ClassLabel(names=['positive', 'negative'], id=None), '30d': ClassLabel(names=['positive', 'negative'], id=None) } ``` **filingDate** - The date the 10-K report was filed with the SEC. - _string_ - '2021-03-10' **docID** - Unique ID for identifying the exact 10-K filing. Unique across all configs and splits. Can be used to identify the document from which the sentence came from. - _string_ - '0000001750_10-K_2020' **sentenceID** - Unique ID for identifying the exact sentence. Unique across all configs and splits. - _string_ - '0000001750_10-K_2020_section_1_100' **sentenceCount** - Integer identiying the running sequence for the sentence. Unique **only** for a given config and split. - _string_ - 123 **name** - The name of the filing entity - _string_ - 'Investar Holding Corp' **tickers** - List of ticker symbols for the filing entity. - _List of strings_ - ['ISTR'] **exchanges** - List of exchanges for the filing entity. - _List of strings_ - ['Nasdaq'] **entityType** - The type of entity as identified in the 10-K filing. - _string_ - 'operating' **sic** - Four digit SIC code for the filing entity. - _string_ - '6022' **stateOfIncorporation** - Two character code for the state of incorporation for the filing entity. - _string_ - 'LA' **tickerCount** - _**Internal use**_. Count of ticker symbols. Always 1. - _int_ - 1 **acceptanceDateTime** - The full timestamp of when the filing was accepted into the SEC EDGAR system. - _string_ - '2021-03-10T14:26:11.000Z' **form** - The type of filing. Always 10-K in the dataset. - _string_ - '10-K' **reportDate** - The last date in the fiscal year for which the entity is filing the report. - _string_ - '2020-12-31' **returns** - _**Internal use**_. The prices and timestamps used to calculate the sentiment labels. - _Dict_ - ```python {'1d': { 'closePriceEndDate': 21.45746421813965, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-03-11T00:00:00-05:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': 0.03912226855754852 }, '5d': { 'closePriceEndDate': 21.743167877197266, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-03-15T00:00:00-04:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': 0.052958063781261444 }, '30d': { 'closePriceEndDate': 20.63919448852539, 'closePriceStartDate': 20.64960479736328, 'endDate': '2021-04-09T00:00:00-04:00', 'startDate': '2021-03-09T00:00:00-05:00', 'ret': -0.0005041408003307879}} ``` ### Data Splits | Config | train | validation | test | | ---------- | ---------: | ---------: | --------: | | large_full | 67,316,227 | 1,585,561 | 2,965,174 | | large_lite | 67,316,227 | 1,585,561 | 2,965,174 | | small_full | 200,000 | 20,000 | 20,000 | | small_lite | 200,000 | 20,000 | 20,000 | ### Dataset Summary Statistics | Variable | count | mean | std | min | 1% | 25% | 50% | 75% | 99% | max | | :-------------------------------- | ---------: | ----: | -----: | -----: | -----: | -----: | ----: | ----: | ----: | --------: | | Unique Firm Count | 4,677 | | | | | | | | | | | Filings Count | 55,349 | | | | | | | | | | | Sentence Count | 71,866,962 | | | | | | | | | | | Filings per Firm | 4,677 | 12 | 9 | 1 | 1 | 4 | 11 | 19 | 27 | 28 | | Return per Filing - 1d | 55,349 | 0.008 | 0.394 | -0.973 | -0.253 | -0.023 | 0 | 0.02 | 0.367 | 77.977 | | Return per Filing - 5d | 55,349 | 0.013 | 0.584 | -0.99 | -0.333 | -0.034 | 0 | 0.031 | 0.5 | 100 | | Return per Filing - 30d | 55,349 | 0.191 | 22.924 | -0.999 | -0.548 | -0.068 | 0.001 | 0.074 | 1 | 5,002.748 | | Sentences per Filing | 55,349 | 1,299 | 654 | 0 | 110 | 839 | 1,268 | 1,681 | 3,135 | 8,286 | | Sentences by Section - section_1 | 55,349 | 221 | 183 | 0 | 0 | 97 | 180 | 293 | 852 | 2,724 | | Sentences by Section - section_10 | 55,349 | 24 | 40 | 0 | 0 | 4 | 6 | 20 | 173 | 1,594 | | Sentences by Section - section_11 | 55,349 | 16 | 47 | 0 | 0 | 3 | 3 | 4 | 243 | 808 | | Sentences by Section - section_12 | 55,349 | 9 | 14 | 0 | 0 | 3 | 4 | 8 | 56 | 1,287 | | Sentences by Section - section_13 | 55,349 | 8 | 20 | 0 | 0 | 3 | 3 | 4 | 79 | 837 | | Sentences by Section - section_14 | 55,349 | 22 | 93 | 0 | 0 | 3 | 3 | 8 | 413 | 3,536 | | Sentences by Section - section_15 | 55,349 | 177 | 267 | 0 | 0 | 9 | 26 | 315 | 1104 | 4,140 | | Sentences by Section - section_1A | 55,349 | 197 | 204 | 0 | 0 | 3 | 158 | 292 | 885 | 2,106 | | Sentences by Section - section_1B | 55,349 | 4 | 31 | 0 | 0 | 1 | 3 | 3 | 13 | 2,414 | | Sentences by Section - section_2 | 55,349 | 16 | 45 | 0 | 0 | 6 | 8 | 13 | 169 | 1,903 | | Sentences by Section - section_3 | 55,349 | 14 | 36 | 0 | 0 | 4 | 5 | 12 | 121 | 2,326 | | Sentences by Section - section_4 | 55,349 | 7 | 17 | 0 | 0 | 3 | 3 | 4 | 66 | 991 | | Sentences by Section - section_5 | 55,349 | 20 | 41 | 0 | 0 | 10 | 15 | 21 | 87 | 3,816 | | Sentences by Section - section_6 | 55,349 | 8 | 29 | 0 | 0 | 3 | 4 | 7 | 43 | 2,156 | | Sentences by Section - section_7 | 55,349 | 265 | 198 | 0 | 0 | 121 | 246 | 373 | 856 | 4,539 | | Sentences by Section - section_7A | 55,349 | 18 | 52 | 0 | 0 | 3 | 9 | 21 | 102 | 3,596 | | Sentences by Section - section_8 | 55,349 | 257 | 296 | 0 | 0 | 3 | 182 | 454 | 1105 | 4,431 | | Sentences by Section - section_9 | 55,349 | 5 | 33 | 0 | 0 | 3 | 3 | 4 | 18 | 2,330 | | Sentences by Section - section_9A | 55,349 | 17 | 16 | 0 | 0 | 8 | 15 | 23 | 50 | 794 | | Sentences by Section - section_9B | 55,349 | 4 | 18 | 0 | 0 | 2 | 3 | 4 | 23 | 813 | | Word count per Sentence | 71,866,962 | 28 | 22 | 1 | 2 | 16 | 24 | 34 | 98 | 8,675 | ## Dataset Creation ### Curation Rationale To create this dataset multiple sources of information have to be cleaned and processed for data merging. Starting from the raw filings: - Useful metadata about the filing and firm was added. - Time windows around the filing date were carefully created. - Stock price data was then added for the windows. - Ambiguous/duplicate records were removed. ### Source Data #### Initial Data Collection and Normalization Initial data was collected and processed by the authors of the research paper [**EDGAR-CORPUS: Billions of Tokens Make The World Go Round**](#references). Market price and returns data was collected from Yahoo Finance. Additional metadata was collected from SEC. #### Who are the source language producers? US public firms filing with the SEC. ### Annotations #### Annotation process Labels for sentiment classification are based on buy-and-hold returns over a fixed time window around the filing date with the SEC i.e. when the data becomes public. Returns are chosen for this process as it reflects the combined market intelligence at parsing the new information in the filings. For each filing date **t** the stock price at **t-1** and **t+W** is used to calculate returns. If, the returns are positive a label of **positive** is assigned else a label of **negative** is assigned. Three different windows are used to assign the labels: - **1d**: _[t-1, t+1]_ - **5d**: _[t-1, t+5]_ - **30d**: _[t-1, t+30]_ The windows are based on calendar days and are adjusted for weekends and holidays. The rationale behind using 3 windows is as follows: - A very short window may not give enough time for all the information contained in the filing to be reflected in the stock price. - A very long window may capture other events that drive stock price for the firm. #### Who are the annotators? Financial market participants. ### Personal and Sensitive Information The dataset contains public filings data from SEC. Market returns data was collected from Yahoo Finance. ## Considerations for Using the Data ### Social Impact of Dataset Low to none. ### Discussion of Biases The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature. ### Other Known Limitations NA ## Additional Information ### Dataset Curators **Aman Khan** ### Licensing Information This dataset is provided under Apache 2.0. ### References - Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, & Prodromos Malakasiotis. (2021). EDGAR-CORPUS [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5589195 ### Citation Information Please use the following to cite this dataset: ``` @ONLINE{financial-reports-sec, author = "Aman Khan", title = "Financial Reports SEC", url = "https://huggingface.co/datasets/JanosAudran/financial-reports-sec" } ```
JanosAudran/financial-reports-sec
