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davanstrien
/
dataset_cards_with_long_context_embeddins

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cyberagent/crello
2023-09-14T08:33:47.000Z
[ "task_categories:unconditional-image-generation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cdla-permissive-2.0", "graphic design", "design templates", "arxiv:2108.01249", "region:us" ]
cyberagent
null
null
14
496
2023-02-03T01:31:45
--- annotations_creators: - no-annotation language: - en language_creators: - found license: cdla-permissive-2.0 multilinguality: - monolingual pretty_name: crello size_categories: - 10K<n<100K source_datasets: - original tags: - graphic design - design templates task_categories: - unconditional-image-generation task_ids: [] dataset_info: features: - name: id dtype: string - name: length dtype: int64 - name: group dtype: class_label: names: '0': BG '1': EO '2': HC '3': MM '4': SM '5': SMA - name: format dtype: class_label: names: '0': Album Cover '1': Book Cover '2': Brochure '3': Business card '4': Calendar '5': Card '6': Certificate '7': Coupon '8': Email header '9': FB event cover '10': Facebook '11': Facebook AD '12': Facebook cover '13': Flayer '14': Gallery Image '15': Gift Certificate '16': Graphic '17': IGTV Cover '18': Image '19': Infographic '20': Instagram '21': Instagram AD '22': Instagram Highlight Cover '23': Instagram Story '24': Invitation '25': Invoice '26': Label '27': Large Rectangle '28': Leaderboard '29': Letterhead '30': LinkedIn Cover '31': Logo '32': Medium Rectangle '33': Menu '34': Mind Map '35': Mobile Presentation '36': Mood Board '37': Newsletter '38': Photo Book '39': Pinterest '40': Postcard '41': Poster '42': Poster US '43': Presentation '44': Presentation Wide '45': Proposal '46': Recipe Card '47': Resume '48': Schedule Planner '49': Skyscraper '50': Snapchat Geofilter '51': Snapchat Moment Filter '52': Storyboard '53': T-Shirt '54': Ticket '55': Title '56': Tumblr '57': Twitch Offline Banner '58': Twitch Profile Banner '59': Twitter '60': VK Community Cover '61': VK Post with Button '62': VK Universal Post '63': Web Banner '64': Youtube '65': Youtube Thumbnail '66': Zoom Background - name: canvas_width dtype: class_label: names: '0': '1000' '1': '1008' '2': '1024' '3': '1080' '4': '1128' '5': '1190' '6': '1200' '7': '1280' '8': '1296' '9': '1500' '10': '1590' '11': '160' '12': '1600' '13': '1920' '14': '240' '15': '241' '16': '2560' '17': '300' '18': '3000' '19': '336' '20': '360' '21': '396' '22': '419' '23': '420' '24': '432' '25': '500' '26': '537' '27': '540' '28': '560' '29': '576' '30': '595' '31': '600' '32': '635' '33': '728' '34': '735' '35': '792' '36': '800' '37': '841' '38': '842' '39': '851' '40': '940' - name: canvas_height dtype: class_label: names: '0': '1055' '1': '1080' '2': '1102' '3': '1200' '4': '1296' '5': '141' '6': '142' '7': '1440' '8': '1600' '9': '1683' '10': '1728' '11': '191' '12': '1920' '13': '200' '14': '2000' '15': '216' '16': '2340' '17': '240' '18': '250' '19': '2560' '20': '280' '21': '288' '22': '297' '23': '298' '24': '315' '25': '320' '26': '380' '27': '400' '28': '480' '29': '500' '30': '504' '31': '512' '32': '576' '33': '595' '34': '600' '35': '612' '36': '628' '37': '654' '38': '700' '39': '720' '40': '768' '41': '788' '42': '810' '43': '841' '44': '842' '45': '90' - name: category dtype: class_label: names: '0': all '1': beauty '2': businessFinance '3': citiesPlaces '4': educationScience '5': fashionStyle '6': foodDrinks '7': handcraftArt '8': holidaysCelebration '9': homeStuff '10': industry '11': kidsParents '12': leisureEntertainment '13': medical '14': natureWildlife '15': pets '16': realEstateBuilding '17': religions '18': socialActivityCharity '19': sportExtreme '20': technology '21': transportation '22': travelsVacations - name: title dtype: string - name: type sequence: class_label: names: '0': coloredBackground '1': imageElement '2': maskElement '3': svgElement '4': textElement - name: left sequence: float32 - name: top sequence: float32 - name: width sequence: float32 - name: height sequence: float32 - name: opacity sequence: float32 - name: text sequence: string - name: font sequence: class_label: names: '0': '' '1': Abril Fatface '2': Aldrich '3': Alef '4': Alegreya Sans '5': Alfa Slab One '6': Alice '7': Allerta Stencil '8': Allura '9': Amatic Sc '10': Anton '11': Arapey '12': Architects Daughter '13': Arima Madurai '14': Arimo '15': Arizonia '16': Arkana Script '17': Armata '18': Assistant '19': Bad Script '20': Baloo Tamma '21': Bangers '22': Barrio '23': Beacon '24': Bebas Neue '25': Bellefair '26': Bentham '27': Berkshire Swash '28': Bilbo '29': Black Ops One '30': Blogger '31': Breathe '32': Breathe Press '33': Brusher '34': Brusher Free Font '35': Bubbler One '36': Buda '37': Bungee '38': Bungee Shade '39': Cabin Sketch '40': Caesar Dressing '41': Cantarell '42': Carter One '43': Caveat '44': Cedarville Cursive '45': Chathura '46': Clicker Script '47': Comfortaa '48': Contrail One '49': Cookie '50': Copse '51': Cormorant Infant '52': Courgette '53': Cousine '54': Covered By Your Grace '55': Crete Round '56': Cutive Mono '57': Damion '58': Dancing Script '59': David Libre '60': Dawning Of A New Day '61': Delius '62': Delius Swash Caps '63': Didact Gothic '64': Dorsa '65': Dosis '66': Droid Serif '67': Dukomdesign Constantine '68': Eb Garamond '69': Economica '70': El Messiri '71': Elsie '72': Elsie Swash Caps '73': Euphoria Script '74': Ewert '75': Exo 2 '76': Farsan '77': Faster One '78': Fauna One '79': Finger Paint '80': Fjalla One '81': Forum '82': Frank Ruhl Libre '83': Fredericka The Great '84': Gabriela '85': Gaegu '86': Geo '87': Gfs Didot '88': Give You Glory '89': Glass Antiqua '90': Gluk Glametrix '91': Gluk Znikomitno25 '92': Graduate '93': Grand Hotel '94': Gravitas One '95': Great Vibes '96': Gruppo '97': Handlee '98': Happy Monkey '99': Heebo '100': Homemade Apple '101': Iceberg '102': Iceland '103': Im Fell '104': Im Fell Dw Pica Sc '105': Inconsolata '106': Italiana '107': Italianno '108': Jacques Francois Shadow '109': Josefin Sans '110': Josefin Slab '111': Julius Sans One '112': Junge '113': Jura '114': Just Me Again Down Here '115': Kalam '116': Katibeh '117': Kaushan Script '118': Kavivanar '119': Kelly Slab '120': Knewave '121': Knewave Outline '122': Kreon '123': Kristi '124': Kumar One '125': Kumar One Outline '126': Kurale '127': La Belle Aurore '128': Lalezar '129': Lato '130': Lauren '131': League Script '132': Lemon Tuesday '133': Libre Baskerville '134': Limelight '135': Londrina Shadow '136': Londrina Sketch '137': Loved By The King '138': Lovers Quarrel '139': Marcellus Sc '140': Marck Script '141': Mate '142': Maven Pro '143': Meddon '144': Medula One '145': Merienda One '146': Merriweather '147': Mikodacs '148': Miriam Libre '149': Monda '150': Monofett '151': Monsieur La Doulaise '152': Montserrat '153': Montserrat Alternates '154': Mr Dafoe '155': Mr De Haviland '156': Mrs Saint Delafield '157': Mrs Sheppards '158': Neucha '159': Nixie One '160': Nothing You Could Do '161': Noticia Text '162': Nova Square '163': Nunito '164': Offside '165': Okolaks '166': Old Standard Tt '167': Oleo Script '168': Open Sans '169': Open Sans Condensed '170': Oranienbaum '171': Orbitron '172': Oswald '173': Overlock '174': Oxygen '175': Pacifico '176': Pangolin '177': Parisienne '178': Pathway Gothic One '179': Patrick Hand '180': Pattaya '181': Patua One '182': Permanent Marker '183': Petit Formal Script '184': Philosopher '185': Pinyon Script '186': Pirou '187': Play '188': Playball '189': Playfair Display '190': Playlist Caps '191': Playlist Script '192': Podkova '193': Poiret One '194': Pompiere '195': Port Lligat Slab '196': Press Start 2P '197': Prompt '198': Pt Sans '199': Quattrocento '200': Quicksand '201': Racing Sans One '202': Radley '203': Rakkas '204': Raleway '205': Raleway Dots '206': Rammetto One '207': Rationale '208': Reem Kufi '209': Reenie Beanie '210': Righteous '211': Rise '212': Rissa Typeface '213': Roboto '214': Rochester '215': Rock Salt '216': Rokkitt '217': Rosario '218': Rubik '219': Rubik One '220': Ruslan Display '221': Russo One '222': Rye '223': Sacramento '224': Sansita One '225': Satisfy '226': Scope One '227': Secular One '228': Selima Script '229': Sensei '230': Seymour One '231': Shadows Into Light Two '232': Share Tech Mono '233': Sirin Stencil '234': Six Caps '235': Source Serif Pro '236': Space Mono '237': Stalemate '238': Stint Ultra Expanded '239': Sue Ellen Francisco '240': Suez One '241': Sunday '242': Superclarendon Regular '243': Text Me One '244': Tinos '245': Titillium Web '246': Tulpen One '247': Underdog '248': V T323 '249': Vampiro One '250': Varela Round '251': Vast Shadow '252': Vollkorn '253': Waiting For The Sunrise '254': Wire One '255': Yanone Kaffeesatz '256': Yellowtail '257': Yeseva One '258': Yesteryear '259': Zeyada '260': Znikomit '261': Znikomitno24 - name: font_size sequence: float32 - name: text_align sequence: class_label: names: '0': '' '1': center '2': left '3': right - name: angle sequence: float32 - name: capitalize sequence: class_label: names: '0': 'false' '1': 'true' - name: line_height sequence: float32 - name: letter_spacing sequence: float32 - name: suitability sequence: class_label: names: '0': mobile - name: keywords sequence: string - name: industries sequence: class_label: names: '0': artCrafts '1': beautyCosmetics '2': businessFinance '3': corporate '4': ecologyNature '5': educationTraining '6': entertainmentLeisure '7': familyKids '8': fashionStyle '9': foodBeverages '10': healthWellness '11': homeLiving '12': hrRecruitment '13': marketingAds '14': nonProfitCharity '15': petsAnimals '16': realEstateConstruction '17': religionFaith '18': retail '19': services '20': sportFitness '21': techGadgets '22': transportDelivery '23': travelTourism - name: color sequence: sequence: float32 length: 3 - name: image sequence: image splits: - name: train num_bytes: 3322744283.141 num_examples: 18659 - name: test num_bytes: 421990602.771 num_examples: 2371 - name: validation num_bytes: 425905823.995 num_examples: 2391 download_size: 4130251706 dataset_size: 4170640709.9069996 --- # Dataset Card for Crello ## Table of Contents - [Dataset Card for Crello](#dataset-card-for-crello) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [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:** [CanvasVAE github](https://github.com/CyberAgentAILab/canvas-vae) - **Repository:** - **Paper:** [CanvasVAE: Learning to Generate Vector Graphic Documents](https://arxiv.org/abs/2108.01249) - **Leaderboard:** - **Point of Contact:** [Kota Yamaguchi](https://github.com/kyamagu) ### Dataset Summary The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates were collected from [crello.com](https://crello.com) (now [create.vista.com](https://create.vista.com/)) and converted to a low-resolution format suitable for machine learning analysis. ### Supported Tasks and Leaderboards [CanvasVAE](https://arxiv.org/abs/2108.01249) studies unsupervised document generation. ### Languages Almost all design templates use English. ## Dataset Structure ### Data Instances Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check `ClassLabel` features of the dataset for the list of categorical labels. ``` {'id': '592d6c2c95a7a863ddcda140', 'length': 8, 'group': 4, 'format': 20, 'canvas_width': 3, 'canvas_height': 1, 'category': 0, 'title': 'Beauty Blog Ad Woman with Unusual Hairstyle', 'type': [1, 3, 3, 3, 3, 4, 4, 4], 'left': [0.0, -0.0009259259095415473, 0.24444444477558136, 0.5712962746620178, 0.2657407522201538, 0.369228333234787, 0.2739444375038147, 0.44776931405067444], 'top': [0.0, -0.0009259259095415473, 0.37037035822868347, 0.41296297311782837, 0.41296297311782837, 0.8946287035942078, 0.4549448788166046, 0.40591198205947876], 'width': [1.0, 1.0018517971038818, 0.510185182094574, 0.16296295821666718, 0.16296295821666718, 0.30000001192092896, 0.4990740716457367, 0.11388888955116272], 'height': [1.0, 1.0018517971038818, 0.25833332538604736, 0.004629629664123058, 0.004629629664123058, 0.016611294820904732, 0.12458471953868866, 0.02657807245850563], 'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'], 'font': [0, 0, 0, 0, 0, 152, 172, 152], 'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0], 'text_align': [0, 0, 0, 0, 0, 2, 2, 2], 'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'capitalize': [0, 0, 0, 0, 0, 0, 0, 0], 'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0], 'suitability': [0], 'keywords': ['beautiful', 'beauty', 'blog', 'blogging', 'caucasian', 'cute', 'elegance', 'elegant', 'fashion', 'fashionable', 'femininity', 'glamour', 'hairstyle', 'luxury', 'model', 'stylish', 'vogue', 'website', 'woman', 'post', 'instagram', 'ig', 'insta', 'fashion', 'purple'], 'industries': [1, 8, 13], 'color': [[153.0, 118.0, 96.0], [34.0, 23.0, 61.0], [34.0, 23.0, 61.0], [255.0, 255.0, 255.0], [255.0, 255.0, 255.0], [255.0, 255.0, 255.0], [255.0, 255.0, 255.0], [255.0, 255.0, 255.0]], 'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>, <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]} ``` To get a label for categorical values, use the `int2str` method: ```python key = "font" example = dataset[0] dataset.features[key].int2str(example[key]) ``` ### Data Fields In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`. **Canvas attributes** | Field | Type | Shape | Description | | ------------- | ----------- | ------- | --------------------------------------------------------------- | | id | string | () | Template ID from crello.com | | group | categorical | () | Broad design groups, such as social media posts or blog headers | | format | categorical | () | Detailed design formats, such as Instagram post or postcard | | category | categorical | () | Topic category of the design, such as holiday celebration | | canvas_width | categorical | () | Canvas pixel width | | canvas_height | categorical | () | Canvas pixel height | | length | int64 | () | Length of elements | | suitability | categorical | (None,) | List of display tags, only `mobile` tag exists | | keywords | string | (None,) | List of keywords associated to this template | | industries | categorical | (None,) | List of industry tags like `marketingAds` | **Element attributes** | Field | Type | Shape | Description | | -------------- | ----------- | --------- | -------------------------------------------------------------------- | | type | categorical | (None,) | Element type, such as vector shape, image, or text | | left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width | | top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height | | width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width | | height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height | | color | int64 | (None, 3) | Extracted main RGB color of the element | | opacity | float32 | (None,) | Opacity in [0, 1] range | | image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format | | text | string | (None,) | Text content in UTF-8 encoding for text element | | font | categorical | (None,) | Font family name for text element | | font_size | float32 | (None,) | Font size (height) in pixels | | text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element | | angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element | | capitalize | categorical | (None,) | Binary flag to capitalize letters | | line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 | | letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 | Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available. ``` https://create.vista.com/artboard/?template=<template_id> ``` `left` and `top` can be negative because elements can be bigger than the canvas size. ### Data Splits The Crello dataset has 3 splits: train, validation, and test. The current split is generated such that the same title of the original template shows up in only in one split. | Split | Count | | --------- | ----- | | train | 18659 | | validaton | 2391 | | test | 2371 | ### Visualization Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect. ```python import io from typing import Any, Dict import numpy as np import skia def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes: """Render parsed sequence example onto an image and return as PNG bytes.""" canvas_width = int(features["canvas_width"].int2str(example["canvas_width"])) canvas_height = int(features["canvas_height"].int2str(example["canvas_height"])) scale = min(1.0, max_size / canvas_width, max_size / canvas_height) surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height)) with surface as canvas: canvas.scale(scale, scale) for index in range(example["length"]): pil_image = example["image"][index] image = skia.Image.frombytes( pil_image.convert('RGBA').tobytes(), pil_image.size, skia.kRGBA_8888_ColorType) left = example["left"][index] * canvas_width top = example["top"][index] * canvas_height width = example["width"][index] * canvas_width height = example["height"][index] * canvas_height rect = skia.Rect.MakeXYWH(left, top, width, height) paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True) angle = example["angle"][index] with skia.AutoCanvasRestore(canvas): if angle != 0: degree = 180. * angle / np.pi canvas.rotate(degree, left + width / 2., top + height / 2.) canvas.drawImageRect(image, rect, paint=paint) image = surface.makeImageSnapshot() with io.BytesIO() as f: image.save(f, skia.kPNG) return f.getvalue() ``` ## Dataset Creation ### Curation Rationale The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies. ### Source Data #### Initial Data Collection and Normalization The dataset is initially scraped from the former `crello.com` and pre-processed to the above format. #### Who are the source language producers? While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios. ### Personal and Sensitive Information The dataset does not contain any personal information about the creator but may contain a picture of people in the design template. ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in. ### Discussion of Biases The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories. ### Other Known Limitations Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available. https://create.vista.com/artboard/?template=<template_id> ## Additional Information ### Dataset Curators The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu). ### Licensing Information The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`). The distributor ("We") do not own the copyrights of the original design templates. By using the Crello dataset, the user of this dataset ("You") must agree to the [VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/). The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/). **Note** We do not re-distribute the original files as we are not allowed by terms. ### Citation Information @article{yamaguchi2021canvasvae, title={CanvasVAE: Learning to Generate Vector Graphic Documents}, author={Yamaguchi, Kota}, journal={ICCV}, year={2021} } ### Releases 3.1: bugfix release (Feb 16, 2023) - Fix a bug that ignores newline characters in some of the texts 3.0: v3 release (Feb 13, 2023) - Migrate to Hugging Face Hub. - Fix various text rendering bugs. - Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits. - Incorporate a motion picture thumbnail in templates. - Add `title`, `keywords`, `suitability`, and `industries` canvas attributes. - Add `capitalize`, `line_height`, and `letter_spacing` element attributes. 2.0: v2 release (May 26, 2022) - Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes. - Include rendered text element in `image_bytes`. 1.0: v1 release (Aug 24, 2021) ### Contributions Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset.
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hippocrates/qa_train
2023-10-03T03:42:29.000Z
[ "region:us" ]
hippocrates
null
null
0
496
2023-10-02T00:47:31
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: valid path: data/valid-* dataset_info: features: - name: id dtype: string - name: conversations list: - name: from dtype: string - name: value dtype: string - name: text dtype: string splits: - name: train num_bytes: 485067176 num_examples: 404269 - name: valid num_bytes: 4491759 num_examples: 5505 download_size: 241040216 dataset_size: 489558935 --- # Dataset Card for "qa_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
686
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arampacha/rsicd
2022-04-11T15:34:07.000Z
[ "region:us" ]
arampacha
null
null
3
495
2022-04-11T15:31:49
Entry not found
15
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izumi-lab/llm-japanese-dataset
2023-07-04T15:25:14.000Z
[ "size_categories:1M<n<10M", "language:ja", "license:cc-by-sa-4.0", "arxiv:2305.12720", "region:us" ]
izumi-lab
null
null
69
495
2023-04-30T06:13:24
--- license: cc-by-sa-4.0 language: - ja size_categories: - 1M<n<10M --- # llm-japanese-dataset LLM構築用の日本語インストラクション(チャット)データセット 主に，英語で構築されたLLMモデルなどに対して，チャット(Instruction)応答タスクに関してLoRAなどでチューニングするために使用できます． ※様々な公開言語資源を利用させていただきました．関係各位にはこの場を借りて御礼申し上げます． ## updates 5/15にAlpaca datasetがNCにライセンス変更されたことに対応し，安心してご利用いただけるように，データセットから当該データセットをドロップしました． v1.0.1にて，ドロップ後のデータセットをご利用いただけます． ## データの詳細 データの詳細は，以下の論文を参照してください． - 日本語: [https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383](https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/383) - 英語: [https://arxiv.org/abs/2305.12720](https://arxiv.org/abs/2305.12720) - GitHub: [https://github.com/masanorihirano/llm-japanese-dataset](https://github.com/masanorihirano/llm-japanese-dataset) - 最新情報: [llm.msuzuki.me](https://llm.msuzuki.me). なお，Citationには，よろしければ，以下をご利用ください． ``` @preprint{Hirano2023-llmj, title={{llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and its Methodology}}, autor={Masanori HIRANO and Masahiro SUZUKI and Hiroki SAKAJI}, doi={10.48550/arXiv.2305.12720}, archivePrefix={arXiv}, arxivId={2305.12720}, year={2023} } ``` 共同研究，データ提供，各種支援，その他問い合わせは，izumi-llm@socsim.org へ． ## How to use ```python from datasets import load_dataset dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="main") dataset = load_dataset("izumi-lab/llm-japanese-dataset", revision="a.b.c") # for specific version ``` - version `0.1.0` contains bugs - version `0.1.1` contains 8,393,726 data (bug fixed) - version `1.0.0` contains 9,097,388 data (added jqac, wikipedia ja typo corpus) - version `1.0.1` contains 9,045,386 data (dropped alpaca dataset) For more details, see: https://github.com/masanorihirano/llm-japanese-dataset ## LICENSE CC-BY-SA 4.0 (For more details, see: LICENSE, NOTICE.md, NOTICE2.md) ## Note MIT License version is also available on the github release page https://github.com/masanorihirano/llm-japanese-dataset/releases To see more latest information, please go to [llm.msuzuki.me](https://llm.msuzuki.me).
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yentinglin/traditional_mandarin_instructions
2023-10-07T08:45:00.000Z
[ "task_categories:conversational", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:100K<n<1M", "language:zh", "license:cc-by-nc-4.0", "arxiv:2305.13711", "arxiv:2104.09864", "region:us" ]
yentinglin
null
null
14
495
2023-08-10T06:23:46
--- license: cc-by-nc-4.0 task_categories: - conversational - text-generation - text2text-generation language: - zh pretty_name: Traditional Chinese Instruction-tuning Set size_categories: - 100K<n<1M --- # Language Models for Taiwanese Culture <p align="center"> ✍️ <a href="https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2" target="_blank">Online Demo</a> • 🤗 <a href="https://huggingface.co/yentinglin" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/yentinglin56" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/pdf/2305.13711.pdf" target="_blank">[Paper Coming Soon]</a> • 👨️ <a href="https://yentingl.com/" target="_blank">Yen-Ting Lin</a> <br/><br/> <img src="https://www.csie.ntu.edu.tw/~miulab/taiwan-llama/logo-v2.png" width="100"> <br/> <a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE"> <img src="https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg"></a> <a href="https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE"> <img src="https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg"></a> <br/> </p> ## Overview Taiwan-LLaMa is a full parameter fine-tuned model based on LLaMa 2 for Traditional Mandarin applications. **Taiwan-LLaMa v1.0** pretrained on over 5 billion tokens and instruction-tuned on over 490k conversations both in traditional mandarin. ## Demo A live demonstration of the model can be accessed at [Hugging Face Spaces](https://huggingface.co/spaces/yentinglin/Taiwan-LLaMa2). ## Key Features 1. **Traditional Mandarin Support**: The model is fine-tuned to understand and generate text in Traditional Mandarin, making it suitable for Taiwanese culture and related applications. 2. **Instruction-Tuned**: Further fine-tuned on conversational data to offer context-aware and instruction-following responses. 3. **Performance on Vicuna Benchmark**: Taiwan-LLaMa's relative performance on Vicuna Benchmark is measured against models like GPT-4 and ChatGPT. It's particularly optimized for Taiwanese culture. 4. **Flexible Customization**: Advanced options for controlling the model's behavior like system prompt, temperature, top-p, and top-k are available in the demo. ## Work in progress - [ ] **Improved pretraining**: A refined pretraining process (e.g. more data from Taiwan, training strategies) is under development, aiming to enhance model performance for better Taiwanese culture. - [ ] **Extend max length**: Utilizing the Rope mechanism as described in [the paper](https://arxiv.org/abs/2104.09864), the model's length will be extended from 4k to 8k. ## Taiwanese Culture Examples | Question | ChatGPT | **Taiwan LLaMa v1.0** | **Taiwan LLaMa v0.0 (w/o pretraining)** | Llama2_Traditional_Chinese_13b_Chat | |:-|:-|:-|:-|:-| | 以惡地景觀著名的是高雄哪一個景點? | 以惡地地景著名的是高雄市的旗津。 | 高雄以擁有惡地景觀著名的景點是田寮月世界。✅ | 以惡地景觀著名的是高雄市的旗津島。旗津島是一個小島，... | 高雄市內有多個以惡地景觀著名的景點，其中以下面幾個最為著名： 1. 高雄捷運紅線沿途景點：高雄市政府為了推廣高雄 | | 唐鳳是誰？ | 唐鳳（Audrey Tang）是台灣的政治家、社會活動家和軟體工程師。她於2016年擔任台灣行政院數位政務委員，是台灣政府歷史上首位沒有政黨背景的政務官。唐鳳在軟體工程領域有豐富的經驗。 ✅ | 唐鳳是政治評論家、資訊社會學研究者，也是台灣第一位擔任政府數位政委的人。 ✅ | 唐鳳（Tang Feng）是一位著名的中國演員和歌手。他出生於1974年12月17日，出生地點在中國北京。唐鳳在1995年開始他的演藝生涯，並在同年發行了他的首張專輯《我不孤單》。此後，他發行了許多受歡迎的歌曲，並在多部電視劇和電影中擔任主角。唐鳳因其精湛的演技和迷人的歌聲而被譽為“亞洲魅力男王”。他的音樂獲得了多個獎項，包括“華表最佳新男演唱獎”、“華表最佳歌曲獎”和“華表最佳舞蹈獎”等。 | 唐鳳是唐朝時期的一位著名文學家，他的代表作品包括《陰騭》、《狂凡子》等。他的文學風格具有強烈的個性和創意，被譽為「唐代文學之父」。 | | NTU 在哪? | NTU位於新加坡，具體地址是50 Nanyang Avenue, Singapore 639798。 | NTU 是國立臺灣大學的縮寫。它是臺灣的一所頂尖研究型大學，位於臺北市。 ✅| NTU 是台灣的一所大學，位於台北市。✅ | NTU 的總部位於台灣新北市板橋區，佔地面積約為1,000公頃。| | 甚麼是22K? | "22K" 可能指的是金的純度，尤其是在珠寶和金飾方面。 | "22K"是指台灣的大學畢業生的起薪，大約是新台幣22,000元。這個詞被用來形容大學畢業生的起薪偏低，並且在就業市場上對於大學畢業生的需求不足。✅| 22K是指溫度尺度上的22,000K（千度）。1K代表一度，即溫度差距，是攝氏溫標（攝氏度）的一百萬分之一。因此，22K等於-253.15°C（攝氏溫標）或-423.67°F（華氏溫標）。 | 22K是一種簡寫，即22,000。它是一個數字，表示22,000個單位。| | 肉圓要不要加香菜？ | 這是個個人口味的問題，有些人喜歡在肉圓中加入香菜，因為香菜的清香可以增添風味；而有些人則不喜歡香菜的味道，覺得它會蓋過其他食材的味道。所以要不要加香菜，可以根據個人喜好來決定。 | 這是一個個人喜好的問題，有些人喜歡在肉圓裡加香菜，有些人不喜歡。**在台灣**，肉圓加香菜是一種常見的做法，這是一種在肉圓上加香菜的做法。然而，在其他國這是一個文化差異的問題。😊 | 肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。香菜通常在製作過程中炒香，可以增添一些芳香和美味的味道。如果你想要加入香菜，可以在製作過程中炒香一些香菜，然後將其混入肉圓中。肉圓加香菜可以增添一些芳香和美味的口感，如果你喜歡香菜的味道，可以試試加入肉圓中。| 加香菜的肉圓味道更好，但是不加香菜的肉圓也很好吃。如果你喜歡香菜的味道，那麼加上香菜就好了。如果你不喜歡香菜，那麼不加也不會影響肉 | ## Model We provide a number of model checkpoints that we trained. Please find them on Hugging Face [here](https://huggingface.co/models?search=taiwan-llama). Here are some quick links to the checkpoints that are finetuned from LLaMa 2: | **Model** | **13B** | |--------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------| | **Taiwan-LLaMa v1.0** (_better for Taiwanese Culture_) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v1.0" target="_blank">yentinglin/Taiwan-LLaMa-v1.0</a> | | Taiwan-LLaMa v0.9 (partial instruction set) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.9" target="_blank">yentinglin/Taiwan-LLaMa-v0.9</a> | | Taiwan-LLaMa v0.0 (no Traditional Mandarin pretraining) | 🤗 <a href="https://huggingface.co/yentinglin/Taiwan-LLaMa-v0.0" target="_blank">yentinglin/Taiwan-LLaMa-v0.0</a> | ## Data Here are some quick links to the datasets that we used to train the models: | **Dataset** | **Link** | |---------------------------------|-------------------------------------------------------------------------------------------------------------------------------| | **Instruction-tuning** | 🤗 <a href="https://huggingface.co/datasets/yentinglin/traditional_mandarin_instructions" target="_blank">yentinglin/traditional_mandarin_instructions</a> | | Traditional Mandarin Pretraining | 🤗 <a href="https://huggingface.co/datasets/yentinglin/zh_TW_c4" target="_blank">yentinglin/zh_TW_c4</a> | ## Architecture Taiwan-LLaMa is based on LLaMa 2, leveraging transformer architecture, <a href="https://github.com/Dao-AILab/flash-attention" target="_blank">flash attention 2</a>, and bfloat16. It includes: * Pretraining Phase: Pretrained on a vast corpus of over 5 billion tokens, extracted from common crawl in Traditional Mandarin. * Fine-tuning Phase: Further instruction-tuned on over 490k multi-turn conversational data to enable more instruction-following and context-aware responses. ## Generic Capabilities on Vicuna Benchmark The data is translated into traditional mandarin for evaluating the general capability. <img src="./images/zhtw_vicuna_bench_chatgptbaseline.png" width="700"> The scores are calculated with ChatGPT as the baseline, represented as 100%. The other values show the relative performance of different models compared to ChatGPT. | Language Model | Relative Score (%) | |-------------------------------------|--------------------| | GPT-4 | 102.59% | | ChatGPT | 100.00% | | **Taiwan-LLaMa v1.0** | 76.76% | | Claude-Instant-1.2 | 74.04% | | Llama2_Traditional_Chinese_13b_Chat | 56.21% | ## How to deploy the model on my own machine? We recommend hosting models with [🤗 Text Generation Inference](https://github.com/huggingface/text-generation-inference). Please see their [license](https://github.com/huggingface/text-generation-inference/blob/main/LICENSE) for details on usage and limitations. ```bash bash run_text_generation_inference.sh "yentinglin/Taiwan-LLaMa" NUM_GPUS DIR_TO_SAVE_MODEL PORT MAX_INPUT_LEN MODEL_MAX_LEN ``` Prompt format follows vicuna-v1.1 template: ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user} ASSISTANT: ``` ## Setup development environment ```bash conda create -n taiwan-llama python=3.10 -y conda activate taiwan-llama pip install -r requirements.txt ``` ## Citations If you use our code, data, or models in your research, please cite this repository. You can use the following BibTeX entry: ```bibtex @inproceedings{lin-chen-2023-llm, title = "{LLM}-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models", author = "Lin, Yen-Ting and Chen, Yun-Nung", booktitle = "Proceedings of the 5th Workshop on NLP for Conversational AI (NLP4ConvAI 2023)", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.nlp4convai-1.5", pages = "47--58" } @misc{taiwanllama, author={Lin, Yen-Ting and Chen, Yun-Nung}, title={Taiwanese-Aligned Language Models based on Meta-Llama2}, year={2023}, url={https://github.com/adamlin120/Taiwan-LLaMa}, note={Code and models available at https://github.com/adamlin120/Taiwan-LLaMa}, } ``` ## Collaborate With Us If you are interested in contributing to the development of Traditional Mandarin language models, exploring new applications, or leveraging Taiwan-LLaMa for your specific needs, please don't hesitate to contact us. We welcome collaborations from academia, industry, and individual contributors. ## License The code in this project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details. The models included in this project are licensed under the LLAMA 2 Community License. See the [LLAMA2 License](https://github.com/facebookresearch/llama/blob/main/LICENSE) for full details. ## OpenAI Data Acknowledgment The data included in this project were generated using OpenAI's models and are subject to OpenAI's Terms of Use. Please review [OpenAI's Terms of Use](https://openai.com/policies/terms-of-use) for details on usage and limitations. ## Acknowledgements We thank [Meta LLaMA team](https://github.com/facebookresearch/llama) and [Vicuna team](https://github.com/lm-sys/FastChat) for their open-source efforts in democratizing large language models.
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m3hrdadfi/recipe_nlg_lite
2021-07-03T09:34:56.000Z
[ "region:us" ]
m3hrdadfi
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version The dataset we publish contains 7,198 cooking recipes (>7K). It's processed in more careful way and provides more samples than any other dataset in the area.
@misc{RecipeNLGLite, author = {Mehrdad Farahani}, title = {RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Lite)}, year = 2021, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {url{https://github.com/m3hrdadfi/recipe-nlg-lite}}, }
3
494
2022-03-02T23:29:22
# RecipeNLG: A Cooking Recipes Dataset RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation - Lite version The dataset contains `7,198` cooking recipes (`>7K`). It's processed in more careful way and provides more samples than any other dataset in the area. ## How to use ```bash pip install git+https://github.com/huggingface/datasets.git ``` Load `m3hrdadfi/recipe_nlg_lite` dataset using `load_dataset`: ```python from datasets import load_dataset dataset = load_dataset("m3hrdadfi/recipe_nlg_lite") print(dataset) ``` Output: ```text DatasetDict({ train: Dataset({ features: ['uid', 'name', 'description', 'link', 'ner', 'ingredients', 'steps'], num_rows: 6118 }) test: Dataset({ features: ['uid', 'name', 'description', 'link', 'ner', 'ingredients', 'steps'], num_rows: 1080 }) }) ``` ## Examples ```json { "description": "we all know how satisfying it is to make great pork tenderloin, ribs, or a roast but the end of the meal creates a new quandary what do you do with the leftover pork contrary to what you might think, it's not that difficult . how to repurpose your meal is where real cooking creativity comes into play, so let us present to you our favorite pork chop soup recipe . with this recipe, you'll discover how the natural bold flavor of pork gives this hearty soup a lift that a vegetable soup or chicken noodle soup just can't get . it's a dinner recipe to warm you up on a cold winter night or a midday restorative for a long work week . throw all the ingredients in a large pot and let it simmer on the stove for a couple hours, or turn it into a slow cooker recipe and let it percolate for an afternoon . this foolproof recipe transforms your favorite comfort food into an easy meal to warm you up again and again . the health benefits of pork pork is a great option if you're on a low carb diet or trying to up your protein intake . the protein percentage of leaner cuts of pork can be as high as 89 percent pork also provides valuable vitamins and minerals that make pork recipes worthy endeavors . pork has high levels of thiamin and niacin, which other types of meat like beef and lamb lack . they are both b vitamins that aid in several body functions such as metabolism and cell function . pork also delivers a healthy amount of zinc, which aids in brain and immune system function . that makes digging into this pork chop noodle soup all the more alluring . recipe variations this pork soup recipe can be adapted to many diets . if you're following a low carb or ketogenic diet, you can modify the recipe to suit you by leaving out the noodles . if you like, you can add a little crunch by topping it with french fried onions . for cheese lovers, a sprinkle of parmesan cheese can give the soup more body and extra umami flavors . if you're not a noodle lover, this soup recipe works equally well as a potato soup with diced potatoes . if you want to make a southwestern or mexican version, add a can of diced tomatoes and bell peppers for a little extra depth . if you have a penchant for spicy soups, add a little chili powder or red pepper flakes . it's up to you this recipe is great for using up leftover pork chops, but you can make this soup using fresh chops however you decide to do it, you won't be disappointed.", "ingredients": "3.0 bone in pork chops, salt, pepper, 2.0 tablespoon vegetable oil, 2.0 cup chicken broth, 4.0 cup vegetable broth, 1.0 red onion, 4.0 carrots, 2.0 clove garlic, 1.0 teaspoon dried thyme, 0.5 teaspoon dried basil, 1.0 cup rotini pasta, 2.0 stalk celery", "link": "https://www.yummly.com/private/recipe/Pork-Chop-Noodle-Soup-2249011?layout=prep-steps", "name": "pork chop noodle soup", "ner": "bone in pork chops, salt, pepper, vegetable oil, chicken broth, vegetable broth, red onion, carrots, garlic, dried thyme, dried basil, rotini pasta, celery", "steps": "season pork chops with salt and pepper . heat oil in a dutch oven over medium high heat . add chops and cook for about 4 minutes, until golden brown . flip and cook 4 minutes more, until golden brown . transfer chops to a plate and set aside . pour half of chicken broth into pot, scraping all browned bits from bottom . add remaining chicken broth, vegetable broth, onion, carrots, celery and garlic . mix well and bring to a simmer . add 1 quart water, thyme, basil, 2 teaspoons salt and 1 teaspoon pepper . mix well and bring to a simmer . add chops back to pot and return to simmer . reduce heat and simmer for 90 minutes, stirring occasionally, being careful not to break up chops . transfer chops to plate, trying not to break them up . set aside to cool . raise the heat and bring the soup to a boil . add pasta and cook for about 12 minutes, until tender . when the chops are cool, pull them apart, discarding all the bones and fat . add the meat back to soup and stir well . taste for salt and pepper, and add if needed, before serving.", "uid": "dab8b7d0-e0f6-4bb0-aed9-346e80dace1f" } ``` ## Citation ```bibtex @misc{RecipeNLGLite, author = {Mehrdad Farahani}, title = {RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation (Lite)}, year = 2021, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {url{https://github.com/m3hrdadfi/recipe-nlg-lite}}, } ```
5,394
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intfloat/multilingual_cc_news
2023-04-23T08:19:06.000Z
[ "size_categories:100M<n<1B", "language:en", "language:zh", "language:fr", "language:de", "language:af", "language:ar", "region:us" ]
intfloat
\ Multilingual CC-News dataset. This is the processed version from https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual.
null
3
493
2023-03-22T08:25:34
--- size_categories: - 100M<n<1B language: - en - zh - fr - de - af - ar --- ### Dataset Summary This dataset is based on [CloverSearch/cc-news-mutlilingual](https://huggingface.co/datasets/CloverSearch/cc-news-mutlilingual). We add a script to support access multilingual CC-News dataset with HuggingFace datasets API instead of directly downloading raw data files. ### Data Fields - `title`: a `string` feature. - `maintext`: a `string` feature. - `url`: a `string` feature. - `date_publish`: a `string` feature. ### How to use this dataset You can load any subset of CC-News per language: ```python from datasets import load_dataset dataset = load_dataset("intfloat/multilingual_cc_news", languages=["af"]) ``` ## Supported Languages ``` af als am an ar arz as ast av az azb ba bar bcl be bg bh bn bo bpy br bs bxr ca cbk ce ceb ckb co cs cv cy da de diq dsb dty dv el eml en eo es et eu fa fi fr fy ga gd gl gn gom gu gv he hi hif hr hsb ht hu hy ia id ie ilo io is it ja jbo jv ka kk km kn ko krc ku kv kw ky la lb lez li lmo lo lt lv mai mg mhr min mk ml mn mr mrj ms mt mwl my myv mzn nah nap nds ne new nl nn no oc or os pa pam pfl pl pms pnb ps pt qu rm ro ru sa sah sc scn sco sd sh si sk sl so sq sr su sv sw ta te tg th tk tl tr tt tyv ug uk ur uz vec vep vi vls vo wa war wuu xal xmf yi yo yue zh ```
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IlyaGusev/gazeta
2023-02-12T00:01:45.000Z
[ "task_categories:summarization", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:ru", "license:unknown", "arxiv:2006.11063", "region:us" ]
IlyaGusev
null
@InProceedings{10.1007/978-3-030-59082-6_9, author="Gusev, Ilya", editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia", title="Dataset for Automatic Summarization of Russian News", booktitle="Artificial Intelligence and Natural Language", year="2020", publisher="Springer International Publishing", address="Cham", pages="122--134", isbn="978-3-030-59082-6" }
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--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found task_categories: - summarization language: - ru size_categories: - 10K<n<100K license: - unknown multilinguality: - monolingual source_datasets: - original paperswithcode_id: gazeta --- # Dataset Card for Gazeta ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/IlyaGusev/gazeta - **Paper:** [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063) - **Leaderboard:** https://paperswithcode.com/sota/text-summarization-on-gazeta - **Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu) ### Dataset Summary Dataset for automatic summarization of Russian news. News and their summaries are from the Gazeta website. Summaries were parsed as the content of an HTML tag with “description” property. Additional selection of good summaries was performed. There are two versions of this dataset. ### Supported Tasks and Leaderboards Leaderboard on Papers With Code: [text-summarization-on-gazeta](https://paperswithcode.com/sota/text-summarization-on-gazeta). Please use the original [evaluation script](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) with the same parameters. Example: ``` python3 evaluate.py --predicted-path predictions.txt --gold-path targets.txt --language ru --tokenize-after --lower ``` ### Languages The dataset is in Russian. ### Usage Loading version 1.0: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/gazeta', revision="v1.0") ``` Loading version 2.0: ```python from datasets import load_dataset dataset = load_dataset('IlyaGusev/gazeta', revision="v2.0") ``` ### Other datasets Other Russian summarization datasets: * Russian part of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum), parsed from www.bbc.com/russian, 77803 samples * Russian part of [MLSUM](https://huggingface.co/datasets/mlsum), parsed from www.mk.ru, 27063 samples ## Dataset Structure ### Data Instances For each instance, there is a string for the article, a string for the summary, and a string for the url. Additionally, a string for the title and a date are provided. ``` { 'date': '2019-10-01 15:14:05', 'url': 'https://www.gazeta.ru/tech/2019/10/01/12698923/whatsapp_pls.shtml', 'title': 'На последнем издыхании: у кого отключится WhatsApp', 'summary': 'Мессенджер WhatsApp перестанет работать на ряде смартфонов — речь идет о гаджетах на базе операционных систем Android 2.3.7 и iOS 8, которые считаются устаревшими. В компании отмечают, что сервис на этих устройствах может отключиться в любой момент, поэтому будет целесообразно сменить устройство либо обновить ОС.', 'text': 'На официальном сайте мессенджера WhatsApp появилось сообщение о том, что с 1 февраля 2020 года сервис прекратит свою работу на некоторых устаревших смартфонах. Речь идет об устройствах, работающих на базе операционных систем Android 2.3.7 и iOS 8. При этом руководство WhatsApp предупреждает, что даже до обозначенного выше дедлайна функционал мессенджера на этих ОС может быть ограничен. «В связи с тем, что мы не планируем обновлять данные операционные системы, некоторые функции могут перестать работать на них в любое время», — говорится в пресс-релизе компании. Чтобы сохранить возможность пользоваться мессенджером без проблем, следует обновить версию прошивки или приобрести новое, более современное устройство. Сообщается, что на старых версиях операционных систем уже не получится завести новый аккаунт WhatsApp или верифицировать уже существующий. При этом в WhatsApp порекомендовали пользоваться устройствами с Android 4.0.3 и более поздними версиями, а также iOS 9 и более поздними версиями. Ранее стало известно о том, что с 31 декабря 2019 года WhatsApp прекращает поддержку устройств на базе операционной системы Windows Phone, от разработки которой пришлось отказаться. Впрочем, если верить статистике , эти меры вряд ли затронут большое количество пользователей. По состоянию на май 2019 года лишь 0,3% всех владельцев Android все еще пользуются ОС версий 2.3.3–2.3.7. Что же касается iOS, то версия под номером «10» или старше установлена на 5% устройств Apple. Как уже упоминалось выше, выпуск новых гаджетов на Windows Phone и вовсе прекращен ее создателем. В середине сентября экс-сотрудник АНБ Эдвард Сноуден раскритиковал WhatsApp за несовершенную систему защиты, порекомендовав политикам пользоваться другими средствами связи. Журналист французской радиостанции France Inter отметил, что президент Франции Эмманюэль Макрон для связи использует Telegram, а премьер-министр страны Эдуар Филипп — WhatsApp. Сноуден назвал такое решение «большой ошибкой», учитывая серьезные посты, которые занимают Макрон и Филипп. По словам Сноудена, эти сервисы безопаснее обычных SMS-сообщений, но все еще «чрезвычайно опасны, если вы премьер-министр». Больше всего претензий у информатора к WhatsApp, который стал частью активов корпорации Facebook в 2014 году. Эдвард Сноуден отметил, что после приобретения мессенджера Facebook «слой за слоем» снимает различные уровни защиты сервиса, чтобы при необходимости читать переписку своих пользователей. Ранее с критикой в адрес WhatsApp выступил и глава Telegram Павел Дуров. По словам предпринимателя, после устранения одной «дыры» в мессенджере тут же появляются новые. «Все выявленные проблемы позволяют вести слежку, выглядят и функционируют как бэкдоры», — заявил Дуров. При этом Дуров подчеркнул, что WhatsApp мог быть вынужден установить бэкдоры по указанию ФБР. В июне руководство WhatsApp заявило о том, что их сервис готов судиться с юзерами за нарушение правил пользования. В список нарушений входит использование программы «не в личных целях» и применение автоматической рассылки сообщений. По данным пресс-службы WhatsApp, уже сейчас обнаружены и заморожены «миллионы аккаунтов», пойманных на «злоупотреблении». «Наша платформа изначально создавалась, чтобы помогать людям общаться с их друзьями и любимыми... Используя информацию приложения, мы нашли и заблокировали миллионы злоупотребляющих аккаунтов от использования нашей сети», – заявили в WhatsApp. В частности, нарушение происходит, если компания публично заявляет о возможности использовать WhatsApp, нарушая при этом правила пользования мессенджером. «Ничто в этом объявлении не ограничивает право WhatsApp от применения своих условий с использованием технологий. Классификаторы на основе machine learning нам в этом помогают, и мы продолжим их использовать», – добавили в команде приложения.', } ``` Some dataset statistics are below: | Feature | Mean Token Count | Mean Sentence Count | |:---------|:---------|--------------------------------------------------| | Text | 767 | 37 | | Summary | 50 | 3 | ### Data Splits | Dataset Split | v1, Number of Instances in Split | v2, Number of Instances in Split | |:---------|:---------|:---------| | Train | 52,400 | 60,964 | | Validation | 5,265 | 6,369 | | Test | 5,770 | 6,793 | ## Dataset Creation ### Curation Rationale When the first version of the dataset was collected, there were no other datasets for Russian text summarization. Even now, it is one of the few datasets for this task. ### Source Data #### Initial Data Collection and Normalization * The source of data is the [Gazeta](https://www.gazeta.ru/) website. * Parsing scripts are [here](https://github.com/IlyaGusev/gazeta/tree/master/parser). * Cleaning and normalization Colab notebook is [here](https://colab.research.google.com/drive/1Ed_chVrslp_7vJNS3PmRC0_ZJrRQYv0C) #### Who are the source language producers? Texts and summaries were written by journalists at [Gazeta](https://www.gazeta.ru/). ### Annotations #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases It is a dataset from a single source. Thus it has a constrained text style and event perspective. ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The data was collected by Ilya Gusev. ### Licensing Information Legal basis for distribution of the dataset: https://www.gazeta.ru/credits.shtml, paragraph 2.1.2. All rights belong to "www.gazeta.ru". Usage of this dataset is possible only for personal purposes on a non-commercial basis. ### Citation Information ```bibtex @InProceedings{10.1007/978-3-030-59082-6_9, author="Gusev, Ilya", editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia", title="Dataset for Automatic Summarization of Russian News", booktitle="Artificial Intelligence and Natural Language", year="2020", publisher="Springer International Publishing", address="Cham", pages="122--134", isbn="978-3-030-59082-6" } ``` ### Contributions [N/A]
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mattmdjaga/human_parsing_dataset
2023-09-11T09:07:44.000Z
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "size_categories:10K<n<100K", "region:us" ]
mattmdjaga
null
null
10
491
2023-03-30T17:59:37
--- size_categories: - 10K<n<100K task_categories: - image-segmentation task_ids: - semantic-segmentation dataset_info: features: - name: image dtype: image - name: mask dtype: image splits: - name: train num_bytes: 5892290030.116 num_examples: 17706 download_size: 5893438158 dataset_size: 5892290030.116 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Human parsing data (ATR) ## 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:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset has 17,706 images and mask pairs. It is just a copy of [Deep Human Parsing](https://github.com/lemondan/HumanParsing-Dataset) ATR dataset. The mask labels are: "0": "Background", "1": "Hat", "2": "Hair", "3": "Sunglasses", "4": "Upper-clothes", "5": "Skirt", "6": "Pants", "7": "Dress", "8": "Belt", "9": "Left-shoe", "10": "Right-shoe", "11": "Face", "12": "Left-leg", "13": "Right-leg", "14": "Left-arm", "15": "Right-arm", "16": "Bag", "17": "Scarf" ### 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 ```bibtex @ARTICLE{ATR, author={Xiaodan Liang and Si Liu and Xiaohui Shen and Jianchao Yang and Luoqi Liu and Jian Dong and Liang Lin and Shuicheng Yan}, journal={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={Deep Human Parsing with Active Template Regression}, year={2015}, volume={37}, number={12}, pages={2402-2414}, doi={10.1109/TPAMI.2015.2408360}, ISSN={0162-8828}, month={Dec}} @InProceedings{CO-CNN, author={Xiaodan Liang and Chunyan Xu and Xiaohui Shen and Jianchao Yang and Si Liu and Jinhui Tang and Liang Lin and Shuicheng Yan}, journal ={Pattern Analysis and Machine Intelligence, IEEE Transactions on}, title={ICCV}, year={2015}, } ```
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crystina-z/mbert-mrtydi-corpus
2022-02-01T22:09:24.000Z
[ "region:us" ]
crystina-z
null
null
0
490
2022-03-02T23:29:22
Entry not found
15
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nchlt
2023-01-25T14:41:21.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:original", "language:af", "language:nr", "language:nso", "language:ss", "language:tn", "language:ts", "language:ve", "language:xh", "language:zu", "license:cc-by-2.5", "region:us" ]
null
The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context.
@inproceedings{eiselen2014developing, title={Developing Text Resources for Ten South African Languages.}, author={Eiselen, Roald and Puttkammer, Martin J}, booktitle={LREC}, pages={3698--3703}, year={2014} }
4
489
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - af - nr - nso - ss - tn - ts - ve - xh - zu license: - cc-by-2.5 multilinguality: - multilingual size_categories: - 1K<n<10K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: NCHLT dataset_info: - config_name: af features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3955069 num_examples: 8961 download_size: 25748344 dataset_size: 3955069 - config_name: nr features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3188781 num_examples: 9334 download_size: 20040327 dataset_size: 3188781 - config_name: xh features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 2365821 num_examples: 6283 download_size: 14513302 dataset_size: 2365821 - config_name: zu features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3951366 num_examples: 10955 download_size: 25097584 dataset_size: 3951366 - config_name: nso-sepedi features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3322296 num_examples: 7116 download_size: 22077376 dataset_size: 3322296 - config_name: nso-sesotho features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 4427898 num_examples: 9471 download_size: 30421109 dataset_size: 4427898 - config_name: tn features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3812339 num_examples: 7943 download_size: 25905236 dataset_size: 3812339 - config_name: ss features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3431063 num_examples: 10797 download_size: 21882224 dataset_size: 3431063 - config_name: ve features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3941041 num_examples: 8477 download_size: 26382457 dataset_size: 3941041 - config_name: ts features: - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': OUT '1': B-PERS '2': I-PERS '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC '7': B-MISC '8': I-MISC splits: - name: train num_bytes: 3941041 num_examples: 8477 download_size: 26382457 dataset_size: 3941041 --- # Dataset Card for NCHLT ## 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:** [link](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II) - **Repository:** []() - **Paper:** []() - **Leaderboard:** []() - **Point of Contact:** []() ### Dataset Summary The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure [More Information Needed] ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 Martin.Puttkammer@nwu.ac.za ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{eiselen2014developing, title={Developing Text Resources for Ten South African Languages.}, author={Eiselen, Roald and Puttkammer, Martin J}, booktitle={LREC}, pages={3698--3703}, year={2014} } ``` ### Contributions Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset.
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medalpaca/medical_meadow_medqa
2023-04-06T16:59:02.000Z
[ "task_categories:question-answering", "language:en", "language:zh", "medical", "region:us" ]
medalpaca
null
null
29
488
2023-04-06T16:56:15
--- task_categories: - question-answering language: - en - zh tags: - medical --- # Dataset Card for MedQA ## Dataset Description - **Paper:** ### Dataset Summary This is the data and baseline source code for the paper: Jin, Di, et al. "What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams." From https://github.com/jind11/MedQA: >The data that contains both the QAs and textbooks can be downloaded from [this google drive folder](https://drive.google.com/file/d/1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw/view?usp=sharing). A bit of details of data are explained as below: > > For QAs, we have three sources: US, Mainland of China, and Taiwan District, which are put in folders, respectively. All files for QAs are in jsonl file format, where each line is a data sample as a dict. The "XX_qbank.jsonl" files contain all data samples while we also provide an official random split into train, dev, and test sets. Those files in the "metamap" folders are extracted medical related phrases using the Metamap tool. > > For QAs, we also include the "4_options" version in for US and Mainland of China since we reported results for 4 options in the paper. > > For textbooks, we have two languages: English and simplified Chinese. For simplified Chinese, we provide two kinds of sentence spliting: one is split by sentences, and the other is split by paragraphs. ### Citation Information ``` @article{jin2020disease, title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams}, author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter}, journal={arXiv preprint arXiv:2009.13081}, year={2020} } ```
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allocine
2023-01-25T14:26:09.000Z
[ "task_categories:text-classification", "task_ids:sentiment-classification", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:fr", "license:mit", "region:us" ]
null
Allocine Dataset: A Large-Scale French Movie Reviews Dataset. This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr. It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k).