[ "task_categories:fill-mask", "task_categories:text-classification", "task_ids:masked-language-modeling", "task_ids:multi-class-classification", "task_ids:sentiment-classification", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:10M<n<100M", "source_datasets:extended|other", "language:en", "license:apache-2.0", "'finance", "financial", "10-K", "10K", "10k", "10-k", "annual", "reports", "sec", "edgar", "sentiment", "firm", "public", "us'", "region:us" ]
2023-01-02T15:21:14+00:00
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["extended|other"], "task_categories": ["fill-mask", "text-classification"], "task_ids": ["masked-language-modeling", "multi-class-classification", "sentiment-classification"], "pretty_name": "US public firm Annual Reports (10-K)", "tags": ["'finance", "financial", "10-K", "10K", "10k", "10-k", "annual", "reports", "sec", "edgar", "sentiment", "firm", "public", "us'"], "dataset_info": [{"config_name": "large_lite", "features": [{"name": "cik", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "section", "dtype": {"class_label": {"names": {"0": "section_1", "1": "section_10", "2": "section_11", "3": "section_12", "4": "section_13", "5": "section_14", "6": "section_15", "7": "section_1A", "8": "section_1B", "9": "section_2", "10": "section_3", "11": "section_4", "12": "section_5", "13": "section_6", "14": "section_7", "15": "section_7A", "16": "section_8", "17": "section_9", "18": "section_9A", "19": "section_9B"}}}}, {"name": "labels", "struct": [{"name": "1d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "5d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "30d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}]}, {"name": "filingDate", "dtype": "string"}, {"name": "docID", "dtype": "string"}, {"name": "sentenceID", "dtype": "string"}, {"name": "sentenceCount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16424576472, "num_examples": 67316227}, {"name": "validation", "num_bytes": 423527281, "num_examples": 1585561}, {"name": "test", "num_bytes": 773116540, "num_examples": 2965174}], "download_size": 13362319126, "dataset_size": 17621220293}, {"config_name": "large_full", "features": [{"name": "cik", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "section", "dtype": {"class_label": {"names": {"0": "section_1", "1": "section_10", "2": "section_11", "3": "section_12", "4": "section_13", "5": "section_14", "6": "section_15", "7": "section_1A", "8": "section_1B", "9": "section_2", "10": "section_3", "11": "section_4", "12": "section_5", "13": "section_6", "14": "section_7", "15": "section_7A", "16": "section_8", "17": "section_9", "18": "section_9A", "19": "section_9B"}}}}, {"name": "labels", "struct": [{"name": "1d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "5d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "30d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}]}, {"name": "filingDate", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "docID", "dtype": "string"}, {"name": "sentenceID", "dtype": "string"}, {"name": "sentenceCount", "dtype": "int64"}, {"name": "tickers", "list": "string"}, {"name": "exchanges", "list": "string"}, {"name": "entityType", "dtype": "string"}, {"name": "sic", "dtype": "string"}, {"name": "stateOfIncorporation", "dtype": "string"}, {"name": "tickerCount", "dtype": "int32"}, {"name": "acceptanceDateTime", "dtype": "string"}, {"name": "form", "dtype": "string"}, {"name": "reportDate", "dtype": "string"}, {"name": "returns", "struct": [{"name": "1d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}, {"name": "5d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}, {"name": "30d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}]}], "splits": [{"name": "train", "num_bytes": 39306095718, "num_examples": 67316227}, {"name": "validation", "num_bytes": 964030458, "num_examples": 1585561}, {"name": "test", "num_bytes": 1785383996, "num_examples": 2965174}], "download_size": 13362319126, "dataset_size": 