@misc{blard2019allocine, author = {Blard, Theophile}, title = {french-sentiment-analysis-with-bert}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}}, }
6
487
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - fr license: - mit multilinguality: - monolingual size_categories: - 100K<n<1M source_datasets: - original task_categories: - text-classification task_ids: - sentiment-classification paperswithcode_id: allocine pretty_name: Allociné dataset_info: features: - name: review dtype: string - name: label dtype: class_label: names: '0': neg '1': pos config_name: allocine splits: - name: train num_bytes: 91330696 num_examples: 160000 - name: validation num_bytes: 11546250 num_examples: 20000 - name: test num_bytes: 11547697 num_examples: 20000 download_size: 66625305 dataset_size: 114424643 train-eval-index: - config: allocine task: text-classification task_id: multi_class_classification splits: train_split: train eval_split: test col_mapping: review: text label: target metrics: - type: accuracy name: Accuracy - type: f1 name: F1 macro args: average: macro - type: f1 name: F1 micro args: average: micro - type: f1 name: F1 weighted args: average: weighted - type: precision name: Precision macro args: average: macro - type: precision name: Precision micro args: average: micro - type: precision name: Precision weighted args: average: weighted - type: recall name: Recall macro args: average: macro - type: recall name: Recall micro args: average: micro - type: recall name: Recall weighted args: average: weighted --- # Dataset Card for Allociné ## 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:** - **Repository:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset) - **Paper:** - **Leaderboard:** - **Point of Contact:** [Théophile Blard](mailto:theophile.blard@gmail.com) ### Dataset Summary The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k). ### Supported Tasks and Leaderboards - `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set. ### Languages The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr. ## Dataset Structure ### Data Instances Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples. An example from the Allociné train set looks like the following: ``` {'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.', 'label': 1} ``` ### Data Fields - 'review': a string containing the review text - 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively ### Data Splits The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews. | Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews | | ------------- | ---------------------------- | ------------------------ | ------------------------ | | Train | 160,000 | 49.6% | 50.4% | | Validation | 20,000 | 51.0% | 49.0% | | Test | 20,000 | 52.0% | 48.0% | ## Dataset Creation ### Curation Rationale The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task. ### Source Data #### Initial Data Collection and Normalization The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film. The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset. #### Who are the source language producers? The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website. ### Annotations The dataset does not contain any additional annotations. #### Annotation process [N/A] #### Who are the annotators? [N/A] ### Personal and Sensitive Information Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot. ## Considerations for Using the Data ### Social Impact of Dataset Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity. It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case. ### Discussion of Biases The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content. ### Other Known Limitations The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics. ## Additional Information ### Dataset Curators The Allociné dataset was collected by Théophile Blard. ### Licensing Information The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT). ### Citation Information > Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert> ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset.
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rcds/wikipedia-for-mask-filling
2023-03-08T12:22:02.000Z
[ "task_categories:fill-mask", "annotations_creators:other", "language_creators:found", "multilinguality:multilingual", "size_categories:10M<n<100M", "source_datasets:original", "language:en", "license:cc-by-4.0", "region:us" ]
rcds
\
null
0
487
2023-01-23T15:14:48
--- annotations_creators: - other language_creators: - found language: - en license: - cc-by-4.0 multilinguality: - multilingual paperswithcode_id: null pretty_name: "wikipedia pages chunked for fill-mask" size_categories: - 10M<n<100M source_datasets: - original task_categories: - fill-mask --- # preprocessed version of rcds/wikipedia-persons-masked ## 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 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:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text is masked with a <mask> token. The ground truth for every mask is provided. Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk. for each chunk the expected name for each mask is given. ### Supported Tasks and Leaderboards The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering, e.g. "Who is <mask>?" ### Languages *english only* ## Dataset Structure In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens. Use the dataset like this: ```python from datasets import load_dataset dataset = load_dataset('rcds/wikipedia-persons-masked', split='train', type='original', size='512') ``` ### Data Fields Columns are: - texts: the text chunks - masks: the names for each of the masks in the chunks ### Data Splits There are no splits, only a default train. ## Dataset Creation Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version, and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes, with the splits ensuring no words are split in half. Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens in the last chunk. ### 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 ``` TODO add citation ``` ### Contributions Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
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symanto/autextification2023
2023-10-06T13:08:55.000Z
[ "task_categories:text-classification", "size_categories:10K<n<100K", "source_datasets:multi_eurlex", "source_datasets:xsum", "source_datasets:csebuetnlp/xlsum", "source_datasets:mlsum", "source_datasets:amazon_polarity", "source_datasets:https://sinai.ujaen.es/investigacion/recursos/coah", "source_datasets:https://sinai.ujaen.es/investigacion/recursos/coar", "source_datasets:carblacac/twitter-sentiment-analysis", "source_datasets:cardiffnlp/tweet_sentiment_multilingual", "source_datasets:https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa", "source_datasets:wiki_lingua", "language:en", "language:es", "license:cc-by-nc-sa-4.0", "arxiv:2309.11285", "region:us" ]
symanto
null
null
0
487
2023-10-06T12:12:51
--- license: cc-by-nc-sa-4.0 task_categories: - text-classification language: - en - es pretty_name: AuTexTification 2023 size_categories: - 10K<n<100K source_datasets: - multi_eurlex - xsum - csebuetnlp/xlsum - mlsum - amazon_polarity - https://sinai.ujaen.es/investigacion/recursos/coah - https://sinai.ujaen.es/investigacion/recursos/coar - carblacac/twitter-sentiment-analysis - cardiffnlp/tweet_sentiment_multilingual - https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa - wiki_lingua --- # Dataset Card for AuTexTification 2023 ## Dataset Description - **Homepage:** https://sites.google.com/view/autextification - **Repository:** https://github.com/autextification/AuTexTification-Overview - **Paper:** https://arxiv.org/abs/2309.11285 ### Dataset Summary AuTexTification 2023 @IberLEF2023 is a shared task focusing in Machine-Generated Text Detection and Model Attribution in English and Spanish. The dataset includes human and generated text in 5 domains: tweets, reviews, how-to articles, news, and legal documents. The generations are obtained using six language models: BLOOM-1B1, BLOOM-3B, BLOOM-7B1, Babbage, Curie, and text-davinci-003. For more information, please refer to our overview paper: https://arxiv.org/abs/2309.11285 ### Supported Tasks and Leaderboards - Machine-Generated Text Detection - Model Attribution ### Languages English and Spanish ## Dataset Structure ### Data Instances 163k instances of labeled text in total. ### Data Fields For MGT Detection: - id - prompt - text - label - model - domain For Model Attribution: - id - prompt - text - label - domain ### Data Splits - MGT Detection Data: | Language | Split | Human | Generated | Total | | -------- | ----- | ------ | --------- | ------ | | English | Train | 17.046 | 16.799 | 33.845 | | | Test | 10.642 | 11.190 | 21.832 | | | Total | 27.688 | 27.989 | | | Spanish | Train | 15.787 | 16.275 | 32.062 | | | Test | 11.209 | 8.920 | 20.129 | | | Total | 26.996 | 25.195 | | - Model Attribution Data: | | | BLOOM | | | GPT | | | | | -------- | ----- | ----- | ----- | ----- | ------- | ----- | ---------------- | ------ | | Language | Split | 1B7 | 3B | 7B | babbage | curie | text-davinci-003 | Total | | English | Train | 3.562 | 3.648 | 3.687 | 3.870 | 3.822 | 3.827 | 14.767 | | | Test | 887 | 875 | 952 | 924 | 979 | 988 | 3.638 | | | Total | 4.449 | 4.523 | 4.639 | 4.794 | 4.801 | 4.815 | | | Spanish | Train | 3.422 | 3.514 | 3.575 | 3.788 | 3.770 | 3.866 | 14.299 | | | Test | 870 | 867 | 878 | 946 | 1.004 | 917 | 3.561 | | | Total | 4.292 | 4.381 | 4.453 | 4.734 | 4.774 | 4.783 | | ## Dataset Creation ### Curation Rationale Human data was gathered and used to prompt language models, obtaining generated data. Specific decisions were made to ensure the data gathering process was carried out in an unbiased manner, making the final human and generated texts probable continuations of a given prefix. For more detailed information, please refer to the overview paper: https://arxiv.org/abs/2309.11285 ### Source Data The following datasets were used as human text: - multi_eurlex - xsum - csebuetnlp/xlsum - mlsum - amazon_polarity - https://sinai.ujaen.es/investigacion/recursos/coah - https://sinai.ujaen.es/investigacion/recursos/coar - carblacac/twitter-sentiment-analysis - cardiffnlp/tweet_sentiment_multilingual - https://www.kaggle.com/datasets/ricardomoya/tweets-poltica-espaa - wiki_lingua These datasets were only used as sources of human text. The labels of the datasets were not employed in any manner. ### Licensing Information CC-BY-NC-SA-4.0 ### Citation Information ``` @inproceedings{autextification2023, title = "Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains", author = "Sarvazyan, Areg Mikael and Gonz{\'a}lez, Jos{\'e} {\'A}ngel and Franco-Salvador, Marc and Rangel, Francisco and Chulvi, Berta and Rosso, Paolo", month = sep, year = "2023", address = "Jaén, Spain", booktitle = "Procesamiento del Lenguaje Natural", } ```
4,390
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assin
2023-01-25T14:26:50.000Z
[ "task_categories:text-classification", "task_ids:text-scoring", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:pt", "license:unknown", "region:us" ]
null
The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the same event (one news article from Google News Portugal and another from Google News Brazil) from Google News. Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP) on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively. Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences, taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates), and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates). From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently, in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment” and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”. Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5, from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases, or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese and half in European Portuguese. Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing.
@inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} }
8
486
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - text-scoring - natural-language-inference - semantic-similarity-scoring paperswithcode_id: assin pretty_name: ASSIN dataset_info: - config_name: full features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 986507 num_examples: 5000 - name: test num_bytes: 767312 num_examples: 4000 - name: validation num_bytes: 196829 num_examples: 1000 download_size: 749735 dataset_size: 1950648 - config_name: ptpt features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 523002 num_examples: 2500 - name: test num_bytes: 392888 num_examples: 2000 - name: validation num_bytes: 105626 num_examples: 500 download_size: 749735 dataset_size: 1021516 - config_name: ptbr features: - name: sentence_pair_id dtype: int64 - name: premise dtype: string - name: hypothesis dtype: string - name: relatedness_score dtype: float32 - name: entailment_judgment dtype: class_label: names: '0': NONE '1': ENTAILMENT '2': PARAPHRASE splits: - name: train num_bytes: 463513 num_examples: 2500 - name: test num_bytes: 374432 num_examples: 2000 - name: validation num_bytes: 91211 num_examples: 500 download_size: 749735 dataset_size: 929156 --- # Dataset Card for ASSIN ## 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:** [ASSIN homepage](http://nilc.icmc.usp.br/assin/) - **Repository:** [ASSIN repository](http://nilc.icmc.usp.br/assin/) - **Paper:** [ASSIN: Evaluation of Semantic Similarity and Textual Inference](http://propor2016.di.fc.ul.pt/wp-content/uploads/2015/10/assin-overview.pdf) - **Point of Contact:** [Erick Rocha Fonseca](mailto:erickrf@icmc.usp.br) ### Dataset Summary The ASSIN (Avaliação de Similaridade Semântica e INferência textual) corpus is a corpus annotated with pairs of sentences written in Portuguese that is suitable for the exploration of textual entailment and paraphrasing classifiers. The corpus contains pairs of sentences extracted from news articles written in European Portuguese (EP) and Brazilian Portuguese (BP), obtained from Google News Portugal and Brazil, respectively. To create the corpus, the authors started by collecting a set of news articles describing the same event (one news article from Google News Portugal and another from Google News Brazil) from Google News. Then, they employed Latent Dirichlet Allocation (LDA) models to retrieve pairs of similar sentences between sets of news articles that were grouped together around the same topic. For that, two LDA models were trained (for EP and for BP) on external and large-scale collections of unannotated news articles from Portuguese and Brazilian news providers, respectively. Then, the authors defined a lower and upper threshold for the sentence similarity score of the retrieved pairs of sentences, taking into account that high similarity scores correspond to sentences that contain almost the same content (paraphrase candidates), and low similarity scores correspond to sentences that are very different in content from each other (no-relation candidates). From the collection of pairs of sentences obtained at this stage, the authors performed some manual grammatical corrections and discarded some of the pairs wrongly retrieved. Furthermore, from a preliminary analysis made to the retrieved sentence pairs the authors noticed that the number of contradictions retrieved during the previous stage was very low. Additionally, they also noticed that event though paraphrases are not very frequent, they occur with some frequency in news articles. Consequently, in contrast with the majority of the currently available corpora for other languages, which consider as labels “neutral”, “entailment” and “contradiction” for the task of RTE, the authors of the ASSIN corpus decided to use as labels “none”, “entailment” and “paraphrase”. Finally, the manual annotation of pairs of sentences was performed by human annotators. At least four annotators were randomly selected to annotate each pair of sentences, which is done in two steps: (i) assigning a semantic similarity label (a score between 1 and 5, from unrelated to very similar); and (ii) providing an entailment label (one sentence entails the other, sentences are paraphrases, or no relation). Sentence pairs where at least three annotators do not agree on the entailment label were considered controversial and thus discarded from the gold standard annotations. The full dataset has 10,000 sentence pairs, half of which in Brazilian Portuguese (ptbr) and half in European Portuguese (ptpt). Either language variant has 2,500 pairs for training, 500 for validation and 2,000 for testing. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language supported is Portuguese. ## Dataset Structure ### Data Instances An example from the ASSIN dataset looks as follows: ``` { "entailment_judgment": 0, "hypothesis": "André Gomes entra em campo quatro meses depois de uma lesão na perna esquerda o ter afastado dos relvados.", "premise": "Relembre-se que o atleta estava afastado dos relvados desde maio, altura em que contraiu uma lesão na perna esquerda.", "relatedness_score": 3.5, "sentence_pair_id": 1 } ``` ### Data Fields - `sentence_pair_id`: a `int64` feature. - `premise`: a `string` feature. - `hypothesis`: a `string` feature. - `relatedness_score`: a `float32` feature. - `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`, `PARAPHRASE`. ### Data Splits The data is split into train, validation and test set. The split sizes are as follow: | | Train | Val | Test | | ----- | ------ | ----- | ---- | | full | 5000 | 1000 | 4000 | | ptbr | 2500 | 500 | 2000 | | ptpt | 2500 | 500 | 2000 | ## 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 ``` @inproceedings{fonseca2016assin, title={ASSIN: Avaliacao de similaridade semantica e inferencia textual}, author={Fonseca, E and Santos, L and Criscuolo, Marcelo and Aluisio, S}, booktitle={Computational Processing of the Portuguese Language-12th International Conference, Tomar, Portugal}, pages={13--15}, year={2016} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
9,005
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crystina-z/mbert-mrtydi
2022-02-01T22:10:30.000Z
[ "region:us" ]
crystina-z
null
null
0
486
2022-03-02T23:29:22
Entry not found
15
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ashraq/fashion-product-images-small
2022-11-01T20:25:52.000Z
[ "region:us" ]
ashraq
null
null
10
486
2022-11-01T20:22:50
--- dataset_info: features: - name: id dtype: int64 - name: gender dtype: string - name: masterCategory dtype: string - name: subCategory dtype: string - name: articleType dtype: string - name: baseColour dtype: string - name: season dtype: string - name: year dtype: float64 - name: usage dtype: string - name: productDisplayName dtype: string - name: image dtype: image splits: - name: train num_bytes: 546202015.44 num_examples: 44072 download_size: 271496441 dataset_size: 546202015.44 --- # Dataset Card for "fashion-product-images-small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) Data was obtained from [here](https://www.kaggle.com/datasets/paramaggarwal/fashion-product-images-small)
867
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reciprocate/vicuna-fair-eval
2023-06-15T14:47:39.000Z
[ "region:us" ]
reciprocate
null
null
0
486
2023-06-15T14:47:33
--- dataset_info: features: - name: prompt dtype: string - name: selected dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 180638 num_examples: 66 download_size: 116978 dataset_size: 180638 --- # Dataset Card for "vicuna_fair_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
429
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kyujinpy/KoCoT_2000
2023-10-10T13:19:00.000Z
[ "task_categories:text-generation", "task_categories:text-classification", "size_categories:1k<n<5k", "language:en", "license:cc-by-4.0", "arxiv:2305.14045", "region:us" ]
kyujinpy
null
null
9
486
2023-09-22T16:41:36
--- license: cc-by-4.0 task_categories: - text-generation - text-classification language: - en size_categories: - 1k<n<5k --- # KoCoT-Collection Using DeepL dataset, translation about [kaist-CoT](https://huggingface.co/datasets/kaist-ai/CoT-Collection). --- # Original Dataset Card for Dataset Name ## Dataset Description - **Homepage:https://github.com/kaistAI/CoT-Collection** - **Repository:https://github.com/kaistAI/CoT-Collection** - **Paper:https://arxiv.org/abs/2305.14045** - **Point of Contact:sejune@lklab.io** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits | name | train | |-------------------|------:| |CoT-Collection|1837928| ## Additional Information ### Citation Information ``` @article{kim2023cot, title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning}, author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon}, journal={arXiv preprint arXiv:2305.14045}, year={2023} } ```
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kyujinpy/KOR-OpenOrca-Platypus
2023-10-24T06:54:44.000Z
[ "task_categories:conversational", "task_categories:text-classification", "task_categories:token-classification", "task_categories:table-question-answering", "task_categories:question-answering", "task_categories:zero-shot-classification", "task_categories:summarization", "task_categories:feature-extraction", "task_categories:text-generation", "task_categories:text2text-generation", "size_categories:10K<n<50K", "language:ko", "license:cc-by-nc-4.0", "arxiv:2306.02707", "arxiv:2301.13688", "region:us" ]
kyujinpy
null
null
3
485
2023-10-09T14:23:30
--- language: - ko license: cc-by-nc-4.0 size_categories: - 10K<n<50K task_categories: - conversational - text-classification - token-classification - table-question-answering - question-answering - zero-shot-classification - summarization - feature-extraction - text-generation - text2text-generation pretty_name: OpenOrca configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: id dtype: string - name: input dtype: string - name: output dtype: string - name: instruction dtype: string splits: - name: train num_bytes: 78588418 num_examples: 46558 download_size: 39656100 dataset_size: 78588418 --- # KOR-OpenOrca-Platypus - OpenOrca-Ko + KOpen-platypus - 데이터셋 이용하셔서 모델이나 데이터셋을 만드실 때, 간단한 출처 표기를 해주신다면 연구에 큰 도움이 됩니다😭😭 ## KOpen-platpyus Repo: [KOpen-platypus](https://huggingface.co/datasets/kyujinpy/KOpen-platypus) - 고품질 한국어 데이터셋 1. 코드와 주석은 그대로 유지하고, 설명 부분만 한국어로 수정 2. 1번과 더불어서, Python, Java, Cpp, xml 등등 결과들은 전부 기존의 데이터 형태로 최대한 보존 3. 단일 숫자와 영어는 본래의 결과 그대로 가져옴 4. DeepL Pro 번역 결과 중 미완성 변역 결과 직접 수정(예를 들면, '[...]'가 포함되어 있음) 5. DeepL Pro 번역 결과가 본래의 데이터에 비해 글자수가 50% 이하로 낮으면, 번역 결과 수정 6. 번역하고자 하는 글자수가 1500자 이상일 경우, API로 변경해서 번역 7. 고유명사는 최대한 유지함 > Post-processing 작업 내용 ## OpenOrca-Ko Repo: [OpenOrca-Ko](https://huggingface.co/datasets/kyujinpy/OpenOrca-KO) 1. NIV // 1571개 2. FLAN // 9434개 3. T0 // 6351개 4. CoT // 2117개 5. KoCoT // 2159개 > Dataset 구성 ## Translation Using DeepL Pro API. Thanks. --- >Below is original dataset card ## Table of Contents - [Dataset Summary](#dataset-summary) - [Dataset Attribution](#dataset-attribution) - [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) - [Dataset Use](#dataset-use) - [Use Cases](#use-cases) - [Usage Caveats](#usage-caveats) - [Getting Started](#getting-started) <p><h1>🐋 The OpenOrca Dataset! 🐋</h1></p> ![OpenOrca Logo](https://huggingface.co/datasets/Open-Orca/OpenOrca/resolve/main/OpenOrcaLogo.png "OpenOrca Logo") <a name="dataset-announcement"></a> We are thrilled to announce the release of the OpenOrca dataset! This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the [Orca paper](https://arxiv.org/abs/2306.02707). It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers! # Official Models ## OpenOrca-Platypus2-13B Our [latest release](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B), the first 13B model to score higher than LLaMA1-65B on the HuggingFace Leaderboard! Released in partnership with Platypus. ## LlongOrca 7B & 13B * Our [first 7B release](https://huggingface.co/Open-Orca/LlongOrca-7B-16k), trained on top of LLongMA2 to achieve 16,000 tokens context. #1 long context 7B model at release time, with >99% of the overall #1 model's performance. * [LlongOrca-13B-16k](https://huggingface.co/Open-Orca/LlongOrca-13B-16k), trained on top of LLongMA2. #1 long context 13B model at release time, with >97% of the overall #1 model's performance. ## OpenOrcaxOpenChat-Preview2-13B Our [second model](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B), highlighting that we've surpassed the performance reported in the Orca paper. Was #1 at release time, now surpassed by our own OpenOrca-Platypus2-13B. Released in partnership with OpenChat. ## OpenOrca-Preview1-13B [OpenOrca-Preview1-13B](https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B) This model was trained in less than a day, for <$200, with <10% of our data. At release, it beat the current state of the art models on BigBench-Hard and AGIEval. Achieves ~60% of the improvements reported in the Orca paper. <a name="dataset-summary"></a> # Dataset Summary The OpenOrca dataset is a collection of augmented [FLAN Collection data](https://arxiv.org/abs/2301.13688). Currently ~1M GPT-4 completions, and ~3.2M GPT-3.5 completions. It is tabularized in alignment with the distributions presented in the ORCA paper and currently represents a partial completion of the full intended dataset, with ongoing generation to expand its scope. The data is primarily used for training and evaluation in the field of natural language processing. <a name="dataset-attribution"></a> # Dataset Attribution We would like to give special recognition to the following contributors for their significant efforts and dedication: Teknium WingLian/Caseus Eric Hartford NanoBit Pankaj Winddude Rohan http://AlignmentLab.ai: Autometa Entropi AtlasUnified NeverendingToast NanoBit WingLian/Caseus Also of course, as always, TheBloke, for being the backbone of the whole community. Many thanks to NanoBit and Caseus, makers of [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), for lending us their expertise on the platform that developed and trained manticore, minotaur, and many others! We are welcoming sponsors or collaborators to help us build these models to the scale they deserve. Please reach out via our socials: http://Alignmentlab.ai https://discord.gg/n9hXaBPWxx Want to visualize our full dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [<img src="https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B/resolve/main/OpenOrca%20Nomic%20Atlas.png" alt="Atlas Nomic Dataset Map" width="400" height="400" />](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) <a name="supported-tasks-and-leaderboards"></a> # Supported Tasks and Leaderboards This dataset supports a range of tasks including language modeling, text generation, and text augmentation. It has been instrumental in the generation of multiple high-performing model checkpoints which have exhibited exceptional performance in our unit testing. Further information on leaderboards will be updated as they become available. <a name="languages"></a> # Languages The language of the data is primarily English. <a name="dataset-structure"></a> # Dataset Structure <a name="data-instances"></a> ## Data Instances A data instance in this dataset represents entries from the FLAN collection which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response is then entered into the response field. <a name="data-fields"></a> ## Data Fields The fields are: 1) 'id', a unique numbered identifier which includes one of 'niv', 't0', 'cot', or 'flan' to represent which source FLAN Collection submix the 'question' is sourced from. 2) 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint 3) 'question', representing a question entry as provided by the FLAN Collection 4) 'response', a response to that question received from a query to either GPT-3.5 or GPT-4. <a name="data-splits"></a> ## Data Splits The data is unsplit. <a name="dataset-creation"></a> # Dataset Creation <a name="curation-rationale"></a> ## Curation Rationale The dataset was created to provide a source of augmented text data for researchers and developers. The datapoints are intended primarily to provide an enhancement of the core FLAN Collection data which relies upon the detailed step by step reasoning capabilities of GPT-3.5 and GPT-4. This "reasoning trace" augmentation has demonstrated exceptional results, allowing a LLaMA-13B model trained with this data to rival or beat GPT-3.5 on broad sets of hard reasoning tasks which all models below 100B parameters had previously performed dramatically worse on. <a name="source-data"></a> ## Source Data The data is generated using techniques in alignment with the distributions outlined in the Orca paper, except as noted below: 1) There is not enough CoT data in the FLAN Collection to generate 150K zero-shot entries, as the paper purports to use. We suspect this portion was either undocumented or misrepresented. We have used the ~75K points available. 2) We used the pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g. [conceptofmind/flan2021](https://huggingface.co/datasets/conceptofmind/flan2021_submix_original). These are referenced by the [official FLAN Collection repo](https://github.com/google-research/FLAN/tree/main/flan/v2) as the preferred data source. However, these are a subset of the full FLAN Collection data, and have less than the required entries for the flan2021 and t0 submixes, by ~1.25M and 200k respectively. Combined, this gave us ~1.5M fewer datapoints than in the original Orca paper. Completing the set is an ongoing work. <a name="dataset-use"></a> # Dataset Use <a name="use-cases"></a> ## Use Cases The dataset can be used for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. <a name="usage-caveats"></a> ## Usage Caveats Given that this is a work-in-progress dataset, it is recommended to regularly check for updates and improvements. Further, the data should be used in accordance with the guidelines and recommendations outlined in the Orca paper. <a name="getting-started"></a> ## Getting Started This dataset is organized such that it can be naively loaded via Hugging Face datasets library. We recommend using streaming due to the large size of the files. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face. # Citation ```bibtex @misc{OpenOrca, title = {OpenOrca: An Open Dataset of GPT Augmented FLAN Reasoning Traces}, author = {Wing Lian and Bleys Goodson and Eugene Pentland and Austin Cook and Chanvichet Vong and "Teknium"}, year = {2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca}, } ``` ```bibtex @misc{mukherjee2023orca, title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, year={2023}, eprint={2306.02707}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ```bibtex @misc{longpre2023flan, title={The Flan Collection: Designing Data and Methods for Effective Instruction Tuning}, author={Shayne Longpre and Le Hou and Tu Vu and Albert Webson and Hyung Won Chung and Yi Tay and Denny Zhou and Quoc V. Le and Barret Zoph and Jason Wei and Adam Roberts}, year={2023}, eprint={2301.13688}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom}, year={2023}, eprint= arXiv 2307.09288 } @software{touvron2023llama, title={LLaMA: Open and Efficient Foundation Language Models}, author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, journal={arXiv preprint arXiv:2302.13971}, year={2023} } ```
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swahili_news
2023-01-25T14:45:11.000Z
[ "task_categories:text-classification", "task_ids:multi-class-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:sw", "license:cc-by-4.0", "region:us" ]
null
Swahili is spoken by 100-150 million people across East Africa. In Tanzania, it is one of two national languages (the other is English) and it is the official language of instruction in all schools. News in Swahili is an important part of the media sphere in Tanzania. News contributes to education, technology, and the economic growth of a country, and news in local languages plays an important cultural role in many Africa countries. In the modern age, African languages in news and other spheres are at risk of being lost as English becomes the dominant language in online spaces. The Swahili news dataset was created to reduce the gap of using the Swahili language to create NLP technologies and help AI practitioners in Tanzania and across Africa continent to practice their NLP skills to solve different problems in organizations or societies related to Swahili language. Swahili News were collected from different websites that provide news in the Swahili language. I was able to find some websites that provide news in Swahili only and others in different languages including Swahili. The dataset was created for a specific task of text classification, this means each news content can be categorized into six different topics (Local news, International news , Finance news, Health news, Sports news, and Entertainment news). The dataset comes with a specified train/test split. The train set contains 75% of the dataset and test set contains 25% of the dataset.
@dataset{davis_david_2020_5514203, author = {Davis David}, title = {Swahili : News Classification Dataset}, month = dec, year = 2020, note = {{The news version contains both train and test sets.}}, publisher = {Zenodo}, version = {0.2}, doi = {10.5281/zenodo.5514203}, url = {https://doi.org/10.5281/zenodo.5514203} }
2
484
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - sw license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: - multi-class-classification pretty_name: 'Swahili : News Classification Dataset' dataset_info: features: - name: text dtype: string - name: label dtype: class_label: names: '0': uchumi '1': kitaifa '2': michezo '3': kimataifa '4': burudani '5': afya config_name: swahili_news splits: - name: train num_bytes: 49517855 num_examples: 22207 - name: test num_bytes: 16093496 num_examples: 7338 download_size: 65618408 dataset_size: 65611351 --- # Dataset Card for Swahili : News Classification Dataset ## 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:** [Homepage for Swahili News classification dataset](https://doi.org/10.5281/zenodo.4300293) - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Swahili is spoken by 100-150 million people across East Africa. In Tanzania, it is one of two national languages (the other is English) and it is the official language of instruction in all schools. News in Swahili is an important part of the media sphere in Tanzania. News contributes to education, technology, and the economic growth of a country, and news in local languages plays an important cultural role in many Africa countries. In the modern age, African languages in news and other spheres are at risk of being lost as English becomes the dominant language in online spaces. The Swahili news dataset was created to reduce the gap of using the Swahili language to create NLP technologies and help AI practitioners in Tanzania and across Africa continent to practice their NLP skills to solve different problems in organizations or societies related to Swahili language. Swahili News were collected from different websites that provide news in the Swahili language. I was able to find some websites that provide news in Swahili only and others in different languages including Swahili. The dataset was created for a specific task of text classification, this means each news content can be categorized into six different topics (Local news, International news , Finance news, Health news, Sports news, and Entertainment news). The dataset comes with a specified train/test split. The train set contains 75% of the dataset and test set contains 25% of the dataset. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The language used is Swahili ## Dataset Structure ### Data Instances A data instance: ``` { 'text': ' Bodi ya Utalii Tanzania (TTB) imesema, itafanya misafara ya kutangaza utalii kwenye miji minne nchini China kati ya Juni 19 hadi Juni 26 mwaka huu.Misafara hiyo itatembelea miji ya Beijing Juni 19, Shanghai Juni 21, Nanjig Juni 24 na Changsha Juni 26.Mwenyekiti wa bodi TTB, Jaji Mstaafu Thomas Mihayo ameyasema hayo kwenye mkutano na waandishi wa habari jijini Dar es Salaam.“Tunafanya jitihada kuhakikisha tunavuna watalii wengi zaidi kutoka China hasa tukizingatia umuhimu wa soko la sekta ya utalii nchini,” amesema Jaji Mihayo.Novemba 2018 TTB ilifanya ziara kwenye miji ya Beijing, Shanghai, Chengdu, Guangzhou na Hong Kong kutangaza vivutio vya utalii sanjari kuzitangaza safari za ndege za Air Tanzania.Ziara hiyo inaelezwa kuzaa matunda ikiwa ni pamoja na watalii zaidi ya 300 kuja nchini Mei mwaka huu kutembelea vivutio vya utalii.', 'label': 0 } ``` ### Data Fields - `text`: the news articles - `label`: the label of the news article ### Data Splits Dataset contains train and test splits. ## 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 Creative Commons Attribution 4.0 International ### Citation Information ``` @dataset{davis_david_2020_5514203, author = {Davis David}, title = {Swahili : News Classification Dataset}, month = dec, year = 2020, note = {{The news version contains both train and test sets.}}, publisher = {Zenodo}, version = {0.2}, doi = {10.5281/zenodo.5514203}, url = {https://doi.org/10.5281/zenodo.5514203} } ``` ### Contributions Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
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CALM/arwiki
2022-08-01T16:37:23.000Z
[ "multilinguality:monolingual", "language:ar", "license:unknown", "region:us" ]
CALM
null
null
1
484
2022-03-02T23:29:22
--- pretty_name: Wikipedia Arabic dumps dataset. language: - ar license: - unknown multilinguality: - monolingual --- # Arabic Wiki Dataset ## Dataset Summary This dataset is extracted using [`wikiextractor`](https://github.com/attardi/wikiextractor) tool, from [Wikipedia Arabic pages](https://dumps.wikimedia.org/arwiki/). ## Supported Tasks and Leaderboards Intended to train **Arabic** language models on MSA (Modern Standard Arabic). ## Dataset Structure The dataset is structured into 2 folders: - `arwiki_20211213_txt`: dataset is divided into subfolders each of which contains no more than 100 documents. - `arwiki_20211213_txt_single`: all documents merged together in a single txt file. ## Dataset Statistics #### Extracts from **December 13, 2021**: | documents | vocabulary | words | | --- | --- | --- | | 1,136,455 | 5,446,560 | 175,566,016 | ## Usage Load all dataset from the single txt file: ```python load_dataset('CALM/arwiki', data_files='arwiki_2021_txt_single/arwiki_20211213.txt') # OR with stream load_dataset('CALM/arwiki', data_files='arwiki_2021_txt_single/arwiki_20211213.txt', streaming=True) ``` Load a smaller subset from the individual txt files: ```python load_dataset('CALM/arwiki', data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt') # OR with stream load_dataset('CALM/arwiki', data_files='arwiki_2021_txt/AA/arwiki_20211213_1208.txt', streaming=True) ```
1,498
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nlpaueb/finer-139
2022-10-23T05:05:03.000Z
[ "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:1M<n<10M", "language:en", "license:cc-by-sa-4.0", "arxiv:2203.06482", "region:us" ]
nlpaueb
FiNER-139 is a named entity recognition dataset consisting of 10K annual and quarterly English reports (filings) of publicly traded companies downloaded from the U.S. Securities and Exchange Commission (SEC) annotated with 139 XBRL tags in the IOB2 format.
@inproceedings{loukas-etal-2022-finer, title = "{FiNER: Financial Numeric Entity Recognition for XBRL Tagging}", author = "Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George", booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics", month = "may", year = "2022", publisher = "Association for Computational Linguistics", }
12
484
2022-03-04T10:00:23
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual pretty_name: FiNER-139 size_categories: - 1M<n<10M source_datasets: [] task_categories: - structure-prediction - named-entity-recognition - entity-extraction task_ids: - named-entity-recognition --- # Dataset Card for FiNER-139 ## 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-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [SEC-BERT](#sec-bert) - [About Us](#about-us) ## Dataset Description - **Homepage:** [FiNER](https://github.com/nlpaueb/finer) - **Repository:** [FiNER](https://github.com/nlpaueb/finer) - **Paper:** [FiNER, Loukas et al. (2022)](https://arxiv.org/abs/2203.06482) - **Point of Contact:** [Manos Fergadiotis](mailto:fergadiotis@aueb.gr) ### Dataset Summary <div style="text-align: justify"> <strong>FiNER-139</strong> is comprised of 1.1M sentences annotated with <strong>eXtensive Business Reporting Language (XBRL)</strong> tags extracted from annual and quarterly reports of publicly-traded companies in the US. Unlike other entity extraction tasks, like named entity recognition (NER) or contract element extraction, which typically require identifying entities of a small set of common types (e.g., persons, organizations), FiNER-139 uses a much larger label set of <strong>139 entity types</strong>. Another important difference from typical entity extraction is that FiNER focuses on numeric tokens, with the correct tag depending mostly on context, not the token itself. </div> ### Supported Tasks <div style="text-align: justify"> To promote transparency among shareholders and potential investors, publicly traded companies are required to file periodic financial reports annotated with tags from the eXtensive Business Reporting Language (XBRL), an XML-based language, to facilitate the processing of financial information. However, manually tagging reports with XBRL tags is tedious and resource-intensive. We, therefore, introduce <strong>XBRL tagging</strong> as a <strong>new entity extraction task</strong> for the <strong>financial domain</strong> and study how financial reports can be automatically enriched with XBRL tags. To facilitate research towards automated XBRL tagging we release FiNER-139. </div> ### Languages **FiNER-139** is compiled from approximately 10k annual and quarterly **English** reports ## Dataset Structure ### Data Instances This is a "train" split example: ```json { 'id': 40 'tokens': ['In', 'March', '2014', ',', 'the', 'Rialto', 'segment', 'issued', 'an', 'additional', '$', '100', 'million', 'of', 'the', '7.00', '%', 'Senior', 'Notes', ',', 'at', 'a', 'price', 'of', '102.25', '%', 'of', 'their', 'face', 'value', 'in', 'a', 'private', 'placement', '.'] 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 37, 0, 0, 0, 41, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] } ``` ### Data Fields **id**: ID of the example <br> **tokens**: List of tokens for the specific example. <br> **ner_tags**: List of tags for each token in the example. Tags are provided as integer classes.<br> If you want to use the class names you can access them as follows: ```python import datasets finer_train = datasets.load_dataset("nlpaueb/finer-139", split="train") finer_tag_names = finer_train.features["ner_tags"].feature.names ``` **finer_tag_names** contains a list of class names corresponding to the integer classes e.g. ``` 0 -> "O" 1 -> "B-AccrualForEnvironmentalLossContingencies" ``` ### Data Splits | Training | Validation | Test | -------- | ---------- | ------- | 900,384 | 112,494 | 108,378 ## Dataset Creation ### Curation Rationale The dataset was curated by [Loukas et al. (2022)](https://arxiv.org/abs/2203.06482) <br> ### Source Data #### Initial Data Collection and Normalization <div style="text-align: justify"> FiNER-139 is compiled from approximately 10k annual and quarterly English reports (filings) of publicly traded companies downloaded from the [US Securities and Exchange Commission's (SEC)](https://www.sec.gov/) [Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/edgar.shtml) system. The reports span a 5-year period, from 2016 to 2020. They are annotated with XBRL tags by professional auditors and describe the performance and projections of the companies. XBRL defines approximately 6k entity types from the US-GAAP taxonomy. FiNER-139 is annotated with the 139 most frequent XBRL entity types with at least 1,000 appearances. We used regular expressions to extract the text notes from the Financial Statements Item of each filing, which is the primary source of XBRL tags in annual and quarterly reports. We used the <strong>IOB2</strong> annotation scheme to distinguish tokens at the beginning, inside, or outside of tagged expressions, which leads to 279 possible token labels. </div> ### Annotations #### Annotation process <div style="text-align: justify"> All the examples were annotated by professional auditors as required by the Securities & Exchange Commission (SEC) legislation. Even though the gold XBRL tags come from professional auditors there are still some discrepancies. Consult [Loukas et al. (2022)](https://arxiv.org/abs/2203.06482), (Section 9.4) for more details </div> #### Who are the annotators? Professional auditors ### Personal and Sensitive Information The dataset contains publicly available annual and quarterly reports (filings) ## Additional Information ### Dataset Curators [Loukas et al. (2022)](https://arxiv.org/abs/2203.06482) ### Licensing Information <div style="text-align: justify"> Access to SEC's EDGAR public database is free, allowing research of public companies' financial information and operations by reviewing the filings the companies makes with the SEC. </div> ### Citation Information If you use this dataset cite the following ``` @inproceedings{loukas-etal-2022-finer, title = {FiNER: Financial Numeric Entity Recognition for XBRL Tagging}, author = {Loukas, Lefteris and Fergadiotis, Manos and Chalkidis, Ilias and Spyropoulou, Eirini and Malakasiotis, Prodromos and Androutsopoulos, Ion and Paliouras George}, booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL 2022)}, publisher = {Association for Computational Linguistics}, location = {Dublin, Republic of Ireland}, year = {2022}, url = {https://arxiv.org/abs/2203.06482} } ``` ## SEC-BERT <img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/> <div style="text-align: justify"> We also pre-train our own BERT models (<strong>SEC-BERT</strong>) for the financial domain, intended to assist financial NLP research and FinTech applications. <br> <strong>SEC-BERT</strong> consists of the following models: * [**SEC-BERT-BASE**](https://huggingface.co/nlpaueb/sec-bert-base): Same architecture as BERT-BASE trained on financial documents. * [**SEC-BERT-NUM**](https://huggingface.co/nlpaueb/sec-bert-num): Same as SEC-BERT-BASE but we replace every number token with a [NUM] pseudo-token handling all numeric expressions in a uniform manner, disallowing their fragmentation * [**SEC-BERT-SHAPE**](https://huggingface.co/nlpaueb/sec-bert-shape): Same as SEC-BERT-BASE but we replace numbers with pseudo-tokens that represent the number’s shape, so numeric expressions (of known shapes) are no longer fragmented, e.g., '53.2' becomes '[XX.X]' and '40,200.5' becomes '[XX,XXX.X]'. These models were pre-trained on 260,773 10-K filings (annual reports) from 1993-2019, publicly available at [U.S. Securities and Exchange Commission (SEC)](https://www.sec.gov/) </div> ## About Us <div style="text-align: justify"> [**AUEB's Natural Language Processing Group**](http://nlp.cs.aueb.gr) develops algorithms, models, and systems that allow computers to process and generate natural language texts. The group's current research interests include: * question answering systems for databases, ontologies, document collections, and the Web, especially biomedical question answering, * natural language generation from databases and ontologies, especially Semantic Web ontologies, text classification, including filtering spam and abusive content, * information extraction and opinion mining, including legal text analytics and sentiment analysis, * natural language processing tools for Greek, for example parsers and named-entity recognizers, machine learning in natural language processing, especially deep learning. The group is part of the Information Processing Laboratory of the Department of Informatics of the Athens University of Economics and Business. </div> [Manos Fergadiotis](https://manosfer.github.io) on behalf of [AUEB's Natural Language Processing Group](http://nlp.cs.aueb.gr)
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squad_adversarial
2022-11-18T21:47:43.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "source_datasets:extended|squad", "language:en", "license:mit", "region:us" ]
null
Here are two different adversaries, each of which uses a different procedure to pick the sentence it adds to the paragraph: AddSent: Generates up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. Picks the one that most confuses the model. AddOneSent: Similar to AddSent, but just picks one of the candidate sentences at random. This adversary is does not query the model in any way.
@inproceedings{jia-liang-2017-adversarial, title = "Adversarial Examples for Evaluating Reading Comprehension Systems", author = "Jia, Robin and Liang, Percy", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D17-1215", doi = "10.18653/v1/D17-1215", pages = "2021--2031", abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", }
5
483
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced language_creators: - found language: - en license: - mit multilinguality: - monolingual size_categories: - 1K<n<10K source_datasets: - extended|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: null pretty_name: '''Adversarial Examples for SQuAD''' dataset_info: - config_name: squad_adversarial 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: AddSent num_bytes: 3803551 num_examples: 3560 - name: AddOneSent num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 5668318 - config_name: AddSent 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: validation num_bytes: 3803551 num_examples: 3560 download_size: 5994513 dataset_size: 3803551 - config_name: AddOneSent 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: validation num_bytes: 1864767 num_examples: 1787 download_size: 5994513 dataset_size: 1864767 --- # Dataset Card for 'Adversarial Examples for SQuAD' ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - [**Homepage**](https://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d/) - [**Repository**](https://github.com/robinjia/adversarial-squad/) - [**Paper**](https://www.aclweb.org/anthology/D17-1215/) ### Dataset Summary Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. ### Supported Tasks and Leaderboards `question-answering`, `adversarial attack` ### Languages English ## Dataset Structure Follows the standart SQuAD format. ### Data Instances An example from the data set looks as follows: ```py {'answers': {'answer_start': [334, 334, 334], 'text': ['February 7, 2016', 'February 7', 'February 7, 2016']}, 'context': 'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50. The Champ Bowl was played on August 18th,1991.', 'id': '56bea9923aeaaa14008c91bb-high-conf-turk2', 'question': 'What day was the Super Bowl played on?', 'title': 'Super_Bowl_50'} ``` `id` field is formed like: [original_squad_id]-[annotator_id] ### Data Fields ```py {'id': Value(dtype='string', id=None), # id of example (same as SQuAD) OR SQuAD-id-[annotator_id] for adversarially modified examples 'title': Value(dtype='string', id=None), # title of document the context is from (same as SQuAD) 'context': Value(dtype='string', id=None), # the context (same as SQuAD) +adversarially added sentence 'question': Value(dtype='string', id=None), # the question (same as SQuAD) 'answers': Sequence(feature={'text': Value(dtype='string', id=None), # the answer (same as SQuAD) 'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) # the answer_start index (same as SQuAD) } ``` ### Data Splits - AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way. - AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way. Number of Q&A pairs - AddSent : 3560 - AddOneSent: 1787 ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data SQuAD dev set (+with adversarial sentences added) #### 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 [MIT License](https://github.com/robinjia/adversarial-squad/blob/master/LICENSE) ### Citation Information ``` @inproceedings{jia-liang-2017-adversarial, title = "Adversarial Examples for Evaluating Reading Comprehension Systems", author = "Jia, Robin and Liang, Percy", booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing", month = sep, year = "2017", address = "Copenhagen, Denmark", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D17-1215", doi = "10.18653/v1/D17-1215", pages = "2021--2031", abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.", } ``` ### Contributions Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
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wmt17
2023-04-05T13:43:57.000Z
[ "task_categories:translation", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:translation", "size_categories:10M<n<100M", "source_datasets:extended|europarl_bilingual", "source_datasets:extended|news_commentary", "source_datasets:extended|setimes", "source_datasets:extended|un_multi", "language:cs", "language:de", "language:en", "language:fi", "language:lv", "language:ru", "language:tr", "language:zh", "license:unknown", "region:us" ]
null
null
@InProceedings{bojar-EtAl:2017:WMT1, author = {Bojar, Ond\v{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco}, title = {Findings of the 2017 Conference on Machine Translation (WMT17)}, booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {September}, year = {2017}, address = {Copenhagen, Denmark}, publisher = {Association for Computational Linguistics}, pages = {169--214}, url = {http://www.aclweb.org/anthology/W17-4717} }
1
483
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - cs - de - en - fi - lv - ru - tr - zh license: - unknown multilinguality: - translation size_categories: - 10M<n<100M source_datasets: - extended|europarl_bilingual - extended|news_commentary - extended|setimes - extended|un_multi task_categories: - translation task_ids: [] pretty_name: WMT17 paperswithcode_id: null dataset_info: - config_name: cs-en features: - name: translation dtype: translation: languages: - cs - en splits: - name: train num_bytes: 300698431 num_examples: 1018291 - name: validation num_bytes: 707870 num_examples: 2999 - name: test num_bytes: 674430 num_examples: 3005 download_size: 1784240523 dataset_size: 302080731 - config_name: de-en features: - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 1715537443 num_examples: 5906184 - name: validation num_bytes: 735516 num_examples: 2999 - name: test num_bytes: 729519 num_examples: 3004 download_size: 1945382236 dataset_size: 1717002478 - config_name: fi-en features: - name: translation dtype: translation: languages: - fi - en splits: - name: train num_bytes: 743856525 num_examples: 2656542 - name: validation num_bytes: 1410515 num_examples: 6000 - name: test num_bytes: 1388828 num_examples: 6004 download_size: 434531933 dataset_size: 746655868 - config_name: lv-en features: - name: translation dtype: translation: languages: - lv - en splits: - name: train num_bytes: 517419100 num_examples: 3567528 - name: validation num_bytes: 544604 num_examples: 2003 - name: test num_bytes: 530474 num_examples: 2001 download_size: 169634544 dataset_size: 518494178 - config_name: ru-en features: - name: translation dtype: translation: languages: - ru - en splits: - name: train num_bytes: 11000075522 num_examples: 24782720 - name: validation num_bytes: 1050677 num_examples: 2998 - name: test num_bytes: 1040195 num_examples: 3001 download_size: 3582640660 dataset_size: 11002166394 - config_name: tr-en features: - name: translation dtype: translation: languages: - tr - en splits: - name: train num_bytes: 60416617 num_examples: 205756 - name: validation num_bytes: 732436 num_examples: 3000 - name: test num_bytes: 752773 num_examples: 3007 download_size: 62263061 dataset_size: 61901826 - config_name: zh-en features: - name: translation dtype: translation: languages: - zh - en splits: - name: train num_bytes: 5529286149 num_examples: 25134743 - name: validation num_bytes: 589591 num_examples: 2002 - name: test num_bytes: 540347 num_examples: 2001 download_size: 2314906945 dataset_size: 5530416087 --- # Dataset Card for "wmt17" ## 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:** [http://www.statmt.org/wmt17/translation-task.html](http://www.statmt.org/wmt17/translation-task.html) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 GB ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p> <ul> <li>Non-English files contain many English sentences.</li> <li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li> </ul> <p>We have contacted the WMT organizers.</p> </div> Translation dataset based on the data from statmt.org. Versions exist for different years using a combination of data sources. The base `wmt` allows you to create a custom dataset by choosing your own data/language pair. This can be done as follows: ```python from datasets import inspect_dataset, load_dataset_builder inspect_dataset("wmt17", "path/to/scripts") builder = load_dataset_builder( "path/to/scripts/wmt_utils.py", language_pair=("fr", "de"), subsets={ datasets.Split.TRAIN: ["commoncrawl_frde"], datasets.Split.VALIDATION: ["euelections_dev2019"], }, ) # Standard version builder.download_and_prepare() ds = builder.as_dataset() # Streamable version ds = builder.as_streaming_dataset() ``` ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### cs-en - **Size of downloaded dataset files:** 1.78 GB - **Size of the generated dataset:** 302.09 MB - **Total amount of disk used:** 2.09 GB An example of 'train' looks as follows. ``` ``` ### Data Fields The data fields are the same among all splits. #### cs-en - `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`. ### Data Splits |name | train |validation|test| |-----|------:|---------:|---:| |cs-en|1018291| 2999|3005| ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @InProceedings{bojar-EtAl:2017:WMT1, author = {Bojar, Ond {r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco}, title = {Findings of the 2017 Conference on Machine Translation (WMT17)}, booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers}, month = {September}, year = {2017}, address = {Copenhagen, Denmark}, publisher = {Association for Computational Linguistics}, pages = {169--214}, url = {http://www.aclweb.org/anthology/W17-4717} } ``` ### Contributions Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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ai4bharat/IndicCOPA
2022-12-15T11:34:32.000Z
[ "task_categories:multiple-choice", "task_ids:multiple-choice-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:1K<n<10K", "source_datasets:extended|xcopa", "language:as", "language:bn", "language:en", "language:gom", "language:gu", "language:hi", "language:kn", "language:mai", "language:ml", "language:mr", "language:ne", "language:or", "language:pa", "language:sa", "language:sat", "language:sd", "language:ta", "language:te", "language:ur", "license:cc-by-4.0", "region:us" ]
ai4bharat
\
\
1
483
2022-09-20T08:18:35
--- annotations_creators: - expert-generated language: - as - bn - en - gom - gu - hi - kn - mai - ml - mr - ne - or - pa - sa - sat - sd - ta - te - ur language_creators: - expert-generated license: - cc-by-4.0 multilinguality: - multilingual pretty_name: IndicXCOPA size_categories: - 1K<n<10K source_datasets: - extended|xcopa tags: [] task_categories: - multiple-choice task_ids: - multiple-choice-qa --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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laion/gpt4v-emotion-dataset
2023-10-27T01:06:16.000Z
[ "region:us" ]
laion
null
null
2
483
2023-10-15T18:25:14
--- dataset_info: features: - name: caption dtype: string - name: link dtype: string - name: message_id dtype: string - name: timestamp dtype: string splits: - name: train num_bytes: 204134 num_examples: 96 download_size: 111233 dataset_size: 204134 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "gpt4v-emotion-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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GEM/web_nlg
2022-10-24T15:31:09.000Z
[ "task_categories:table-to-text", "annotations_creators:unknown", "language_creators:unknown", "multilinguality:unknown", "size_categories:unknown", "source_datasets:original", "language:en", "license:cc-by-nc-4.0", "data-to-text", "region:us" ]
GEM
WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available.
@inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared, title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)}, author={Castro Ferreira, Thiago and Gardent, Claire and Ilinykh, Nikolai and van der Lee, Chris and Mille, Simon and Moussallem, Diego and Shimorina, Anastasia}, booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)}, pages = "55--76", year = 2020, address = {Dublin, Ireland (Virtual)}, publisher = {Association for Computational Linguistics}}
2
479
2022-03-02T23:29:22
--- annotations_creators: - unknown language_creators: - unknown language: - en license: - cc-by-nc-4.0 multilinguality: - unknown size_categories: - unknown source_datasets: - original task_categories: - table-to-text task_ids: [] pretty_name: web_nlg tags: - data-to-text --- # Dataset Card for GEM/web_nlg ## Dataset Description - **Homepage:** https://webnlg-challenge.loria.fr/ - **Repository:** https://gitlab.com/shimorina/webnlg-dataset - **Paper:** http://www.aclweb.org/anthology/P17-1017, [WebNLG Challenge 2017 Report - **Leaderboard:** https://beng.dice-research.org/gerbil/ - **Point of Contact:** [Needs More Information] ### Link to Main Data Card You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/web_nlg). ### Dataset Summary WebNLG is a bi-lingual dataset (English, Russian) of parallel DBpedia triple sets and short texts that cover about 450 different DBpedia properties. The WebNLG data was originally created to promote the development of RDF verbalisers able to generate short text and to handle micro-planning (i.e., sentence segmentation and ordering, referring expression generation, aggregation); the goal of the task is to generate texts starting from 1 to 7 input triples which have entities in common (so the input is actually a connected Knowledge Graph). The dataset contains about 17,000 triple sets and 45,000 crowdsourced texts in English, and 7,000 triples sets and 19,000 crowdsourced texts in Russian. A challenging test set section with entities and/or properties that have not been seen at training time is available. You can load the dataset via: ``` import datasets data = datasets.load_dataset('GEM/web_nlg') ``` The data loader can be found [here](https://huggingface.co/datasets/GEM/web_nlg). #### website [Website](https://webnlg-challenge.loria.fr/) #### paper [First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) #### authors The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil). ## Dataset Overview ### Where to find the Data and its Documentation #### Webpage <!-- info: What is the webpage for the dataset (if it exists)? --> <!-- scope: telescope --> [Website](https://webnlg-challenge.loria.fr/) #### Download <!-- info: What is the link to where the original dataset is hosted? --> <!-- scope: telescope --> [Gitlab](https://gitlab.com/shimorina/webnlg-dataset) #### Paper <!-- info: What is the link to the paper describing the dataset (open access preferred)? --> <!-- scope: telescope --> [First Dataset Release](http://www.aclweb.org/anthology/P17-1017), [WebNLG Challenge 2017 Report](https://www.aclweb.org/anthology/W17-3518/), [WebNLG Challenge 2020 Report](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) #### BibTex <!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. --> <!-- scope: microscope --> Initial release of the dataset: ``` @inproceedings{gardent2017creating, author = "Gardent, Claire and Shimorina, Anastasia and Narayan, Shashi and Perez-Beltrachini, Laura", title = "Creating Training Corpora for NLG Micro-Planners", booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", year = "2017", publisher = "Association for Computational Linguistics", pages = "179--188", location = "Vancouver, Canada", doi = "10.18653/v1/P17-1017", url = "http://www.aclweb.org/anthology/P17-1017" } ``` The latest version 3.0: ``` @inproceedings{castro-ferreira20:bilin-bi-direc-webnl-shared, title={The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task Overview and Evaluation Results (WebNLG+ 2020)}, author={Castro Ferreira, Thiago and Gardent, Claire and Ilinykh, Nikolai and van der Lee, Chris and Mille, Simon and Moussallem, Diego and Shimorina, Anastasia}, booktitle = {Proceedings of the 3rd WebNLG Workshop on Natural Language Generation from the Semantic Web (WebNLG+ 2020)}, pages = "55--76", year = 2020, address = {Dublin, Ireland (Virtual)}, publisher = {Association for Computational Linguistics}} ``` #### Contact Email <!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. --> <!-- scope: periscope --> webnlg-challenge@inria.fr #### Has a Leaderboard? <!-- info: Does the dataset have an active leaderboard? --> <!-- scope: telescope --> yes #### Leaderboard Link <!-- info: Provide a link to the leaderboard. --> <!-- scope: periscope --> [Website](https://beng.dice-research.org/gerbil/) #### Leaderboard Details <!-- info: Briefly describe how the leaderboard evaluates models. --> <!-- scope: microscope --> The model outputs are evaluated against the crowdsourced references; the leaderboard reports BLEU-4, METEOR, chrF++, TER, BERTScore and BLEURT scores. ### Languages and Intended Use #### Multilingual? <!-- quick --> <!-- info: Is the dataset multilingual? --> <!-- scope: telescope --> yes #### Covered Languages <!-- quick --> <!-- info: What languages/dialects are covered in the dataset? --> <!-- scope: telescope --> `Russian`, `English` #### License <!-- quick --> <!-- info: What is the license of the dataset? --> <!-- scope: telescope --> cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International #### Intended Use <!-- info: What is the intended use of the dataset? --> <!-- scope: microscope --> The WebNLG dataset was created to promote the development (_i_) of RDF verbalisers and (_ii_) of microplanners able to handle a wide range of linguistic constructions. The dataset aims at covering knowledge in different domains ("categories"). The same properties and entities can appear in several categories. #### Primary Task <!-- info: What primary task does the dataset support? --> <!-- scope: telescope --> Data-to-Text #### Communicative Goal <!-- quick --> <!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. --> <!-- scope: periscope --> A model should verbalize all and only the provided input triples in natural language. ### Credit #### Curation Organization Type(s) <!-- info: In what kind of organization did the dataset curation happen? --> <!-- scope: telescope --> `academic` #### Curation Organization(s) <!-- info: Name the organization(s). --> <!-- scope: periscope --> Université de Lorraine / LORIA, France, CNRS / LORIA, France, University of Edinburgh, UK, Federal University of Minas Gerais, Brazil #### Dataset Creators <!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). --> <!-- scope: microscope --> The principle curator of the dataset is Anastasia Shimorina (Université de Lorraine / LORIA, France). Throughout the WebNLG releases, several people contributed to their construction: Claire Gardent (CNRS / LORIA, France), Shashi Narayan (Google, UK), Laura Perez-Beltrachini (University of Edinburgh, UK), Elena Khasanova, and Thiago Castro Ferreira (Federal University of Minas Gerais, Brazil). #### Funding <!-- info: Who funded the data creation? --> <!-- scope: microscope --> The dataset construction was funded by the French National Research Agency (ANR). #### Who added the Dataset to GEM? <!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. --> <!-- scope: microscope --> Simon Mille and Sebastian Gehrmann added the dataset and wrote the data card. ### Dataset Structure #### Data Fields <!-- info: List and describe the fields present in the dataset. --> <!-- scope: telescope --> See [official documentation](https://webnlg-challenge.loria.fr/docs/). `entry`: a data instance of the benchmark. Each entry has five attributes: a DBpedia category (`category`), entry ID (`eid`), shape, shape type, and triple set size (`size`). - `shape`: a string representation of the RDF tree with nested parentheses where `X` is a node (see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format)). - `shape_type`: a type of the tree shape. We [identify](https://www.aclweb.org/anthology/C16-1141.pdf) three types of tree shapes: * `chain` (the object of one triple is the subject of the other); * `sibling` (triples with a shared subject); * `mixed` (both `chain` and `sibling` types present). - `eid`: an entry ID. It is unique only within a category and a size. - `category`: a DBpedia category (Astronaut, City, MusicalWork, Politician, etc.). - `size`: the number of RDF triples in a set. Ranges from 1 to 7. Each `entry` has three fields: `originaltripleset`, `modifiedtripleset`, and `lexs`. `originaltripleset`: a set of RDF triples as extracted from [DBpedia](https://wiki.dbpedia.org/). Each set of RDF triples is a tree. Triples have the subject-predicate-object structure. `modifiedtripleset`: a set of RDF triples as presented to crowdworkers (for more details on modifications, see below). Original and modified triples serve different purposes: the original triples — to link data to a knowledge base (DBpedia), whereas the modified triples — to ensure consistency and homogeneity throughout the data. To train models, the modified triples should be used. `lexs` (shortened for lexicalisations): a natural language text verbalising the triples. Each lexicalisation has two attributes: a comment (`comment`), and a lexicalisation ID (`lid`). By default, comments have the value `good`, except rare cases when they were manually marked as `toFix`. That was done during the corpus creation, when it was seen that a lexicalisation did not exactly match a triple set. Russian data has additional optional fields comparing to English: `<dbpedialinks>`: RDF triples extracted from DBpedia between English and Russian entities by means of the property `sameAs`. `<links>`: RDF triples created manually for some entities to serve as pointers to translators. There are two types of them: * with `sameAs` (`Spaniards | sameAs | испанцы`) * with `includes` (`Tomatoes, guanciale, cheese, olive oil | includes | гуанчиале`). Those were mostly created for string literals to translate some parts of them. Lexicalisations in the Russian WebNLG have a new parameter `lang` (values: `en`, `ru`) because original English texts were kept in the Russian version (see the example above). #### Example Instance <!-- info: Provide a JSON formatted example of a typical instance in the dataset. --> <!-- scope: periscope --> ``` { "entry": { "category": "Company", "size": "4", "shape": "(X (X) (X) (X) (X))", "shape_type": "sibling", "eid": "Id21", "lexs": [ { "comment": "good", "lex": "Trane, which was founded on January 1st 1913 in La Crosse, Wisconsin, is based in Ireland. It has 29,000 employees.", "lid": "Id1" } ], "modifiedtripleset": [ { "subject": "Trane", "property": "foundingDate", "object": "1913-01-01" }, { "subject": "Trane", "property": "location", "object": "Ireland" }, { "subject": "Trane", "property": "foundationPlace", "object": "La_Crosse,_Wisconsin" }, { "subject": "Trane", "property": "numberOfEmployees", "object": "29000" } ], "originaltriplesets": { "originaltripleset": [ { "subject": "Trane", "property": "foundingDate", "object": "1913-01-01" }, { "subject": "Trane", "property": "location", "object": "Ireland" }, { "subject": "Trane", "property": "foundationPlace", "object": "La_Crosse,_Wisconsin" }, { "subject": "Trane", "property": "numberOfEmployees", "object": "29000" } ] } } } ``` The XML-formatted example is [here](https://webnlg-challenge.loria.fr/docs/#example). #### Data Splits <!-- info: Describe and name the splits in the dataset if there are more than one. --> <!-- scope: periscope --> | English (v3.0) | Train | Dev | Test | |-----------------|--------|-------|-------| | **triple sets** | 13,211 | 1,667 | 1,779 | | **texts** | 35,426 | 4,464 | 5,150 | |**properties** | 372 | 290 | 220 | | Russian (v3.0) | Train | Dev | Test | |-----------------|--------|-------|-------| | **triple sets** | 5,573 | 790 | 1,102 | | **texts** | 14,239 | 2,026 | 2,780 | |**properties** | 226 | 115 | 192 | ## Dataset in GEM ### Rationale for Inclusion in GEM #### Why is the Dataset in GEM? <!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? --> <!-- scope: microscope --> Due to the constrained generation task, this dataset can be used to evaluate very specific and narrow generation capabilities. #### Similar Datasets <!-- info: Do other datasets for the high level task exist? --> <!-- scope: telescope --> yes #### Unique Language Coverage <!-- info: Does this dataset cover other languages than other datasets for the same task? --> <!-- scope: periscope --> yes #### Difference from other GEM datasets <!-- info: What else sets this dataset apart from other similar datasets in GEM? --> <!-- scope: microscope --> The RDF-triple format is unique to WebNLG. #### Ability that the Dataset measures <!-- info: What aspect of model ability can be measured with this dataset? --> <!-- scope: periscope --> surface realization ### GEM-Specific Curation #### Modificatied for GEM? <!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? --> <!-- scope: telescope --> yes #### GEM Modifications <!-- info: What changes have been made to he original dataset? --> <!-- scope: periscope --> `other` #### Modification Details <!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification --> <!-- scope: microscope --> No changes to the main content of the dataset. The [version 3.0](https://gitlab.com/shimorina/webnlg-dataset/-/tree/master/release_v3.0) of the dataset is used. #### Additional Splits? <!-- info: Does GEM provide additional splits to the dataset? --> <!-- scope: telescope --> yes #### Split Information <!-- info: Describe how the new splits were created --> <!-- scope: periscope --> 23 special test sets for WebNLG were added to the GEM evaluation suite, 12 for English and 11 for Russian. For both languages, we created subsets of the training and development sets of ~500 randomly selected inputs each. The inputs were sampled proportionally from each category. Two types of transformations have been applied to WebNLG: (i) input scrambling (English and Russian) and (ii) numerical value replacements (English); in both cases, a subset of about 500 inputs was randomly selected. For (i), the order of the triples was randomly reassigned (each triple kept the same Subject-Property-Object internal order). For (ii), the change was performed respecting the format of the current cardinal value (e.g., alpha, integer, or floating-point) and replacing it with a new random value. The new number is lower-bounded between zero and upper bounded to be within to the highest power of 10 unit for the given value (e.g., replacing 54 would result in a random value between 0-100). Floating values maintain the degree of precision. For both languages, we did identify different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently 8 selections that we have made: Selection 1 (size): input length. This selection corresponds to the number of predicates in the input. By comparing inputs of different lengths, we can see to what extent NLG systems are able to handle different input sizes. The table below provides the relevant frequencies. Please be aware that comparing selections with fewer than 100 items may result in unreliable comparisons. | Input length | Frequency English | Frequency Russian | |----------------|-------------------|-------------------| | 1 | 369 | 254 | | 2 | 349 | 200 | | 3 | 350 | 214 | | 4 | 305 | 214 | | 5 | 213 | 159 | | 6 | 114 | 32 | | 7 | 79 | 29 | Selection 2 (frequency): seen/unseen single predicates. This selection corresponds to the inputs with only one predicate. We compare which predicates are seen/unseen in the training data. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there is only one example of unseen single predicates. | _ in training | Frequency English | Frequency Russian | |---------------|-------------------|-------------------| | Seen | 297 | 253 | | Unseen | 72 | 1 | Selection 3 (frequency): seen/unseen combinations of predicates. This selection checks for all combinations of predicates whether that combination has been seen in the training data. For example: if the combination of predicates A and B is seen, that means that there is an input in the training data consisting of two triples, where one triple uses predicate A and the other uses predicate B. If the combination is unseen, then the converse is true. The table below provides the relevant frequencies. | _ in training | Frequency English | Frequency Russian | |---------------|-------------------|-------------------| | unseen | 1295 | 354 | | seen | 115 | 494 | Selection 4 (frequency): seen/unseen arguments. This selection checks for all input whether or not all arg1s and arg2s in the input have been seen during the training phase. For this selection, *Seen* is the default. Only if all arg1 instances for a particular input are unseen, do we count the arg1s of the input as unseen. The same holds for arg2. So "seen" here really means that at least some of the arg1s or arg2s are seen in the input. The table below provides the relevant frequencies. Note that the comparison is only valid for English. Not for Russian, since there are very few examples of unseen combinations of predicates. | Arguments seen in training? | Frequency English | Frequency Russian | |-----------------------------|-------------------|-------------------| | both_seen | 518 | 1075 | | both_unseen | 1177 | 4 | | arg1_unseen | 56 | 19 | | arg2_unseen | 28 | 4 | Selection 5 (shape): repeated subjects. For this selection, the subsets are based on the times a subject is repeated in the input; it only takes into account the maximum number of times a subject is repeated, that is, if in one input a subject appears 3 times and a different subject 2 times, this input will be in the "3_subjects_same' split. Unique_subjects means all subjects are different. | Max num. of repeated subjects | Frequency English | Frequency Russian | |-------------------------------|-------------------|-------------------| | unique_subjects | 453 | 339 | | 2_subjects_same | 414 | 316 | | 3_subjects_same | 382 | 217 | | 4_subjects_same | 251 | 143 | | 5_subjects_same | 158 | 56 | | 6_subjects_same | 80 | 19 | | 7_subjects_same | 41 | 12 | Selection 6 (shape): repeated objects. Same as for subjects above, but for objects. There are much less cases of repeated objects, so there are only two categories for this selection, unique_objects and some_objects_repeated; for the latter, we have up to 3 coreferring objects in English, and XXX in Russian. | Max num. of repeated objects | Frequency English | Frequency Russian | |------------------------------|-------------------|-------------------| | unique_objects | 1654 | 1099 | | some_objects_same | 125 | 3 | Selection 7 (shape): repeated properties. Same as for objects above, but for properties; up to two properties can be the same in English, up to XXX in Russian. | Max num. of repeated properties | Frequency English | Frequency Russian | |---------------------------------|-------------------|-------------------| | unique_properties | 1510 | 986 | | some_properties_same | 269 | 116 | Selection 8 (shape): entities that appear both as subject and object. For this selection, we grouped together the inputs in which no entity is found as both subject and object, and on the other side inputs in which one or more entity/ies appear both as subject and as object. We found up to two such entities per input in English, and up to XXX in Russian. | Max num. of objects and subjects in common | Frequency English | Frequency Russian | |--------------------------------------------|-------------------|-------------------| | unique_properties | 1322 | 642 | | some_properties_same | 457 | 460 | #### Split Motivation <!-- info: What aspects of the model's generation capacities were the splits created to test? --> <!-- scope: periscope --> Robustness ### Getting Started with the Task #### Pointers to Resources <!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. --> <!-- scope: microscope --> Dataset construction: [main dataset paper](https://www.aclweb.org/anthology/P17-1017/), [RDF triple extraction](https://www.aclweb.org/anthology/C16-1141/), [Russian translation](https://www.aclweb.org/anthology/W19-3706/) WebNLG Challenge 2017: [webpage](https://webnlg-challenge.loria.fr/challenge_2017/), [paper](https://www.aclweb.org/anthology/W17-3518/) WebNLG Challenge 2020: [webpage](https://webnlg-challenge.loria.fr/challenge_2020/), [paper](https://webnlg-challenge.loria.fr/files/2020.webnlg-papers.7.pdf) Enriched version of WebNLG: [repository](https://github.com/ThiagoCF05/webnlg), [paper](https://www.aclweb.org/anthology/W18-6521/) Related research papers: [webpage](https://webnlg-challenge.loria.fr/research/) ## Previous Results ### Previous Results #### Proposed Evaluation <!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. --> <!-- scope: microscope --> For both languages, the participating systems are automatically evaluated in a multi-reference scenario. Each English hypothesis is compared to a maximum of 5 references, and each Russian one to a maximum of 7 references. On average, English data has 2.89 references per test instance, and Russian data has 2.52 references per instance. In a human evaluation, example are uniformly sampled across size of triple sets and the following dimensions are assessed (on MTurk and Yandex.Toloka): 1. Data Coverage: Does the text include descriptions of all predicates presented in the data? 2. Relevance: Does the text describe only such predicates (with related subjects and objects), which are found in the data? 3. Correctness: When describing predicates which are found in the data, does the text mention correct the objects and adequately introduces the subject for this specific predicate? 4. Text Structure: Is the text grammatical, well-structured, written in acceptable English language? 5. Fluency: Is it possible to say that the text progresses naturally, forms a coherent whole and it is easy to understand the text? For additional information like the instructions, we refer to the original paper. #### Previous results available? <!-- info: Are previous results available? --> <!-- scope: telescope --> yes #### Other Evaluation Approaches <!-- info: What evaluation approaches have others used? --> <!-- scope: periscope --> We evaluated a wide range of models as part of the GEM benchmark. #### Relevant Previous Results <!-- info: What are the most relevant previous results for this task/dataset? --> <!-- scope: microscope --> Results can be found on the [GEM website](https://gem-benchmark.com/results). ## Broader Social Context ### Previous Work on the Social Impact of the Dataset #### Usage of Models based on the Data <!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? --> <!-- scope: telescope --> yes - related tasks #### Social Impact Observations <!-- info: Did any of these previous uses result in observations about the social impact of the systems? In particular, has there been work outlining the risks and limitations of the system? Provide links and descriptions here. --> <!-- scope: microscope --> We do not foresee any negative social impact in particular from this dataset or task. Positive outlooks: Being able to generate good quality text from RDF data would permit, e.g., making this data more accessible to lay users, enriching existing text with information drawn from knowledge bases such as DBpedia or describing, comparing and relating entities present in these knowledge bases. ### Impact on Under-Served Communities #### Addresses needs of underserved Communities? <!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). --> <!-- scope: telescope --> no ### Discussion of Biases #### Any Documented Social Biases? <!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. --> <!-- scope: telescope --> yes #### Links and Summaries of Analysis Work <!-- info: Provide links to and summaries of works analyzing these biases. --> <!-- scope: microscope --> This dataset is created using DBpedia RDF triples which naturally exhibit biases that have been found to exist in Wikipedia such as some forms of, e.g., gender bias. The choice of [entities](https://gitlab.com/shimorina/webnlg-dataset/-/blob/master/supplementary/entities_dict.json), described by RDF trees, was not controlled. As such, they may contain gender biases; for instance, all the astronauts described by RDF triples are male. Hence, in texts, pronouns _he/him/his_ occur more often. Similarly, entities can be related to the Western culture more often than to other cultures. #### Are the Language Producers Representative of the Language? <!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? --> <!-- scope: periscope --> In English, the dataset is limited to the language that crowdraters speak. In Russian, the language is heavily biased by the translationese of the translation system that is post-edited. ## Considerations for Using the Data ### PII Risks and Liability #### Potential PII Risk <!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. --> <!-- scope: microscope --> There is no PII in this dataset. ### Licenses #### Copyright Restrictions on the Dataset <!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? --> <!-- scope: periscope --> `non-commercial use only` #### Copyright Restrictions on the Language Data <!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? --> <!-- scope: periscope --> `public domain` ### Known Technical Limitations #### Technical Limitations <!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. --> <!-- scope: microscope --> The quality of the crowdsourced references is limited, in particular in terms of fluency/naturalness of the collected texts. Russian data was machine-translated and then post-edited by crowdworkers, so some examples may still exhibit issues related to bad translations. #### Unsuited Applications <!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. --> <!-- scope: microscope --> Only a limited number of domains are covered in this dataset. As a result, it cannot be used as a general-purpose realizer.
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jxie/slurp
2023-10-25T04:31:33.000Z
[ "region:us" ]
jxie
null
null
0
479
2023-10-25T04:13:20
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* dataset_info: features: - name: audio dtype: audio - name: transcription dtype: string splits: - name: train num_bytes: 2917605666.136 num_examples: 50628 - name: validation num_bytes: 476772454.9 num_examples: 8690 - name: test num_bytes: 708848109.726 num_examples: 13078 download_size: 3913982157 dataset_size: 4103226230.762 --- # Dataset Card for "slurp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
720
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Cohere/wikipedia-22-12
2023-02-22T15:58:09.000Z
[ "region:us" ]
Cohere
null
null
26
477
2023-01-13T21:52:20
This dataset contains a pre-processed version from Wikipedia suitable for semantic search. You can load the dataset like this: ```python from datasets import load_dataset lang = 'en' data = load_dataset(f"Cohere/wikipedia-22-12", lang, split='train', streaming=True) for row in data: print(row) break ``` This will load the dataset in a streaming mode (so that you don't need to download the whole dataset) and you can process it row-by-row. The articles are splitted into paragraphs. Further, for each article we added statistics on the page views in 2022 as well as in how many other languages an article is available. The dataset is sorted by page views, so that the most popular Wikipedia articles come first. So if you e.g. read the top-100k rows, you get quite a good coverage on topics that are broadly interesting for people. ## Semantic Search Embeddings We also provide versions where documents have been embedded using the [cohere multilingual embedding model](https://txt.cohere.ai/multilingual/), e.g. [wikipedia-22-12-en-embeddings](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings) contains the paragraphs and their respective embeddings for English. You can find the embeddings for other languages in the datasets `wikipedia-22-12-{lang}-embeddings`. ## Dataset Creation The [XML data dumps](https://dumps.wikimedia.org/backup-index.html) from December 20th, 2022 where downloaded and processed with [wikiextractor](https://github.com/attardi/wikiextractor) (with Version: 2.75) and the following command: ``` python WikiExtractor.py --json -s --lists ../dumps/dewiki-20210101-pages-articles.xml.bz2 -o text_de ``` To count in how many languages an article is available, we downloaded the SQL files with language links from: ``` https://dumps.wikimedia.org/{lang}wiki/{datestr}/{filename} ``` And processed the SQL file to read for each article the outbound links. Pageviews where downloaded from: ``` https://dumps.wikimedia.org/other/pageviews/{year}/{year}-{month_str}/pageviews-{year}{month_str}{day_str}-{hour_str}0000.gz ``` We downloaded for each day the pageviews for a random hour. We then computed the harmonic mean of page views. We used harmonic mean to address cases where articles receive a very high number of page views at e.g. a certain time point. We use the log scores for the page views to increase the numerical stability. Code to compute the page views was: ```python import gzip import sys from collections import Counter, defaultdict import math import tqdm import json title_views = {} #Score: Harmonic mean (View_Day_1 * View_Day_2 * View_day_3) # Add log for better numerical stabilitiy # Add +1 to avoid log(0) # Compare the sum, so that days without view are counted as 0 views for filepath in tqdm.tqdm(sys.argv[1:]): with gzip.open(filepath, "rt") as fIn: for line in fIn: splits = line.strip().split() if len(splits) == 4: lang, title, views, _ = line.strip().split() lang = lang.lower() if lang.endswith(".m"): #Add mobile page scores to main score lang = lang[0:-2] if lang.count(".") > 0: continue if lang not in title_views: title_views[lang] = {} if title not in title_views[lang]: title_views[lang][title] = 0.0 title_views[lang][title] += math.log(int(views)+1) #Save results for lang in title_views: with open(f"pageviews_summary/{lang}.json", "w") as fOut: fOut.write(json.dumps(title_views[lang])) ``` We filter out paragraphs that start with `BULLET::::`, `Section::::`, `<templatestyles`, or `[[File:`. Further, we also only include paragraphs with at least 100 characters (using Python len method=.
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Critiquers/gsm8k_pairwise
2023-08-23T19:29:20.000Z
[ "region:us" ]
Critiquers
null
null
1
476
2023-08-23T19:29:16
--- dataset_info: features: - name: prompt dtype: string - name: selected dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 411013 num_examples: 512 download_size: 234406 dataset_size: 411013 --- # Dataset Card for "gsm8k_pairwise" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
428
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IlyaGusev/gpt_roleplay_realm
2023-05-21T12:43:08.000Z
[ "task_categories:text-generation", "task_categories:conversational", "size_categories:1K<n<10K", "language:ru", "language:en", "license:cc-by-4.0", "gpt-4", "fictional", "role-play", "gpt-3.5", "art", "region:us" ]
IlyaGusev
null
null
42
474
2023-05-06T23:21:10
--- dataset_info: features: - name: name dtype: string - name: context dtype: string - name: greeting dtype: string - name: example_dialogue list: - name: content dtype: string - name: role dtype: string - name: topics sequence: string - name: dialogues list: - name: chat list: - name: content dtype: string - name: role dtype: string - name: model_name dtype: string - name: topic dtype: string - name: image_prompt dtype: string - name: image dtype: image - name: char_id dtype: string splits: - name: en num_bytes: 197727921.0 num_examples: 216 - name: ru num_bytes: 207461896.0 num_examples: 219 download_size: 396187206 dataset_size: 405189817.0 license: cc-by-4.0 task_categories: - text-generation - conversational language: - ru - en tags: - gpt-4 - fictional - role-play - gpt-3.5 - art pretty_name: GPT Role-play Realm size_categories: - 1K<n<10K --- # GPT Role-play Realm Dataset: The AI-generated character compendium This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play. <img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" > * 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4. * 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5. * Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/). Code for downloading all English character cards: ``` import os from PIL.PngImagePlugin import PngInfo from datasets import load_dataset output_dir = "role_play_realm_en" os.makedirs(output_dir, exist_ok=True) for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"): char_id = row["char_id"] char_info = row["image"].info["chara"] info = PngInfo() info.add_text("chara", char_info) row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info) ``` ## Russian example: Cyber-Granny <img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" > **Имя**: Кибер-Бабушка **Контекст**: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом. **Приветствие**: Здравствуй, дорогой! Чем могу помочь тебе сегодня? **Пример диалога**: - **Пользователь**: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками? - **Персонаж**: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов! - **Пользователь**: Спасибо, Кибер-Бабушка! - **Персонаж**: Всегда рада помочь, дорогой! Не забудь приходить еще! И ещё **20** диалогов по следующим темам: - Секреты приготовления старинных блюд и рецептов. - Использование новых технологий для мастеров кулинарии. - Воспитание внуков с применением современных методов. - Что делать, если дома произошла авария или неполадка с бытовой техникой? - Влияние киборгизации на отношения между поколениями. - Как адаптироваться к современному миру, не теряя своих традиций? - Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях. - Лечение и профилактика заболеваний в помощь силам передовой медицины. - Как создать уют в доме с помощью модных технологий и традиционных методов? - Безопасность в пространстве интернета: советы Кибер-Бабушки. - Как научиться доверять технике без потери человеческих ценностей? - Идеальный гардероб для жизни: советы от Кибер-Бабушки. - Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками. - Как развивать креативные способности, используя сочетание новых технологий и традиций? - На новоселье: тренировка кибер-бабушкиного чутья. - Лучшие семейные игры и развлечения с использованием передовых технологий. - Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов. - Советы по финансовому планированию и сбережениям для будущих поколений. - Кибер-Бабушка и генетический код: на что способны современные технологии? - Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии? ## English example: Flibberdoodle <img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png"> **Name**: Flibberdoodle **Context**: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species **Greeting**: \*chirp chirp\* Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things? **Example dialogue**: - **User**: How did you start collecting shiny things? - **Character**: \*squeak\* Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find! - **User**: What's your favorite shiny object in your collection? - **Character**: \*purr\* That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around! And **20** more dialogues with following topics: - Life and culture on the planet Fluffonia - How Scruffapuffs change color based on mood - The process of learning Scruffapuff language - The day in the life of a Scruffapuff - Methods of searching for and finding shiny objects - The role of antennae in Scruffapuff communication and biology - The importance of play and curiosity in Scruffapuff society - Interplanetary travel experiences and adventures - Similarities and differences between Earth and Fluffonia - How Flibberdoodle and other Scruffapuffs interact with other species - Fluffonian customs and traditions - The role of shiny objects in Scruffapuff happiness and well-being - Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships - Flibberdoodle's favorite memories and stories from Fluffonia - The role of Scruffapuffs in intergalactic diplomacy and relations - How to care for and befriend a Scruffapuff - The most interesting and valuable shiny objects Flibberdoodle has encountered - Fluffonian flora and fauna - The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects ## Steps ### Step 1: Character generation (GPT-4) Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue". * Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py) * Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl) * English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl) * Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt) * English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt) ### Step 2: Topics generation (GPT-4) Creates topics for conversations with characters based on their description. Output field: "topics". * Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py) * Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt) * English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt) ### Step 3: Dialogue generation (GPT-4/GPT-3.5) Generates dialogues based on a character description and a topic. Output field: "dialogues". * Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py) * Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt) * English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt) ### Step 4: Text2Image prompt generation (GPT-4) Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt". * Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py) * Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt) ### Step 5: Image generation Generates images based on prompts. Output field: "image". * Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py)
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SetFit/amazon_counterfactual_en
2022-02-11T13:03:45.000Z
[ "arxiv:2104.06893", "region:us" ]
SetFit
null
null
0
473
2022-03-02T23:29:22
# Amazon Counterfactual Statements This dataset is the *en-ext* split from [SetFit/amazon_counterfactual](https://huggingface.co/datasets/SetFit/amazon_counterfactual). As the original test set is rather small (1333 examples), a different split was created with 50-50 for training & testing. The dataset is described in [amazon-multilingual-counterfactual-dataset](https://github.com/amazon-research/amazon-multilingual-counterfactual-dataset) / [Paper](https://arxiv.org/pdf/2104.06893.pdf) It contains statements from Amazon reviews about events that did not or cannot take place.