42055510172}, {"config_name": "small_full", "features": [{"name": "cik", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "section", "dtype": {"class_label": {"names": {"0": "section_1", "1": "section_1A", "2": "section_1B", "3": "section_2", "4": "section_3", "5": "section_4", "6": "section_5", "7": "section_6", "8": "section_7", "9": "section_7A", "10": "section_8", "11": "section_9", "12": "section_9A", "13": "section_9B", "14": "section_10", "15": "section_11", "16": "section_12", "17": "section_13", "18": "section_14", "19": "section_15"}}}}, {"name": "labels", "struct": [{"name": "1d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "5d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "30d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}]}, {"name": "filingDate", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "docID", "dtype": "string"}, {"name": "sentenceID", "dtype": "string"}, {"name": "sentenceCount", "dtype": "int64"}, {"name": "tickers", "list": "string"}, {"name": "exchanges", "list": "string"}, {"name": "entityType", "dtype": "string"}, {"name": "sic", "dtype": "string"}, {"name": "stateOfIncorporation", "dtype": "string"}, {"name": "tickerCount", "dtype": "int32"}, {"name": "acceptanceDateTime", "dtype": "string"}, {"name": "form", "dtype": "string"}, {"name": "reportDate", "dtype": "string"}, {"name": "returns", "struct": [{"name": "1d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}, {"name": "5d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}, {"name": "30d", "struct": [{"name": "closePriceEndDate", "dtype": "float32"}, {"name": "closePriceStartDate", "dtype": "float32"}, {"name": "endDate", "dtype": "string"}, {"name": "startDate", "dtype": "string"}, {"name": "ret", "dtype": "float32"}]}]}], "splits": [{"name": "train", "num_bytes": 128731540, "num_examples": 200000}, {"name": "validation", "num_bytes": 13411689, "num_examples": 20000}, {"name": "test", "num_bytes": 13188331, "num_examples": 20000}], "download_size": 42764380, "dataset_size": 155331560}, {"config_name": "small_lite", "features": [{"name": "cik", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "section", "dtype": {"class_label": {"names": {"0": "section_1", "1": "section_1A", "2": "section_1B", "3": "section_2", "4": "section_3", "5": "section_4", "6": "section_5", "7": "section_6", "8": "section_7", "9": "section_7A", "10": "section_8", "11": "section_9", "12": "section_9A", "13": "section_9B", "14": "section_10", "15": "section_11", "16": "section_12", "17": "section_13", "18": "section_14", "19": "section_15"}}}}, {"name": "labels", "struct": [{"name": "1d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "5d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}, {"name": "30d", "dtype": {"class_label": {"names": {"0": "positive", "1": "negative"}}}}]}, {"name": "filingDate", "dtype": "string"}, {"name": "docID", "dtype": "string"}, {"name": "sentenceID", "dtype": "string"}, {"name": "sentenceCount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 60681688, "num_examples": 200000}, {"name": "validation", "num_bytes": 6677389, "num_examples": 20000}, {"name": "test", "num_bytes": 6351730, "num_examples": 20000}], "download_size": 42764380, "dataset_size": 73710807}]}
2023-01-06T17:44:08+00:00
d23f740082eba235d37aa73b33b1635c6f5ee8fe
# Dataset Card for "test_repo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pyakymenko/test_repo
[ "region:us" ]
2023-01-02T15:25:54+00:00
{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167117.0, "num_examples": 3}], "download_size": 162079, "dataset_size": 167117.0}}
2023-01-02T15:26:01+00:00
2f8fdb2da8146443c7704c4f14bed35d841e79f5
Snorlax51/Harshkumar23
[ "license:artistic-2.0", "region:us" ]
2023-01-02T15:26:50+00:00
{"license": "artistic-2.0"}
2023-01-02T15:26:50+00:00
f1c139eea788f137cf0e20f07fdf0fadee13784e
This dataset contains only 100 hours train data of librispeech_clean. Functionality of librispeech-other and test-clean and dev-clean is unchanged
rohitp1/librispeech_asr_clean
[ "license:cc-by-4.0", "region:us" ]
2023-01-02T15:42:43+00:00
{"license": "cc-by-4.0"}
2023-01-03T18:08:17+00:00
866af329e5cf8a061d8e991f6539b16f24ae3e71
# Dataset Card for "dsn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
styskin/dsn
[ "region:us" ]
2023-01-02T15:57:13+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 2779967.0, "num_examples": 100}], "download_size": 2726219, "dataset_size": 2779967.0}}
2023-01-02T16:19:56+00:00