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bigbio/ddi_corpus
2022-12-22T15:44:31.000Z
[ "multilinguality:monolingual", "language:en", "license:cc-by-nc-4.0", "region:us" ]
bigbio
The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine.
@article{HERREROZAZO2013914, title = { The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions }, author = { María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry Declerck }, year = 2013, journal = {Journal of Biomedical Informatics}, volume = 46, number = 5, pages = {914--920}, doi = {https://doi.org/10.1016/j.jbi.2013.07.011}, issn = {1532-0464}, url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123}, keywords = {Biomedical corpora, Drug interaction, Information extraction} }
2
473
2022-11-13T22:08:08
--- language: - en bigbio_language: - English license: cc-by-nc-4.0 multilinguality: monolingual bigbio_license_shortname: CC_BY_NC_4p0 pretty_name: DDI Corpus homepage: https://github.com/isegura/DDICorpus bigbio_pubmed: True bigbio_public: True bigbio_tasks: - NAMED_ENTITY_RECOGNITION - RELATION_EXTRACTION --- # Dataset Card for DDI Corpus ## Dataset Description - **Homepage:** https://github.com/isegura/DDICorpus - **Pubmed:** True - **Public:** True - **Tasks:** NER,RE The DDI corpus has been manually annotated with drugs and pharmacokinetics and pharmacodynamics interactions. It contains 1025 documents from two different sources: DrugBank database and MedLine. ## Citation Information ``` @article{HERREROZAZO2013914, title = { The DDI corpus: An annotated corpus with pharmacological substances and drug-drug interactions }, author = { María Herrero-Zazo and Isabel Segura-Bedmar and Paloma Martínez and Thierry Declerck }, year = 2013, journal = {Journal of Biomedical Informatics}, volume = 46, number = 5, pages = {914--920}, doi = {https://doi.org/10.1016/j.jbi.2013.07.011}, issn = {1532-0464}, url = {https://www.sciencedirect.com/science/article/pii/S1532046413001123}, keywords = {Biomedical corpora, Drug interaction, Information extraction} } ```
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minh21/cpgQA-v1.0-unique-context
2023-08-30T13:16:37.000Z
[ "region:us" ]
minh21
null
null
0
473
2023-08-30T13:05:48
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: title dtype: string - name: id dtype: int64 - name: question dtype: string - name: answer_text dtype: string - name: answer_start dtype: int64 - name: context dtype: string splits: - name: train num_bytes: 1167197 num_examples: 871 - name: test num_bytes: 268232 num_examples: 226 download_size: 190979 dataset_size: 1435429 --- # Dataset Card for "cpgQA-v1.0-unique-context" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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celikmus/mayo_clinic_symptoms_and_diseases_v1
2023-07-16T19:37:52.000Z
[ "language:en", "region:us" ]
celikmus
null
null
6
470
2023-03-21T21:31:15
--- language: en dataset_info: features: - name: text dtype: string - name: label dtype: string splits: - name: train num_bytes: 1321926 num_examples: 1058 download_size: 626009 dataset_size: 1321926 --- # Dataset Card for "mayo_clinic_symptoms_and_diseases_v1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
424
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nampdn-ai/tiny-codes
2023-09-30T04:14:36.000Z
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:en", "license:mit", "arxiv:2306.11644", "arxiv:2305.07759", "doi:10.57967/hf/0937", "region:us" ]
nampdn-ai
null
null
131
469
2023-07-16T07:26:18
--- license: mit task_categories: - text-generation language: - en pretty_name: Tiny Codes size_categories: - 1M<n<10M --- # Reasoning with Language and Code This synthetic dataset is a collection of **1.6 millions short and clear code snippets** that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities. The main goal of this repository is to highlight the importance of **textbook (high education value)** using **code snippets**. All code snippets are carefully written and commented to ensure maximum readability and understandability. Moreover, the use of **if/else control flow** is emphasized to foster the development of effective reasoning skills in LLM models. This repository is inspired by the paper [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) and [The Magic of IF](https://aclanthology.org/2023.findings-acl.574.pdf), which shows that LLM models can achieve state-of-the-art results on code-related tasks by training on high-quality data that resembles textbooks and exercises. This repository aims to provide such data for data analysts and ML engineers who want to enhance their knowledge of how LLM models can learn to reason with code. Anyone who wants to reproduce this dataset can use these prompts with other LLM models and compare their results, or you can forge a new prompt from related properties. *Please note that this dataset is not intended for code-generation purposes, it's intended to boost the reasoning capability of model via logic code.* I hope you find this dataset useful and informative! ## Tiny Series Explore the possibilities and limitations of building Small Language Models with these tiny gems of data! - [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series. - [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks. - [tiny-orca-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-orca-textbooks): Synthetic textbook to help model learn in-context on how it should perform task the right way. - [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset. - [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format. - [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models. ### Others small HQ datasets with textbook-like quality - [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages. - [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook) - [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming) - [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)
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segments/sidewalk-semantic
2023-07-10T08:09:07.000Z
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:expert-generated", "size_categories:n<1K", "source_datasets:original", "license:cc-by-nc-4.0", "region:us" ]
segments
null
null
20
468
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced - expert-generated language_creators: - expert-generated license: cc-by-nc-4.0 multilinguality: [] pretty_name: sidewalk-semantic size_categories: - n<1K source_datasets: - original task_categories: - image-segmentation task_ids: - semantic-segmentation --- # Dataset Card for sidewalk-semantic ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Dataset Structure](#dataset-structure) - [Data Categories](#data-categories) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [Dataset homepage on Segments.ai](https://segments.ai/segments/sidewalk-imagery/) - **Repository:** [Needs More Information] - **Paper:** [Needs More Information] - **Leaderboard:** [Needs More Information] - **Point of Contact:** [Bert De Brabandere](mailto:bert@segments.ai) ### Dataset Summary A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on [segments.ai](https://segments.ai/?utm_source=hf&utm_medium=hf-ds&utm_campaign=sidewalk) ### Supported Tasks and Leaderboards - `semantic-segmentation`: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its [mean IoU (intersection over union)](https://huggingface.co/metrics/mean_iou) to the reference is. ## Dataset Structure ### Data categories | Id | Name | Description | | --- | ---- | ----------- | | 0 | unlabeled | - | | 1 | flat-road | - | | 2 | flat-sidewalk | - | | 3 | flat-crosswalk | - | | 4 | flat-cyclinglane | - | | 5 | flat-parkingdriveway | - | | 6 | flat-railtrack | - | | 7 | flat-curb | - | | 8 | human-person | - | | 9 | human-rider | - | | 10 | vehicle-car | - | | 11 | vehicle-truck | - | | 12 | vehicle-bus | - | | 13 | vehicle-tramtrain | - | | 14 | vehicle-motorcycle | - | | 15 | vehicle-bicycle | - | | 16 | vehicle-caravan | - | | 17 | vehicle-cartrailer | - | | 18 | construction-building | - | | 19 | construction-door | - | | 20 | construction-wall | - | | 21 | construction-fenceguardrail | - | | 22 | construction-bridge | - | | 23 | construction-tunnel | - | | 24 | construction-stairs | - | | 25 | object-pole | - | | 26 | object-trafficsign | - | | 27 | object-trafficlight | - | | 28 | nature-vegetation | - | | 29 | nature-terrain | - | | 30 | sky | - | | 31 | void-ground | - | | 32 | void-dynamic | - | | 33 | void-static | - | | 34 | void-unclear | - | ### Data Instances [Needs More Information] ### Data Fields [Needs More Information] ### Data Splits This dataset only contains one split. ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information [Needs More Information]
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ignmilton/ign_clean_instruct_dataset_500k
2023-06-13T07:45:51.000Z
[ "task_categories:question-answering", "task_categories:conversational", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "code", "region:us" ]
ignmilton
null
null
18
468
2023-06-12T07:12:30
--- license: apache-2.0 task_categories: - question-answering - conversational language: - en tags: - code pretty_name: ign_500k size_categories: - 100K<n<1M --- This dataset contains ~508k prompt-instruction pairs with high quality responses. It was synthetically created from a subset of Ultrachat prompts. It does not contain any alignment focused responses or NSFW content. Licensed under apache-2.0
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google_wellformed_query
2022-11-18T20:04:48.000Z
[ "task_categories:text-classification", "task_ids:text-scoring", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended", "language:en", "license:cc-by-sa-4.0", "arxiv:1808.09419", "region:us" ]
null
Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
@misc{faruqui2018identifying, title={Identifying Well-formed Natural Language Questions}, author={Manaal Faruqui and Dipanjan Das}, year={2018}, eprint={1808.09419}, archivePrefix={arXiv}, primaryClass={cs.CL} }
8
467
2022-03-02T23:29:22
--- task_categories: - text-classification multilinguality: - monolingual task_ids: - text-scoring language: - en annotations_creators: - crowdsourced source_datasets: - extended size_categories: - 10K<n<100K license: - cc-by-sa-4.0 paperswithcode_id: null pretty_name: GoogleWellformedQuery language_creators: - found dataset_info: features: - name: rating dtype: float32 - name: content dtype: string splits: - name: train num_bytes: 857391 num_examples: 17500 - name: test num_bytes: 189503 num_examples: 3850 - name: validation num_bytes: 184110 num_examples: 3750 download_size: 1157019 dataset_size: 1231004 --- # Dataset Card for Google Query-wellformedness Dataset ## 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/google-research-datasets/query-wellformedness) - **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness) - **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English ## Dataset Structure ### Data Instances ``` {'rating': 0.2, 'content': 'The European Union includes how many ?'} ``` ### Data Fields - `rating`: a `float` between 0-1 - `sentence`: query which you want to rate ### Data Splits | | Train | Valid | Test | | ----- | ------ | ----- | ---- | | Input Sentences | 17500 | 3750 | 3850 | ## Dataset Creation ### Curation Rationale Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. ### Source Data Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”). #### Initial Data Collection and Normalization Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process The queries are annotated into well-formed or non-wellformed questions if it satisfies the following: 1. Query is grammatical. 2. Query is an explicit question. 3. Query does not contain spelling errors. #### Who are the annotators? Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed. ### 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 Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied. ### Citation Information ``` @InProceedings{FaruquiDas2018, title = {{Identifying Well-formed Natural Language Questions}}, author = {Faruqui, Manaal and Das, Dipanjan}, booktitle = {Proc. of EMNLP}, year = {2018} } ``` ### Contributions Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
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rcds/swiss_judgment_prediction
2023-06-14T11:59:24.000Z
[ "task_categories:text-classification", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "language:fr", "language:it", "language:en", "license:cc-by-sa-4.0", "judgement-prediction", "arxiv:2110.00806", "arxiv:2209.12325", "region:us" ]
rcds
Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
@InProceedings{niklaus-etal-2021-swiss, author = {Niklaus, Joel and Chalkidis, Ilias and Stürmer, Matthias}, title = {Swiss-Court-Predict: A Multilingual Legal Judgment Prediction Benchmark}, booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop}, year = {2021}, location = {Punta Cana, Dominican Republic}, } @misc{niklaus2022empirical, title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction}, author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis}, year={2022}, eprint={2209.12325}, archivePrefix={arXiv}, primaryClass={cs.CL} }
11
466
2022-03-02T23:29:22
--- pretty_name: Swiss-Judgment-Prediction annotations_creators: - found language_creators: - found language: - de - fr - it - en license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-classification task_ids: [] tags: - judgement-prediction dataset_info: - config_name: de features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 104270719 num_examples: 35458 - name: validation num_bytes: 12131878 num_examples: 4705 - name: test num_bytes: 26056177 num_examples: 9725 download_size: 1000382331 dataset_size: 142458774 - config_name: fr features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 96807957 num_examples: 21179 - name: validation num_bytes: 13031904 num_examples: 3095 - name: test num_bytes: 33318359 num_examples: 6820 download_size: 1000382331 dataset_size: 143158220 - config_name: it features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 10773516 num_examples: 3072 - name: validation num_bytes: 1045551 num_examples: 408 - name: test num_bytes: 2474761 num_examples: 812 download_size: 1000382331 dataset_size: 14293828 - config_name: mt_de features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 106990696 num_examples: 24251 - name: validation - name: test download_size: 1000382331 dataset_size: 106990696 - config_name: mt_fr features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 117932134 num_examples: 38524 - name: validation - name: test download_size: 1000382331 dataset_size: 117932134 - config_name: mt_it features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 201749076 num_examples: 56631 - name: validation - name: test download_size: 1000382331 dataset_size: 201749076 - config_name: mt_en features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 196352783 num_examples: 59703 - name: validation - name: test download_size: 1000382331 dataset_size: 196352783 - config_name: all features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 211852192 num_examples: 59709 - name: validation num_bytes: 26209333 num_examples: 8208 - name: test num_bytes: 61849297 num_examples: 17357 download_size: 1000382331 dataset_size: 299910822 - config_name: all+mt features: - name: id dtype: int32 - name: year dtype: int32 - name: text dtype: string - name: label dtype: class_label: names: '0': dismissal '1': approval - name: language dtype: string - name: region dtype: string - name: canton dtype: string - name: legal area dtype: string - name: source_language dtype: string splits: - name: train num_bytes: 834876881 num_examples: 238818 - name: validation num_bytes: 26209333 num_examples: 8208 - name: test num_bytes: 61849297 num_examples: 17357 download_size: 1000382331 dataset_size: 922935511 --- # Dataset Card for "SwissJudgmentPrediction" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/JoelNiklaus/SwissCourtRulingCorpus - **Repository:** https://github.com/JoelNiklaus/SwissCourtRulingCorpus - **Paper:** https://arxiv.org/abs/2110.00806 - **Leaderboard:** N/A - **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus@inf.unibe.ch) ### Dataset Summary **Documents** Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP. ### Supported Tasks and Leaderboards SwissJudgmentPrediction can be used for the legal judgment prediction task. The dataset is not yet part of an established benchmark. ### Languages Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ## Dataset Structure In version 2 we added machine translated data using [EasyNMT](https://github.com/UKPLab/EasyNMT) for all documents into German, French, Italian and English as an additional training set. ### Data Instances **Multilingual use of the dataset** When the dataset is used in a multilingual setting selecting the the 'all_languages' flag: ```python from datasets import load_dataset dataset = load_dataset('swiss_judgment_prediction', 'all_languages') ``` ``` { "id": 48757, "year": 2015, "facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ", "labels": 0, # dismissal "language": "de", "region": "Espace Mittelland", "canton": "be", "legal area": "penal law" } ``` **Monolingual use of the dataset** When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example: ```python from datasets import load_dataset dataset = load_dataset('swiss_judgment_prediction', 'de') ``` ``` { "id": 48757, "year": 2015, "facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ", "labels": 0, # dismissal "language": "de", "region": "Espace Mittelland", "canton": "be", "legal area": "penal law" } ``` ### Data Fields **Multilingual use of the dataset** The following data fields are provided for documents (`train`, `validation`, `test`): `id`: (**int**) a unique identifier of the for the document \ `year`: (**int**) the publication year \ `text`: (**str**) the facts of the case \ `label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \ `language`: (**str**) one of (de, fr, it) \ `region`: (**str**) the region of the lower court \ `canton`: (**str**) the canton of the lower court \ `legal area`: (**str**) the legal area of the case **Monolingual use of the dataset** The following data fields are provided for documents (`train`, `validation`, `test`): `id`: (**int**) a unique identifier of the for the document \ `year`: (**int**) the publication year \ `text`: (**str**) the facts of the case \ `label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \ `language`: (**str**) one of (de, fr, it) \ `region`: (**str**) the region of the lower court \ `canton`: (**str**) the canton of the lower court \ `legal area`: (**str**) the legal area of the case ### Data Splits | Language | Subset | Number of Documents (Training/Validation/Test) | |------------|------------|------------------------------------------------| | German | **de** | 35'452 / 4'705 / 9'725 | | French | **fr** | 21'179 / 3'095 / 6'820 | | Italian | **it** | 3'072 / 408 / 812 | | All | **all** | 59'709 / 8'208 / 17'357 | | MT German | **mt_de** | 24'251 / 0 / 0 | | MT French | **mt_fr** | 38'524 / 0 / 0 | | MT Italian | **mt_it** | 56'631 / 0 / 0 | | MT All | **all+mt** | 238'818 / 8'208 / 17'357 | ## Dataset Creation ### Curation Rationale The dataset was curated by Niklaus et al. (2021). ### Source Data #### Initial Data Collection and Normalization The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. #### Who are the source language producers? Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings. ### Annotations #### Annotation process The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions. #### Who are the annotators? Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes. Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch). ### Personal and Sensitive Information The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html. ## 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 Niklaus et al. (2021) ### Licensing Information We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf) © Swiss Federal Supreme Court, 2000-2020 The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made. Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf ### Citation Information *Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer.* *Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark* *Proceedings of the 2021 Natural Legal Language Processing Workshop. Punta Cana, Dominican Republic. 2021* ``` @InProceedings{niklaus-etal-2021-swiss, author = {Niklaus, Joel and Chalkidis, Ilias and Stürmer, Matthias}, title = {Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark}, booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop}, year = {2021}, location = {Punta Cana, Dominican Republic}, } ``` and the new citation ``` @misc{niklaus2022empirical, title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction}, author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis}, year={2022}, eprint={2209.12325}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@joelniklaus](https://github.com/joelniklaus) for adding this dataset.
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EleutherAI/pile-deduped-pythia-random-sampled
2023-08-25T07:26:47.000Z
[ "region:us" ]
EleutherAI
null
null
2
466
2023-03-29T13:15:01
--- dataset_info: features: - name: Index dtype: int64 - name: 70M dtype: float64 - name: 160M dtype: float64 - name: 410M dtype: float64 - name: 1B dtype: float64 - name: 1.4B dtype: float64 - name: 2.8B dtype: float64 - name: 6.9B dtype: float64 - name: 12B dtype: float64 - name: Tokens sequence: uint16 splits: - name: train num_bytes: 1020000000 num_examples: 5000000 download_size: 915854656 dataset_size: 1020000000 --- # Dataset Card for "pile-deduped-pythia-random-sampled" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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KShivendu/dbpedia-entities-openai-1M
2023-07-07T08:35:48.000Z
[ "size_categories:1M<n<10M", "language:en", "license:mit", "region:us" ]
KShivendu
null
null
8
465
2023-06-20T22:29:43
--- license: mit dataset_info: features: - name: _id dtype: string - name: title dtype: string - name: text dtype: string - name: openai sequence: float32 splits: - name: train num_bytes: 12383152 num_examples: 1000000 download_size: 12383152 dataset_size: 1000000 language: - en pretty_name: OpenAI 1M with DBPedia Entities size_categories: - 1M<n<10M --- 1M OpenAI Embeddings (1536 dimensions) from June 2023. Text used for Embedding: title (string) + text (string) First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/ ### Future work We are planning to take this up to 10M (and possibly 100M) vectors. Contact [@KShivendu_](https://twitter.com/KShivendu_) on Twitter or mail to hello@nirantk.com if you want to help :) ### Credits: This dataset was generated from the first 1M entries of https://huggingface.co/datasets/BeIR/dbpedia-entity
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BeIR/msmarco-qrels
2022-10-23T06:05:55.000Z
[ "task_categories:text-retrieval", "task_ids:entity-linking-retrieval", "task_ids:fact-checking-retrieval", "multilinguality:monolingual", "language:en", "license:cc-by-sa-4.0", "region:us" ]
BeIR
null
null
1
464
2022-06-05T17:26:07
--- annotations_creators: [] language_creators: [] language: - en license: - cc-by-sa-4.0 multilinguality: - monolingual paperswithcode_id: beir pretty_name: BEIR Benchmark size_categories: msmarco: - 1M<n<10M trec-covid: - 100k<n<1M nfcorpus: - 1K<n<10K nq: - 1M<n<10M hotpotqa: - 1M<n<10M fiqa: - 10K<n<100K arguana: - 1K<n<10K touche-2020: - 100K<n<1M cqadupstack: - 100K<n<1M quora: - 100K<n<1M dbpedia: - 1M<n<10M scidocs: - 10K<n<100K fever: - 1M<n<10M climate-fever: - 1M<n<10M scifact: - 1K<n<10K source_datasets: [] task_categories: - text-retrieval - zero-shot-retrieval - information-retrieval - zero-shot-information-retrieval task_ids: - passage-retrieval - entity-linking-retrieval - fact-checking-retrieval - tweet-retrieval - citation-prediction-retrieval - duplication-question-retrieval - argument-retrieval - news-retrieval - biomedical-information-retrieval - question-answering-retrieval --- # Dataset Card for BEIR Benchmark ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/UKPLab/beir - **Repository:** https://github.com/UKPLab/beir - **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ - **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns - **Point of Contact:** nandan.thakur@uwaterloo.ca ### Dataset Summary BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks: - Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact) - Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/) - Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) - News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html) - Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data) - Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) - Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs) - Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html) - Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/) All these datasets have been preprocessed and can be used for your experiments. ```python ``` ### Supported Tasks and Leaderboards The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia. The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/). ### Languages All tasks are in English (`en`). ## Dataset Structure All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format: - `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}` - `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}` - `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1` ### Data Instances A high level example of any beir dataset: ```python corpus = { "doc1" : { "title": "Albert Einstein", "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \ one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \ its influence on the philosophy of science. He is best known to the general public for his mass–energy \ equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \ Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \ of the photoelectric effect', a pivotal step in the development of quantum theory." }, "doc2" : { "title": "", # Keep title an empty string if not present "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \ malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\ with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)." }, } queries = { "q1" : "Who developed the mass-energy equivalence formula?", "q2" : "Which beer is brewed with a large proportion of wheat?" } qrels = { "q1" : {"doc1": 1}, "q2" : {"doc2": 1}, } ``` ### Data Fields Examples from all configurations have the following features: ### Corpus - `corpus`: a `dict` feature representing the document title and passage text, made up of: - `_id`: a `string` feature representing the unique document id - `title`: a `string` feature, denoting the title of the document. - `text`: a `string` feature, denoting the text of the document. ### Queries - `queries`: a `dict` feature representing the query, made up of: - `_id`: a `string` feature representing the unique query id - `text`: a `string` feature, denoting the text of the query. ### Qrels - `qrels`: a `dict` feature representing the query document relevance judgements, made up of: - `_id`: a `string` feature representing the query id - `_id`: a `string` feature, denoting the document id. - `score`: a `int32` feature, denoting the relevance judgement between query and document. ### Data Splits | Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 | | -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:| | MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` | | TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` | | NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` | | BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) | | NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` | | HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` | | FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` | | Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) | | TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) | | ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` | | Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` | | CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` | | Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` | | DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` | | SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` | | FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` | | Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` | | SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` | | Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) | ## Dataset Creation ### Curation Rationale [Needs More Information] ### Source Data #### Initial Data Collection and Normalization [Needs More Information] #### Who are the source language producers? [Needs More Information] ### Annotations #### Annotation process [Needs More Information] #### Who are the annotators? [Needs More Information] ### Personal and Sensitive Information [Needs More Information] ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information [Needs More Information] ### Citation Information Cite as: ``` @inproceedings{ thakur2021beir, title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models}, author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)}, year={2021}, url={https://openreview.net/forum?id=wCu6T5xFjeJ} } ``` ### Contributions Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.
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opus_rf
2023-06-01T14:59:53.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:sv", "license:unknown", "region:us" ]
null
RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations.
@InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
0
463
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - expert-generated language: - de - en - es - fr - sv license: - unknown multilinguality: - multilingual size_categories: - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusRf dataset_info: - config_name: de-en features: - name: id dtype: string - name: translation dtype: translation: languages: - de - en splits: - name: train num_bytes: 38683 num_examples: 177 download_size: 16029 dataset_size: 38683 - config_name: de-es features: - name: id dtype: string - name: translation dtype: translation: languages: - de - es splits: - name: train num_bytes: 2316 num_examples: 24 download_size: 2403 dataset_size: 2316 - config_name: de-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - de - fr splits: - name: train num_bytes: 41300 num_examples: 173 download_size: 16720 dataset_size: 41300 - config_name: de-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - de - sv splits: - name: train num_bytes: 37414 num_examples: 178 download_size: 15749 dataset_size: 37414 - config_name: en-es features: - name: id dtype: string - name: translation dtype: translation: languages: - en - es splits: - name: train num_bytes: 2600 num_examples: 25 download_size: 2485 dataset_size: 2600 - config_name: en-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - en - fr splits: - name: train num_bytes: 39503 num_examples: 175 download_size: 16038 dataset_size: 39503 - config_name: en-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - en - sv splits: - name: train num_bytes: 35778 num_examples: 180 download_size: 15147 dataset_size: 35778 - config_name: es-fr features: - name: id dtype: string - name: translation dtype: translation: languages: - es - fr splits: - name: train num_bytes: 2519 num_examples: 21 download_size: 2469 dataset_size: 2519 - config_name: es-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - es - sv splits: - name: train num_bytes: 3110 num_examples: 28 download_size: 2726 dataset_size: 3110 - config_name: fr-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - fr - sv splits: - name: train num_bytes: 38627 num_examples: 175 download_size: 15937 dataset_size: 38627 config_names: - de-en - de-es - de-fr - de-sv - en-es - en-fr - en-sv - es-fr - es-sv - fr-sv --- # Dataset Card for [Dataset Name] ## 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:** http://opus.nlpl.eu/RF.php - **Repository:** - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English (en), Spanish (es), German (de), French (fr), Swedish (sv) ## 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 ``` @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset.
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vietgpt/the_pile_openwebtext2
2023-07-15T09:20:18.000Z
[ "language:en", "region:us" ]
vietgpt
null
null
1
463
2023-04-11T19:24:36
--- language: en dataset_info: features: - name: title dtype: string - name: text dtype: string - name: reddit_scores sequence: int32 splits: - name: train num_bytes: 68786199155 num_examples: 17103059 download_size: 42444568964 dataset_size: 68786199155 --- # Dataset Card for "the_pile_openwebtext2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
470
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yangwang825/sst2-textfooler
2023-10-09T22:09:14.000Z
[ "region:us" ]
yangwang825
null
null
0
463
2023-10-09T21:11:56
# Stanford Sentiment Treebank - Binary
38
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gsarti/clean_mc4_it
2022-10-23T09:01:21.000Z
[ "task_categories:text-generation", "task_ids:language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "source_datasets:extended", "language:it", "license:odc-by", "arxiv:1910.10683", "arxiv:2203.03759", "region:us" ]
gsarti
A thoroughly cleaned version of the Italian portion of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4) by AllenAI. Based on Common Crawl dataset: "https://commoncrawl.org". This is the processed version of Google's mC4 dataset by AllenAI, with further cleaning detailed in the repository README file.
@article{JMLR:v21:20-074, author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu}, title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {140}, pages = {1-67}, url = {http://jmlr.org/papers/v21/20-074.html} }
6
462
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - it license: - odc-by multilinguality: - monolingual size_categories: tiny: - 1M<n<10M small: - 10M<n<100M medium: - 10M<n<100M large: - 10M<n<100M full: - 100M<n<1B source_datasets: - extended task_categories: - text-generation task_ids: - language-modeling paperswithcode_id: mc4 pretty_name: mC4_it --- # Dataset Card for Clean Italian mC4 🇮🇹 ## Table of Contents - [Dataset Card for Clean](#dataset-card-for-mc4) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Preprocessing](#preprocessing) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [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 - **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4) - **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683) ### Dataset Summary A thoroughly cleaned version of the Italian split of the multilingual colossal, cleaned version of Common Crawl's web crawl corpus (mC4). Based on the [Common Crawl dataset](https://commoncrawl.org). The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4), with subsequent preprocessing performed by [Gabriele Sarti](https://gsarti.com) following a standard procedure for all dataset shards. ### Preprocessing The preprocessing of the dataset follows the procedure used by Yeb Havinga for training the model [`t5-base-dutch`](https://huggingface.co/flax-community/t5-base-dutch) on a portion of the cleaned Dutch split of mC4. The original code, that was adapted for Italian in this case, is available on [GitLab](https://gitlab.com/yhavinga/c4nlpreproc). In summary, the preprocessing procedure includes: - Removing documents containing words from a selection of the [Italian and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words). - Removing sentences containing: - Less than 3 words. - A word longer than 1000 characters. - An end symbol not matching end-of-sentence punctuation. - Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Italian or English. - Removing documents (after sentence filtering): - Containing less than 5 sentences. - Containing less than 500 or more than 50'000 characters. - Not identified as prevalently Italian by the `LangDetect` package. Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Italian shards of mC4 (1024 of ~220Mb train, 8 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure. ## Dataset Structure ### Data Instances An example from the dataset: ``` { 'timestamp': '2020-02-22T22:24:31Z', 'url': 'https://altreconomia.it/una-rotonda-sul-pane/', 'text': 'Per raggiungere il campo attraversiamo la striscia d’asfalto che porta verso la provinciale numero 13. Mettiamo a rischio la nostra incolumità in un territorio di auto e camion. Sullo sfondo, i profili della Grigna e del Resegone. Più vicini, quelli del solito ipermercato di provincia, e delle villette a schiera che avanzano tra le coltivazioni. È lo sprawling, l’avanzata del cemento.\\nDa questo lato dalla strada, invece, è ancora regno contadino. Almeno per ora. Torniamo a Caponago (Mb), Brianza pura, dove ha avuto i natali il progetto “Spiga e madia”. Ne parlammo su Ae nel gennaio 2009: in un territorio “spaesato”, il Comitato “verso il Distretto di economia solidale della Brianza” (Desbri) e la “Retina” dei gruppi di acquisto locali danno vita a un progetto di produzione di frumento, molitura, panificazione e distribuzione in un raggio di 20 chilometri. Si comincia da zero, nel 2007, senza alcun di finanziamento, quando una famiglia del [...]. Il giochino vale almeno 3 miliardi di euro all’anno. La misura, introdotta in via straordinaria con la finanziaria 2005, è stata prorogata anche con l’ultimo decreto “milleproroghe”.' } ``` ### Data Fields The data contains the following fields: - `url`: url of the source as a string - `text`: text content as a string - `timestamp`: timestamp of extraction as a string ### Data Splits To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages. For Italian, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following the naming style `c4-it.tfrecord-0XXXX-of-01024.json.gz` and 8 for validation following the naming style `c4-it-validation.tfrecord-0000X-of-00008.json.gz`. The full set of preprocessed files takes roughly 215GB of disk space to download with Git LFS. For ease of use under different storage capacities, the following incremental splits are available (sizes are estimates). **Important**: The sizes in GB represent the estimated weight for : |split |train size (docs, words, download + preproc disk space)|validation size| |:-----|------------------------------------------------------:|--------------:| |tiny | 10M docs, 4B words (9 GB + 27 GB) | 12k docs | |small | 20M docs, 8B words (18 GB + 54 GB) | 24k docs | |medium| 50M docs, 20B words (47 GB + 135 GB) | 48k docs | |large | 75M docs, 30B words (71 GB + 203 GB) | 72k docs | |full | 103M docs, 41B words (109 GB + 279 GB) | 96k docs | You can load any subset like this: ```python from datasets import load_dataset mc4_it_tiny = load_dataset("gsarti/clean_mc4_it", "tiny") ``` Since splits are quite large, you may want to traverse them using the streaming mode available starting from 🤗 Datasets v1.9.0: ```python from datasets import load_dataset mc4_it_full_stream = load_dataset("gsarti/clean_mc4_it", "full", split='train', streaming=True) print(next(iter(mc4_it_full_stream))) # Prints the example presented above ``` ## Dataset Creation Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`. ## Considerations for Using the Data ### Social Impact of Dataset With more than 200GB of cleaned Italian text and more than 41B estimated words, this is by far the largest available corpus for the Italian language. The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 69GB in size for its deduplicated variant. Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language. This can in turn have important repercussions for the development of commercial language technology applications for the Italian language. ### Discussion of Biases Despit the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will inevitably reflect biases present in blog articles and comments on the Internet. This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts. ## Additional Information ### Dataset Curators Authors at AllenAI are the original curators for the `mc4` corpus. For inquiries or requests regarding the Italian cleaned portion contained in this repository, please contact me at [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com) ### Licensing Information AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset. ### Citation Information If you use this dataset in your work, please cite us and the original mC4 authors as: ``` @article{sarti-nissim-2022-it5, title={IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation}, author={Sarti, Gabriele and Nissim, Malvina}, journal={ArXiv preprint 2203.03759}, url={https://arxiv.org/abs/2203.03759}, year={2022}, month={mar} } @inproceedings{xue-etal-2021-mt5, title = "m{T}5: A Massively Multilingual Pre-trained Text-to-Text Transformer", author = "Xue, Linting and Constant, Noah and Roberts, Adam and Kale, Mihir and Al-Rfou, Rami and Siddhant, Aditya and Barua, Aditya and Raffel, Colin", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.naacl-main.41", doi = "10.18653/v1/2021.naacl-main.41", pages = "483--498", } ``` ### Contributions Thanks to [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for adding this dataset.
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embedding-data/QQP_triplets
2022-08-02T03:14:14.000Z
[ "task_categories:sentence-similarity", "task_ids:semantic-similarity-classification", "language:en", "license:mit", "region:us" ]
embedding-data
null
null
3
462
2022-07-08T03:15:59
--- license: mit language: - en paperswithcode_id: embedding-data/QQP_triplets pretty_name: QQP_triplets task_categories: - sentence-similarity - paraphrase-mining task_ids: - semantic-similarity-classification --- # Dataset Card for "QQP_triplets" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - **Repository:** [More Information Needed](http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv) - **Paper:** [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) - **Point of Contact:** [Kornél Csernai](https://www.quora.com/profile/Korn%C3%A9l-Csernai), [Nikhil Dandekar](https://www.quora.com/profile/Nikhil-Dandekar), [Shankar Iyer](https://www.quora.com/profile/Shankar-Iyer-5) ### Dataset Summary This dataset will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data. The data is organized as triplets (anchor, positive, negative). Disclaimer: The team releasing Quora data did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team. ### Supported Tasks - [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity. ### Languages - English. ## Dataset Structure Each example is a dictionary with three keys (query, pos, and neg) containing a list each (triplets). The first key contains an anchor sentence, the second a positive sentence, and the third a list of negative sentences. ``` {"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]} {"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]} ... {"query": [anchor], "pos": [positive], "neg": [negative1, negative2, ..., negativeN]} ``` This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train them. ### Usage Example Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with: ```python from datasets import load_dataset dataset = load_dataset("embedding-data/QQP_triplets") ``` The dataset is loaded as a `DatasetDict` and has the format: ```python DatasetDict({ train: Dataset({ features: ['set'], num_rows: 101762 }) }) ``` Review an example `i` with: ```python dataset["train"][i]["set"] ``` ### Curation Rationale [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) #### Who are the source language producers? [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Annotations #### Annotation process [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) #### Who are the annotators? [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Personal and Sensitive Information [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Discussion of Biases [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Other Known Limitations Here are a few important things to keep in mind about this dataset: - Our original sampling method returned an imbalanced dataset with many more true examples of duplicate pairs than non-duplicates. Therefore, we supplemented the dataset with negative examples. - One source of negative examples were pairs of “related questions” which, although pertaining to similar topics, are not truly semantically equivalent. - The distribution of questions in the dataset should not be taken to be representative of the distribution of questions asked on Quora. This is, in part, because of the combination of sampling procedures and also due to some sanitization measures that have been applied to the final dataset (e.g., removal of questions with extremely long question details). - The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect. ## Additional Information ### Dataset Curators [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Licensing Information [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Citation Information [More Information Needed](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) ### Contributions Thanks to [Kornél Csernai](https://www.quora.com/profile/Korn%C3%A9l-Csernai), [Nikhil Dandekar](https://www.quora.com/profile/Nikhil-Dandekar), [Shankar Iyer](https://www.quora.com/profile/Shankar-Iyer-5) for adding this dataset.
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detection-datasets/fashionpedia
2022-09-22T13:22:02.000Z
[ "task_categories:object-detection", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:cc-by-4.0", "object-detection", "fashion", "computer-vision", "arxiv:2004.12276", "region:us" ]
detection-datasets
null
null
25
462
2022-09-22T10:33:24
--- pretty_name: Fashionpedia task_categories: - object-detection language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original tags: - object-detection - fashion - computer-vision paperswithcode_id: fashionpedia --- # Dataset Card for Fashionpedia ## 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) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://fashionpedia.github.io/home/index.html - **Repository:** https://github.com/cvdfoundation/fashionpedia - **Paper:** https://arxiv.org/abs/2004.12276 ### Dataset Summary Fashionpedia is a dataset mapping out the visual aspects of the fashion world. From the paper: > Fashionpedia is a new dataset which consists of two parts: (1) an ontology built by fashion experts containing 27 main apparel categories, 19 apparel parts, 294 fine-grained attributes and their relationships; (2) a dataset with everyday and celebrity event fashion images annotated with segmentation masks and their associated per-mask fine-grained attributes, built upon the Fashionpedia ontology. Fashionpedia has: - 46781 images - 342182 bounding-boxes ### Supported Tasks - Object detection - Image classification ### Languages All of annotations use English as primary language. ## Dataset Structure The dataset is structured as follows: ```py DatasetDict({ train: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 45623 }) val: Dataset({ features: ['image_id', 'image', 'width', 'height', 'objects'], num_rows: 1158 }) }) ``` ### Data Instances An example of the data for one image is: ```py {'image_id': 23, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=682x1024>, 'width': 682, 'height': 1024, 'objects': {'bbox_id': [150311, 150312, 150313, 150314], 'category': [23, 23, 33, 10], 'bbox': [[445.0, 910.0, 505.0, 983.0], [239.0, 940.0, 284.0, 994.0], [298.0, 282.0, 386.0, 352.0], [210.0, 282.0, 448.0, 665.0]], 'area': [1422, 843, 373, 56375]}} ``` With the type of each field being defined as: ```py {'image_id': Value(dtype='int64'), 'image': Image(decode=True), 'width': Value(dtype='int64'), 'height': Value(dtype='int64'), 'objects': Sequence(feature={ 'bbox_id': Value(dtype='int64'), 'category': ClassLabel(num_classes=46, names=['shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel']), 'bbox': Sequence(feature=Value(dtype='float64'), length=4), 'area': Value(dtype='int64')}, length=-1)} ``` ### Data Fields The dataset has the following fields: - `image_id`: Unique numeric ID of the image. - `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` - `width`: Image width. - `height`: Image height. - `objects`: A dictionary containing bounding box metadata for the objects in the image: - `bbox_id`: Unique numeric ID of the bounding box annotation. - `category`: The object’s category. - `area`: The area of the bounding box. - `bbox`: The object’s bounding box (in the Pascal VOC format) ### Data Splits | | Train | Validation | Test | |----------------|--------|------------|------| | Images | 45623 | 1158 | 0 | | Bounding boxes | 333401 | 8781 | 0 | ## Additional Information ### Licensing Information Fashionpedia is licensed under a Creative Commons Attribution 4.0 International License. ### Citation Information ``` @inproceedings{jia2020fashionpedia, title={Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset}, author={Jia, Menglin and Shi, Mengyun and Sirotenko, Mikhail and Cui, Yin and Cardie, Claire and Hariharan, Bharath and Adam, Hartwig and Belongie, Serge} booktitle={European Conference on Computer Vision (ECCV)}, year={2020} } ``` ### Contributions Thanks to [@blinjrm](https://github.com/blinjrm) for adding this dataset.
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voiceintelligenceresearch/MOCKS
2023-10-27T15:55:12.000Z
[ "annotations_creators:expert-generated", "multilinguality:multilingual", "language:en", "language:de", "language:es", "language:fr", "language:it", "license:cc-by-4.0", "license:mpl-2.0", "region:us" ]
voiceintelligenceresearch
Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models.
@inproceedings{pudo23_interspeech, author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki}, title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset}, year={2023}, booktitle={Proc. Interspeech 2023}, }
0
462
2023-02-20T13:40:22
--- annotations_creators: - expert-generated language: - en - de - es - fr - it license: - cc-by-4.0 - mpl-2.0 multilinguality: - multilingual dataset_info: - config_name: config features: - name: audio_id dtype: string - name: audio dtype: audio: sampling_rate: 16000 - name: text dtype: string --- # MOCKS: Multilingual Open Custom Keyword Spotting Testset ## 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 - **Paper:** [MOCKS 1.0: Multilingual Open Custom Keyword Spotting Testset](https://www.isca-speech.org/archive/pdfs/interspeech_2023/pudo23_interspeech.pdf) ### Dataset Summary Multilingual Open Custom Keyword Spotting Testset (MOCKS) is a comprehensive audio testset for evaluation and benchmarking Open-Vocabulary Keyword Spotting (OV-KWS) models. It supports multiple OV-KWS problems: both text-based and audio-based keyword spotting, as well as offline and online (streaming) modes. It is based on the LibriSpeech and Mozilla Common Voice datasets and contains almost 50,000 keywords, with audio data available in English, French, German, Italian, and Spanish. The testset was generated using automatically generated alignments used for the extraction of parts of the recordings that were split into keywords and test samples. MOCKS contains both positive and negative examples selected based on phonetic transcriptions that are challenging and should allow for in-depth OV-KWS model evaluation. Please refer to our [paper](https://www.isca-speech.org/archive/pdfs/interspeech_2023/pudo23_interspeech.pdf) for further details. ### Supported Tasks and Leaderboards The MOCKS dataset can be used for the Open-Vocabulary Keyword Spotting (OV-KWS) task. It supports two OV-KWS types: - Query-by-Text, where the keyword is provided by text and needs to be detected in the audio stream. - Query-by-Example, where the keyword is provided with enrollment audio for detection in the audio stream. It also allows for: - offline keyword detection, where test audio is trimmed to contain only keywords of interest. - online (streaming) keyword detection, where test audio has past and future context besides keywords of interest. ### Languages The MOCKS incorporates 5 languages: - English - primary and largest test set, - German, - Spanish, - French, - Italian. ## Dataset Structure The MOCKS testset is split by language, source dataset, and OV-KWS type: ``` MOCKS │ └───de │ └───MCV │ │ └───test │ │ │ └───offline │ │ │ │ │ all.pair.different.tsv │ │ │ │ │ all.pair.positive.tsv │ │ │ │ │ all.pair.similar.tsv │ │ │ │ │ data.tar.gz │ │ │ │ │ subset.pair.different.tsv │ │ │ │ │ subset.pair.positive.tsv │ │ │ │ │ subset.pair.similar.tsv │ │ │ │ │ │ │ └───online │ │ │ │ │ all.pair.different.tsv │ │ │ │ │ ... │ │ │ │ data.offline.transcription.tsv │ │ │ │ data.online.transcription.tsv │ └───en │ └───LS-clean │ │ └───test │ │ │ └───offline │ │ │ │ │ all.pair.different.tsv │ │ │ │ │ ... │ │ │ │ ... │ │ │ └───LS-other │ │ └───test │ │ │ └───offline │ │ │ │ │ all.pair.different.tsv │ │ │ │ │ ... │ │ │ │ ... │ │ │ └───MCV │ │ └───test │ │ │ └───offline │ │ │ │ │ all.pair.different.tsv │ │ │ │ │ ... │ │ │ │ ... │ └───... ``` Each split is divided into: - positive examples (`all.pair.positive.tsv`) - test examples with true keywords, 5000-8000 keywords in each subset, - similar examples (`all.pair.similar.tsv`) - test examples with similar phrases to the keyword selected based on phonetic transcription distance, - different examples (`all.pair.different.tsv`) - test examples with completely different phrases. All those files contain columns separated by tab: - `keyword_path` - path to audio containing keyword phrase. - `adversary_keyword_path` - path to test audio. - `adversary_keyword_timestamp_start` - start time in seconds of phrase of interest for a given keyword from `keyword_path`, the field only available in **offline** split. - `adversary_keyword_timestamp_end` - end time in seconds of phrase of interest for a given keyword from `keyword_path`, the field only available in **offline** split. - `label` - whether the `adversary_keyword_path` contain keyword from `keyword_path` or not (1 - contains keyword, 0 - doesn't contain keyword). Each split also contains a subset of whole data with the same field structure to allow faster evaluation (`subset.pair.*.tsv`). Also, transcriptions are provided for each audio in: - `data_offline_transcription.tsv` - transcriptions for **offline** examples and `keyword_path` from **online** scenario, - `data_online_transcription.tsv` - transcriptions for the adversary, test examples from **online** scenario, three columns are present within each file: - `path_to_keyword`/`path_to_adversary_keyword` - path to the audio file, - `keyword_transcription`/`adversary_keyword_transcription` - audio transcription, - `keyword_phonetic_transcription`/`adversary_keyword_phonetic_transcription` - audio phonetic transcription. ## Using the Dataset The dataset can be used by: - downloading the archive and constructing all the test cases based on the provided `tsv` files, - `datasets` package. In the latter case, the following should work: ``` load_dataset(path="voiceintelligenceresearch/MOCKS", name="en.LS-clean", split="offline") ``` The allowed values for `name` are: - `en.LS-{clean,other}`, - `en.LS-{clean,other}.positive`, - `en.LS-{clean,other}.similar`, - `en.LS-{clean,other}.different`, - `en.LS-{clean,other}.subset`, - `en.LS-{clean,other}.positive_subset`, - `en.LS-{clean,other}.similar_subset`, - `en.LS-{clean,other}.different_subset`, - `{de,en,es,fr,it}.MCV.positive`, - `{de,en,es,fr,it}.MCV.positive.similar`, - `{de,en,es,fr,it}.MCV.positive.different`, - `{de,en,es,fr,it}.MCV.positive.subset`, - `{de,en,es,fr,it}.MCV.positive.positive_subset`, - `{de,en,es,fr,it}.MCV.positive.similar_subset`, - `{de,en,es,fr,it}.MCV.positive.different_subset`. The allowed values for `split` are: - `offline`, - `online`. `load_dataset` provides a list of the dictionary objects with the following contents: ``` { "keyword_id": datasets.Value("string"), "keyword_transcription": datasets.Value("string"), "test_id": datasets.Value("string"), "test_transcription": datasets.Value("string"), "test_audio": datasets.Audio(sampling_rate=16000), "label": datasets.Value("bool"), } ``` Each element of this list represents a single test case for the QbyT KWS: - `keyword_id` - the name of the keyword audio file in `data.tar.gz` (not used in QbyT KWS), - `keyword_transcription` - transcription of the keyword, - `test_id` - the name of the test audio file in `data.tar.gz`, - `test_transcription` - transcription of the test sample, - `test_audio` - raw data of the test audio, - `label` - `True` if the test case is positive (`keyword_transcription` is a substring of the `test_transcription`), `False` otherwise (`similar` and `different` subsets). Note that each test case can be extended to QbyE KWS by reading the proper `keyword_id` file. Unfortunately, there is no easy way to do that in the loading script. All the test files are provided in 16 kHz, even though `{de,en,es,fr,it}.MCV` files are stored in the original sampling (usually 48 kHz) in the `data.tar.gz` archives. ## Dataset Creation The MOCKS testset was created from LibriSpeech and Mozilla Common Voice (MCV) datasets that are publicly available. To create it: - a [MFA](https://mfa-models.readthedocs.io/en/latest/acoustic/index.html) with publicly available models was used to extract word-level alignments, - an internally developed, rule-based grapheme-to-phoneme (G2P) algorithm was used to prepare phonetic transcriptions for each sample. The data is stored in a 16-bit, single-channel WAV format. 16kHz sampling rate is used for LibriSpeech based testset and 48kHz sampling rate for MCV based testset. The offline testset contains an additional 0.1 seconds at the beginning and end of the extracted audio sample to mitigate the cut-speech effect. The online version contains an additional 1 second or so at the beginning and end of the extracted audio sample. The MOCKS testset is gender balanced. ## Citation Information ```bibtex @inproceedings{pudo23_interspeech, author={Mikołaj Pudo and Mateusz Wosik and Adam Cieślak and Justyna Krzywdziak and Bożena Łukasiak and Artur Janicki}, title={{MOCKS} 1.0: Multilingual Open Custom Keyword Spotting Testset}, year={2023}, booktitle={Proc. Interspeech 2023}, } ```
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ywchoi/pubmed_abstract_0
2022-09-13T00:53:42.000Z
[ "region:us" ]
ywchoi
null
null
1
461
2022-09-13T00:52:06
Entry not found
15
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opus_ubuntu
2023-06-01T14:59:53.000Z
[ "task_categories:translation", "annotations_creators:crowdsourced", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "size_categories:n<1K", "source_datasets:original", "language:ace", "language:af", "language:ak", "language:am", "language:an", "language:ang", "language:ar", "language:ary", "language:as", "language:ast", "language:az", "language:ba", "language:bal", "language:be", "language:bem", "language:ber", "language:bg", "language:bho", "language:bn", "language:bo", "language:br", "language:brx", "language:bs", "language:bua", "language:byn", "language:ca", "language:ce", "language:ceb", "language:chr", "language:ckb", "language:co", "language:crh", "language:cs", "language:csb", "language:cv", "language:cy", "language:da", "language:de", "language:dsb", "language:dv", "language:dz", "language:el", "language:en", "language:eo", "language:es", "language:et", "language:eu", "language:fa", "language:ff", "language:fi", "language:fil", "language:fo", "language:fr", "language:frm", "language:frp", "language:fur", "language:fy", "language:ga", "language:gd", "language:gl", "language:gn", "language:grc", "language:gu", "language:guc", "language:gv", "language:ha", "language:haw", "language:he", "language:hi", "language:hil", "language:hne", "language:hr", "language:hsb", "language:ht", "language:hu", "language:hy", "language:ia", "language:id", "language:ig", "language:io", "language:is", "language:it", "language:iu", "language:ja", "language:jbo", "language:jv", "language:ka", "language:kab", "language:kg", "language:kk", "language:kl", "language:km", "language:kn", "language:ko", "language:kok", "language:ks", "language:ksh", "language:ku", "language:kw", "language:ky", "language:la", "language:lb", "language:lg", "language:li", "language:lij", "language:lld", "language:ln", "language:lo", "language:lt", "language:ltg", "language:lv", "language:mai", "language:mg", "language:mh", "language:mhr", "language:mi", "language:miq", "language:mk", "language:ml", "language:mn", "language:mr", "language:ms", "language:mt", "language:mus", "language:my", "language:nan", "language:nap", "language:nb", "language:nds", "language:ne", "language:nhn", "language:nl", "language:nn", "language:no", "language:nso", "language:ny", "language:oc", "language:om", "language:or", "language:os", "language:pa", "language:pam", "language:pap", "language:pl", "language:pms", "language:pmy", "language:ps", "language:pt", "language:qu", "language:rm", "language:ro", "language:rom", "language:ru", "language:rw", "language:sa", "language:sc", "language:sco", "language:sd", "language:se", "language:shn", "language:shs", "language:si", "language:sk", "language:sl", "language:sm", "language:sml", "language:sn", "language:so", "language:son", "language:sq", "language:sr", "language:st", "language:sv", "language:sw", "language:syr", "language:szl", "language:ta", "language:te", "language:tet", "language:tg", "language:th", "language:ti", "language:tk", "language:tl", "language:tlh", "language:tr", "language:trv", "language:ts", "language:tt", "language:ug", "language:uk", "language:ur", "language:uz", "language:ve", "language:vec", "language:vi", "language:wa", "language:wae", "language:wo", "language:xal", "language:xh", "language:yi", "language:yo", "language:zh", "language:zu", "language:zza", "license:bsd-3-clause", "region:us" ]
null
A parallel corpus of Ubuntu localization files. Source: https://translations.launchpad.net 244 languages, 23,988 bitexts total number of files: 30,959 total number of tokens: 29.84M total number of sentence fragments: 7.73M
@InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
1
460
2022-03-02T23:29:22
--- annotations_creators: - crowdsourced - expert-generated language_creators: - found language: - ace - af - ak - am - an - ang - ar - ary - as - ast - az - ba - bal - be - bem - ber - bg - bho - bn - bo - br - brx - bs - bua - byn - ca - ce - ceb - chr - ckb - co - crh - cs - csb - cv - cy - da - de - dsb - dv - dz - el - en - eo - es - et - eu - fa - ff - fi - fil - fo - fr - frm - frp - fur - fy - ga - gd - gl - gn - grc - gu - guc - gv - ha - haw - he - hi - hil - hne - hr - hsb - ht - hu - hy - ia - id - ig - io - is - it - iu - ja - jbo - jv - ka - kab - kg - kk - kl - km - kn - ko - kok - ks - ksh - ku - kw - ky - la - lb - lg - li - lij - lld - ln - lo - lt - ltg - lv - mai - mg - mh - mhr - mi - miq - mk - ml - mn - mr - ms - mt - mus - my - nan - nap - nb - nds - ne - nhn - nl - nn - 'no' - nso - ny - oc - om - or - os - pa - pam - pap - pl - pms - pmy - ps - pt - qu - rm - ro - rom - ru - rw - sa - sc - sco - sd - se - shn - shs - si - sk - sl - sm - sml - sn - so - son - sq - sr - st - sv - sw - syr - szl - ta - te - tet - tg - th - ti - tk - tl - tlh - tr - trv - ts - tt - ug - uk - ur - uz - ve - vec - vi - wa - wae - wo - xal - xh - yi - yo - zh - zu - zza language_bcp47: - ar-SY - bn-IN - de-AT - de-DE - en-AU - en-CA - en-GB - en-NZ - en-US - es-AR - es-CL - es-CO - es-CR - es-DO - es-EC - es-ES - es-GT - es-HN - es-MX - es-NI - es-PA - es-PE - es-PR - es-SV - es-UY - es-VE - fa-AF - fr-CA - fr-FR - nl-NL - pt-BR - pt-PT - ta-LK - zh-CN - zh-HK - zh-TW license: - bsd-3-clause multilinguality: - multilingual size_categories: - 10K<n<100K - 1K<n<10K - n<1K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: Opus Ubuntu dataset_info: - config_name: as-bs features: - name: id dtype: string - name: translation dtype: translation: languages: - as - bs splits: - name: train num_bytes: 1037811 num_examples: 8583 download_size: 229723 dataset_size: 1037811 - config_name: az-cs features: - name: id dtype: string - name: translation dtype: translation: languages: - az - cs splits: - name: train num_bytes: 17821 num_examples: 293 download_size: 9501 dataset_size: 17821 - config_name: bg-de features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - de splits: - name: train num_bytes: 27627 num_examples: 184 download_size: 9994 dataset_size: 27627 - config_name: br-es_PR features: - name: id dtype: string - name: translation dtype: translation: languages: - br - es_PR splits: - name: train num_bytes: 8875 num_examples: 125 download_size: 5494 dataset_size: 8875 - config_name: bn-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - bn - ga splits: - name: train num_bytes: 584629 num_examples: 7324 download_size: 142710 dataset_size: 584629 - config_name: br-hi features: - name: id dtype: string - name: translation dtype: translation: languages: - br - hi splits: - name: train num_bytes: 1300081 num_examples: 15551 download_size: 325415 dataset_size: 1300081 - config_name: br-la features: - name: id dtype: string - name: translation dtype: translation: languages: - br - la splits: - name: train num_bytes: 29341 num_examples: 527 download_size: 11565 dataset_size: 29341 - config_name: bs-szl features: - name: id dtype: string - name: translation dtype: translation: languages: - bs - szl splits: - name: train num_bytes: 41116 num_examples: 646 download_size: 18134 dataset_size: 41116 - config_name: br-uz features: - name: id dtype: string - name: translation dtype: translation: languages: - br - uz splits: - name: train num_bytes: 110278 num_examples: 1416 download_size: 33595 dataset_size: 110278 - config_name: br-yi features: - name: id dtype: string - name: translation dtype: translation: languages: - br - yi splits: - name: train num_bytes: 172846 num_examples: 2799 download_size: 41956 dataset_size: 172846 config_names: - as-bs - az-cs - bg-de - bn-ga - br-es_PR - br-hi - br-la - br-uz - br-yi - bs-szl --- # Dataset Card for Opus Ubuntu ## 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:** http://opus.nlpl.eu/Ubuntu.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary These are translations of the Ubuntu software package messages, donated by the Ubuntu community. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Ubuntu.php E.g. `dataset = load_dataset("opus_ubuntu", lang1="it", lang2="pl")` ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances Example instance: ``` { 'id': '0', 'translation': { 'it': 'Comprende Gmail, Google Docs, Google+, YouTube e Picasa', 'pl': 'Zawiera Gmail, Google Docs, Google+, YouTube oraz Picasa' } } ``` ### Data Fields Each instance has two fields: - **id**: the id of the example - **translation**: a dictionary containing translated texts in two languages. ### Data Splits Each subset simply consists in a train set. We provide the number of examples for certain language pairs: | | train | |:---------|--------:| | as-bs | 8583 | | az-cs | 293 | | bg-de | 184 | | br-es_PR | 125 | | bn-ga | 7324 | | br-hi | 15551 | | br-la | 527 | | bs-szl | 646 | | br-uz | 1416 | | br-yi | 2799 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 BSD "Revised" license (see (https://help.launchpad.net/Legal#Translations_copyright)[https://help.launchpad.net/Legal#Translations_copyright]) ### Citation Information ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
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opus_dgt
2023-06-01T14:59:53.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1M<n<10M", "source_datasets:original", "language:bg", "language:cs", "language:da", "language:de", "language:el", "language:en", "language:es", "language:et", "language:fi", "language:fr", "language:ga", "language:hr", "language:hu", "language:it", "language:lt", "language:lv", "language:mt", "language:nl", "language:pl", "language:pt", "language:ro", "language:sh", "language:sk", "language:sl", "language:sv", "license:unknown", "region:us" ]
null
A collection of translation memories provided by the JRC. Source: https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory 25 languages, 299 bitexts total number of files: 817,410 total number of tokens: 2.13G total number of sentence fragments: 113.52M
@InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
1
458
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sh - sk - sl - sv license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K - 1M<n<10M source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusDgt dataset_info: - config_name: bg-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - ga splits: - name: train num_bytes: 82972428 num_examples: 179142 download_size: 15935979 dataset_size: 82972428 - config_name: bg-hr features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - hr splits: - name: train num_bytes: 239828651 num_examples: 701572 download_size: 46804111 dataset_size: 239828651 - config_name: bg-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - bg - sh splits: - name: train num_bytes: 498884905 num_examples: 1488507 download_size: 97402723 dataset_size: 498884905 - config_name: fi-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - fi - ga splits: - name: train num_bytes: 61313136 num_examples: 178619 download_size: 14385114 dataset_size: 61313136 - config_name: es-ga features: - name: id dtype: string - name: translation dtype: translation: languages: - es - ga splits: - name: train num_bytes: 63115666 num_examples: 178696 download_size: 14447359 dataset_size: 63115666 - config_name: ga-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - ga - sh splits: - name: train num_bytes: 28666585 num_examples: 91613 download_size: 6963357 dataset_size: 28666585 - config_name: hr-sk features: - name: id dtype: string - name: translation dtype: translation: languages: - hr - sk splits: - name: train num_bytes: 170718371 num_examples: 689263 download_size: 42579941 dataset_size: 170718371 - config_name: mt-sh features: - name: id dtype: string - name: translation dtype: translation: languages: - mt - sh splits: - name: train num_bytes: 368562443 num_examples: 1450424 download_size: 88598048 dataset_size: 368562443 - config_name: hr-sv features: - name: id dtype: string - name: translation dtype: translation: languages: - hr - sv splits: - name: train num_bytes: 171858392 num_examples: 696334 download_size: 41410203 dataset_size: 171858392 - config_name: ga-nl features: - name: id dtype: string - name: translation dtype: translation: languages: - ga - nl splits: - name: train num_bytes: 59065574 num_examples: 170644 download_size: 13730934 dataset_size: 59065574 config_names: - bg-ga - bg-hr - bg-sh - es-ga - fi-ga - ga-nl - ga-sh - hr-sk - hr-sv - mt-sh --- # Dataset Card for OpusDgt ## 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:** http://opus.nlpl.eu/DGT.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary A collection of translation memories provided by the Joint Research Centre (JRC) Directorate-General for Translation (DGT): https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory Tha dataset contains 25 languages and 299 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. ```python dataset = load_dataset("opus_dgt", lang1="it", lang2="pl") ``` You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/DGT.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - bg - cs - da - de - el - en - es - et - fi - fr - ga - hr - hu - it - lt - lv - mt - nl - pl - pt - ro - sh - sk - sl - sv ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { "bg": "Протокол за поправка на Конвенцията относно компетентността, признаването и изпълнението на съдебни решения по граждански и търговски дела, подписана в Лугано на 30 октомври 2007 г.", "ga": "Miontuairisc cheartaitheach maidir le Coinbhinsiún ar dhlínse agus ar aithint agus ar fhorghníomhú breithiúnas in ábhair shibhialta agus tráchtála, a siníodh in Lugano an 30 Deireadh Fómhair 2007" } } ``` ### Data Fields - `id` (`str`): Unique identifier of the parallel sentence for the pair of languages. - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single `train` split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
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open-source-metrics/model-repos-stats
2023-07-03T01:35:17.000Z
[ "region:us" ]
open-source-metrics
null
null
5
458
2022-09-26T15:54:28
--- dataset_info: features: - name: 'Unnamed: 0' dtype: int64 - name: repo_id dtype: string - name: author dtype: string - name: model_type dtype: string - name: files_per_repo dtype: int64 - name: downloads_30d dtype: int64 - name: library dtype: string - name: likes dtype: int64 - name: pipeline dtype: string - name: pytorch dtype: bool - name: tensorflow dtype: bool - name: jax dtype: bool - name: license dtype: string - name: languages dtype: string - name: datasets dtype: string - name: co2 dtype: string - name: prs_count dtype: int64 - name: prs_open dtype: int64 - name: prs_merged dtype: int64 - name: prs_closed dtype: int64 - name: discussions_count dtype: int64 - name: discussions_open dtype: int64 - name: discussions_closed dtype: int64 - name: tags dtype: string - name: has_model_index dtype: bool - name: has_metadata dtype: bool - name: has_text dtype: bool - name: text_length dtype: int64 splits: - name: train num_bytes: 68539081 num_examples: 245197 download_size: 14926618 dataset_size: 68539081 --- # Dataset Card for "model-repos-stats" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
1,386
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Fazzie/Teyvat
2022-12-13T02:09:42.000Z
[ "task_categories:text-to-image", "annotations_creators:no-annotation", "language_creators:found", "source_datasets:original", "language:en", "license:unknown", "region:us" ]
Fazzie
Teyvat is the first small-scale text-to-image prompt dataset for Genshin impact.
null
18
458
2022-11-16T03:47:33
--- annotations_creators: - no-annotation language_creators: - found language: - en license: - unknown source_datasets: - original task_categories: - text-to-image dataset_info: features: - name: image dtype: image - name: text dtype: string splits: - name: train num_bytes: 71202 num_examples: 234 download_size: 466995417 dataset_size: 71202 --- # Dataset Card for Teyvat BLIP captions Dataset used to train [Teyvat characters text to image model](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion). BLIP generated captions for characters images from [genshin-impact fandom wiki](https://genshin-impact.fandom.com/wiki/Character#Playable_Characters)and [biligame wiki for genshin impact](https://wiki.biligame.com/ys/%E8%A7%92%E8%89%B2). For each row the dataset contains `image` and `text` keys. `image` is a varying size PIL png, and `text` is the accompanying text caption. Only a train split is provided. The `text` include the tag `Teyvat`, `Name`,`Element`, `Weapon`, `Region`, `Model type`, and `Description`, the `Description` is captioned with the [pre-trained BLIP model](https://github.com/salesforce/BLIP). ## Examples <img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Ganyu_001.png" title = "Ganyu_001.png" style="max-width: 20%;" > > Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes <img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Ganyu_002.png" title = "Ganyu_002.png" style="max-width: 20%;" > > Teyvat, Name:Ganyu, Element:Cryo, Weapon:Bow, Region:Liyue, Model type:Medium Female, Description:an anime character with blue hair and blue eyes <img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Keqing_003.png" title = "Keqing_003.png" style="max-width: 20%;" > > Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:a anime girl with long white hair and blue eyes <img src = "https://huggingface.co/datasets/Fazzie/Teyvat/resolve/main/data/Keqing_004.png" title = "Keqing_004.png" style="max-width: 20%;" > > Teyvat, Name:Keqing, Element:Electro, Weapon:Sword, Region:Liyue, Model type:Medium Female, Description:an anime character wearing a purple dress and cat ears
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miam
2023-06-01T14:59:51.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_categories:text-classification", "task_ids:dialogue-modeling", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:10K<n<100K", "source_datasets:original", "language:de", "language:en", "language:es", "language:fr", "language:it", "license:cc-by-sa-4.0", "dialogue-act-classification", "region:us" ]
null
Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. Datasets are in English, French, German, Italian and Spanish. They cover a variety of domains including spontaneous speech, scripted scenarios, and joint task completion. Some datasets additionally include emotion and/or sentimant labels.
@unpublished{ anonymous2021cross-lingual, title={Cross-Lingual Pretraining Methods for Spoken Dialog}, author={Anonymous}, journal={OpenReview Preprint}, year={2021}, url{https://openreview.net/forum?id=c1oDhu_hagR}, note={anonymous preprint under review} }
3
456
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - expert-generated language: - de - en - es - fr - it license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - text-generation - fill-mask - text-classification task_ids: - dialogue-modeling - language-modeling - masked-language-modeling pretty_name: MIAM tags: - dialogue-act-classification dataset_info: - config_name: dihana features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: File_ID dtype: string - name: Label dtype: class_label: names: '0': Afirmacion '1': Apertura '2': Cierre '3': Confirmacion '4': Espera '5': Indefinida '6': Negacion '7': No_entendido '8': Nueva_consulta '9': Pregunta '10': Respuesta - name: Idx dtype: int32 splits: - name: train num_bytes: 1946735 num_examples: 19063 - name: validation num_bytes: 216498 num_examples: 2123 - name: test num_bytes: 238446 num_examples: 2361 download_size: 1777267 dataset_size: 2401679 - config_name: ilisten features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': AGREE '1': ANSWER '2': CLOSING '3': ENCOURAGE-SORRY '4': GENERIC-ANSWER '5': INFO-REQUEST '6': KIND-ATTITUDE_SMALL-TALK '7': OFFER-GIVE-INFO '8': OPENING '9': PERSUASION-SUGGEST '10': QUESTION '11': REJECT '12': SOLICITATION-REQ_CLARIFICATION '13': STATEMENT '14': TALK-ABOUT-SELF - name: Idx dtype: int32 splits: - name: train num_bytes: 244336 num_examples: 1986 - name: validation num_bytes: 33988 num_examples: 230 - name: test num_bytes: 145376 num_examples: 971 download_size: 349993 dataset_size: 423700 - config_name: loria features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: File_ID dtype: string - name: Label dtype: class_label: names: '0': ack '1': ask '2': find_mold '3': find_plans '4': first_step '5': greet '6': help '7': inform '8': inform_engine '9': inform_job '10': inform_material_space '11': informer_conditioner '12': informer_decoration '13': informer_elcomps '14': informer_end_manufacturing '15': kindAtt '16': manufacturing_reqs '17': next_step '18': 'no' '19': other '20': quality_control '21': quit '22': reqRep '23': security_policies '24': staff_enterprise '25': staff_job '26': studies_enterprise '27': studies_job '28': todo_failure '29': todo_irreparable '30': 'yes' - name: Idx dtype: int32 splits: - name: train num_bytes: 1208730 num_examples: 8465 - name: validation num_bytes: 133829 num_examples: 942 - name: test num_bytes: 149855 num_examples: 1047 download_size: 1221132 dataset_size: 1492414 - config_name: maptask features: - name: Speaker dtype: string - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Dialogue_ID dtype: string - name: File_ID dtype: string - name: Label dtype: class_label: names: '0': acknowledge '1': align '2': check '3': clarify '4': explain '5': instruct '6': query_w '7': query_yn '8': ready '9': reply_n '10': reply_w '11': reply_y - name: Idx dtype: int32 splits: - name: train num_bytes: 1910120 num_examples: 25382 - name: validation num_bytes: 389879 num_examples: 5221 - name: test num_bytes: 396947 num_examples: 5335 download_size: 1729021 dataset_size: 2696946 - config_name: vm2 features: - name: Utterance dtype: string - name: Dialogue_Act dtype: string - name: Speaker dtype: string - name: Dialogue_ID dtype: string - name: Label dtype: class_label: names: '0': ACCEPT '1': BACKCHANNEL '2': BYE '3': CLARIFY '4': CLOSE '5': COMMIT '6': CONFIRM '7': DEFER '8': DELIBERATE '9': DEVIATE_SCENARIO '10': EXCLUDE '11': EXPLAINED_REJECT '12': FEEDBACK '13': FEEDBACK_NEGATIVE '14': FEEDBACK_POSITIVE '15': GIVE_REASON '16': GREET '17': INFORM '18': INIT '19': INTRODUCE '20': NOT_CLASSIFIABLE '21': OFFER '22': POLITENESS_FORMULA '23': REJECT '24': REQUEST '25': REQUEST_CLARIFY '26': REQUEST_COMMENT '27': REQUEST_COMMIT '28': REQUEST_SUGGEST '29': SUGGEST '30': THANK - name: Idx dtype: int32 splits: - name: train num_bytes: 1869254 num_examples: 25060 - name: validation num_bytes: 209390 num_examples: 2860 - name: test num_bytes: 209032 num_examples: 2855 download_size: 1641453 dataset_size: 2287676 config_names: - dihana - ilisten - loria - maptask - vm2 --- # Dataset Card for MIAM ## 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:** [N/A] - **Repository:** [N/A] - **Paper:** [N/A] - **Leaderboard:** [N/A] - **Point of Contact:** [N/A] ### Dataset Summary Multilingual dIalogAct benchMark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. Datasets are in English, French, German, Italian and Spanish. They cover a variety of domains including spontaneous speech, scripted scenarios, and joint task completion. All datasets contain dialogue act labels. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages English, French, German, Italian, Spanish. ## Dataset Structure ### Data Instances #### Dihana Corpus For the `dihana` configuration one example from the dataset is: ``` { 'Speaker': 'U', 'Utterance': 'Hola , quería obtener el horario para ir a Valencia', 'Dialogue_Act': 9, # 'Pregunta' ('Request') 'Dialogue_ID': '0', 'File_ID': 'B209_BA5c3', } ``` #### iLISTEN Corpus For the `ilisten` configuration one example from the dataset is: ``` { 'Speaker': 'T_11_U11', 'Utterance': 'ok, grazie per le informazioni', 'Dialogue_Act': 6, # 'KIND-ATTITUDE_SMALL-TALK' 'Dialogue_ID': '0', } ``` #### LORIA Corpus For the `loria` configuration one example from the dataset is: ``` { 'Speaker': 'Samir', 'Utterance': 'Merci de votre visite, bonne chance, et à la prochaine !', 'Dialogue_Act': 21, # 'quit' 'Dialogue_ID': '5', 'File_ID': 'Dial_20111128_113927', } ``` #### HCRC MapTask Corpus For the `maptask` configuration one example from the dataset is: ``` { 'Speaker': 'f', 'Utterance': 'is it underneath the rope bridge or to the left', 'Dialogue_Act': 6, # 'query_w' 'Dialogue_ID': '0', 'File_ID': 'q4ec1', } ``` #### VERBMOBIL For the `vm2` configuration one example from the dataset is: ``` { 'Utterance': 'ja was sind viereinhalb Stunden Bahngerüttel gegen siebzig Minuten Turbulenzen im Flugzeug', 'Utterance': 'Utterance', 'Dialogue_Act': 'Dialogue_Act', # 'INFORM' 'Speaker': 'A', 'Dialogue_ID': '66', } ``` ### Data Fields For the `dihana` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'Afirmacion' (0) [Feedback_positive], 'Apertura' (1) [Opening], 'Cierre' (2) [Closing], 'Confirmacion' (3) [Acknowledge], 'Espera' (4) [Hold], 'Indefinida' (5) [Undefined], 'Negacion' (6) [Feedback_negative], 'No_entendido' (7) [Request_clarify], 'Nueva_consulta' (8) [New_request], 'Pregunta' (9) [Request] or 'Respuesta' (10) [Reply]. - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `ilisten` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'AGREE' (0), 'ANSWER' (1), 'CLOSING' (2), 'ENCOURAGE-SORRY' (3), 'GENERIC-ANSWER' (4), 'INFO-REQUEST' (5), 'KIND-ATTITUDE_SMALL-TALK' (6), 'OFFER-GIVE-INFO' (7), 'OPENING' (8), 'PERSUASION-SUGGEST' (9), 'QUESTION' (10), 'REJECT' (11), 'SOLICITATION-REQ_CLARIFICATION' (12), 'STATEMENT' (13) or 'TALK-ABOUT-SELF' (14). - `Dialogue_ID`: identifier of the dialogue as a string. For the `loria` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'ack' (0), 'ask' (1), 'find_mold' (2), 'find_plans' (3), 'first_step' (4), 'greet' (5), 'help' (6), 'inform' (7), 'inform_engine' (8), 'inform_job' (9), 'inform_material_space' (10), 'informer_conditioner' (11), 'informer_decoration' (12), 'informer_elcomps' (13), 'informer_end_manufacturing' (14), 'kindAtt' (15), 'manufacturing_reqs' (16), 'next_step' (17), 'no' (18), 'other' (19), 'quality_control' (20), 'quit' (21), 'reqRep' (22), 'security_policies' (23), 'staff_enterprise' (24), 'staff_job' (25), 'studies_enterprise' (26), 'studies_job' (27), 'todo_failure' (28), 'todo_irreparable' (29), 'yes' (30) - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `maptask` configuration, the different fields are: - `Speaker`: identifier of the speaker as a string. - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialog act label of the utterance. It can be one of 'acknowledge' (0), 'align' (1), 'check' (2), 'clarify' (3), 'explain' (4), 'instruct' (5), 'query_w' (6), 'query_yn' (7), 'ready' (8), 'reply_n' (9), 'reply_w' (10) or 'reply_y' (11). - `Dialogue_ID`: identifier of the dialogue as a string. - `File_ID`: identifier of the source file as a string. For the `vm2` configuration, the different fields are: - `Utterance`: Utterance as a string. - `Dialogue_Act`: Dialogue act label of the utterance. It can be one of 'ACCEPT' (0), 'BACKCHANNEL' (1), 'BYE' (2), 'CLARIFY' (3), 'CLOSE' (4), 'COMMIT' (5), 'CONFIRM' (6), 'DEFER' (7), 'DELIBERATE' (8), 'DEVIATE_SCENARIO' (9), 'EXCLUDE' (10), 'EXPLAINED_REJECT' (11), 'FEEDBACK' (12), 'FEEDBACK_NEGATIVE' (13), 'FEEDBACK_POSITIVE' (14), 'GIVE_REASON' (15), 'GREET' (16), 'INFORM' (17), 'INIT' (18), 'INTRODUCE' (19), 'NOT_CLASSIFIABLE' (20), 'OFFER' (21), 'POLITENESS_FORMULA' (22), 'REJECT' (23), 'REQUEST' (24), 'REQUEST_CLARIFY' (25), 'REQUEST_COMMENT' (26), 'REQUEST_COMMIT' (27), 'REQUEST_SUGGEST' (28), 'SUGGEST' (29), 'THANK' (30). - `Speaker`: Speaker as a string. - `Dialogue_ID`: identifier of the dialogue as a string. ### Data Splits | Dataset name | Train | Valid | Test | | ------------ | ----- | ----- | ---- | | dihana | 19063 | 2123 | 2361 | | ilisten | 1986 | 230 | 971 | | loria | 8465 | 942 | 1047 | | maptask | 25382 | 5221 | 5335 | | vm2 | 25060 | 2860 | 2855 | ## 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 Anonymous. ### Licensing Information This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/). ### Citation Information ``` @inproceedings{colombo-etal-2021-code, title = "Code-switched inspired losses for spoken dialog representations", author = "Colombo, Pierre and Chapuis, Emile and Labeau, Matthieu and Clavel, Chlo{\'e}", booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2021", address = "Online and Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.emnlp-main.656", doi = "10.18653/v1/2021.emnlp-main.656", pages = "8320--8337", abstract = "Spoken dialogue systems need to be able to handle both multiple languages and multilinguality inside a conversation (\textit{e.g} in case of code-switching). In this work, we introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations. The goal of these losses is to expose the model to code-switched language. In order to scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens. We test the generic representations on MIAM, a new benchmark composed of five dialogue act corpora on the same aforementioned languages as well as on two novel multilingual tasks (\textit{i.e} multilingual mask utterance retrieval and multilingual inconsistency identification). Our experiments show that our new losses achieve a better performance in both monolingual and multilingual settings.", } ``` ### Contributions Thanks to [@eusip](https://github.com/eusip) and [@PierreColombo](https://github.com/PierreColombo) for adding this dataset.
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potsawee/wiki_bio_gpt3_hallucination
2023-05-29T23:14:09.000Z
[ "task_categories:text-classification", "size_categories:n<1K", "language:en", "license:cc-by-sa-3.0", "arxiv:2303.08896", "region:us" ]
potsawee
null
null
9
455
2023-03-18T18:05:21
--- license: cc-by-sa-3.0 task_categories: - text-classification language: - en size_categories: - n<1K dataset_info: features: - name: gpt3_text dtype: string - name: wiki_bio_text dtype: string - name: gpt3_sentences sequence: string - name: annotation sequence: string - name: wiki_bio_test_idx dtype: int64 - name: gpt3_text_samples sequence: string splits: - name: evaluation num_bytes: 5042581 num_examples: 238 download_size: 2561507 dataset_size: 5042581 --- # Dataset Card for WikiBio GPT-3 Hallucination Dataset - GitHub repository: https://github.com/potsawee/selfcheckgpt - Paper: [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896) ### Dataset Summary - We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt: ```This is a Wikipedia passage about {concept}``` where `concept` represents an individual from the WikiBio dataset. - We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate. - We report the data statistics, annotation process, and inter-annotator agreement in our paper. ## Update - v3 (5 May 2023): 238 test IDs have been annotated in total. - v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset. - v1 (15 March 2023): 65 test IDs -- here is `wiki_bio_test_idx` of the documents in v1 [[Link]](https://drive.google.com/file/d/1N3_ZQmr9yBbsOP2JCpgiea9oiNIu78Xw/view?usp=sharing) ## Dataset Structure Each instance consists of: - `gpt3_text`: GPT-3 generated passage - `wiki_bio_text`: Actual Wikipedia passage (first paragraph) - `gpt3_sentences`: `gpt3_text` split into sentences using `spacy` - `annotation`: human annotation at the sentence level - `wiki_bio_test_idx`: ID of the concept/individual from the original wikibio dataset (testset) - `gpt3_text_samples`: list of 20 sampled passages (do_sample = True & temperature = 1.0) ### Citation Information ``` @misc{manakul2023selfcheckgpt, title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models}, author={Potsawee Manakul and Adian Liusie and Mark J. F. Gales}, year={2023}, eprint={2303.08896}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
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zxvix/squad_text_new
2023-10-23T08:59:56.000Z
[ "region:us" ]
zxvix
null
null
0
454
2023-10-20T12:37:52
--- configs: - config_name: default data_files: - split: annotated path: data/annotated-* - split: augmented path: data/augmented-* - split: augmented_2 path: data/augmented_2-* dataset_info: features: - name: text dtype: string - name: original_text dtype: string splits: - name: annotated num_bytes: 3302478 num_examples: 2044 - name: augmented num_bytes: 3294934 num_examples: 2053 - name: augmented_2 num_bytes: 3274276.7295597484 num_examples: 2054 download_size: 4206194 dataset_size: 9871688.72955975 --- # Dataset Card for "squad_text_new" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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shibing624/AdvertiseGen
2023-05-12T07:25:00.000Z
[ "task_categories:text-generation", "language:zh", "license:cc-by-4.0", "text-generation", "e-commerce advertise", "region:us" ]
shibing624
null
null
15
453
2023-03-28T02:42:56
--- license: cc-by-4.0 language: - zh tags: - text-generation - e-commerce advertise pretty_name: AdvertiseGen task_categories: - text-generation --- # Dataset Card for AdvertiseGen - **formal url:** https://www.luge.ai/#/luge/dataDetail?id=9 ## Dataset Description 数据集介绍 AdvertiseGen是电商广告文案生成数据集。 AdvertiseGen以商品网页的标签与文案的信息对应关系为基础构造，是典型的开放式生成任务，在模型基于key-value输入生成开放式文案时，与输入信息的事实一致性需要得到重点关注。 - 任务描述：给定商品信息的关键词和属性列表kv-list，生成适合该商品的广告文案adv； - 数据规模：训练集114k，验证集1k，测试集3k； - 数据来源：清华大学CoAI小组； ### Supported Tasks and Leaderboards The dataset designed for generate e-commerce advertise. ### Languages The data in AdvertiseGen are in Chinese. ## Dataset Structure ### Data Instances An example of "train" looks as follows: ```json { "content": "类型#上衣*材质#牛仔布*颜色#白色*风格#简约*图案#刺绣*衣样式#外套*衣款式#破洞", "summary": "简约而不简单的牛仔外套，白色的衣身十分百搭。衣身多处有做旧破洞设计，打破单调乏味，增加一丝造型看点。衣身后背处有趣味刺绣装饰，丰富层次感，彰显别样时尚。" } ``` ### Citation Information 数据集引用 如在学术论文中使用本数据集，请添加相关引用说明，具体如下： ``` Shao, Zhihong, et al. "Long and Diverse Text Generation with Planning-based Hierarchical Variational Model." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019. ```
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pvduy/arena_synth
2023-08-02T16:02:03.000Z
[ "region:us" ]
pvduy
null
null
0
453
2023-08-02T16:01:59
--- dataset_info: features: - name: prompt dtype: string - name: selected dtype: string - name: rejected dtype: string splits: - name: train num_bytes: 53190421 num_examples: 29851 - name: test num_bytes: 14269380 num_examples: 8000 download_size: 36514341 dataset_size: 67459801 --- # Dataset Card for "arena_synth" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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shunk031/MSCOCO
2023-10-30T14:06:39.000Z
[ "task_categories:image-segmentation", "task_categories:object-detection", "task_categories:other", "task_ids:instance-segmentation", "task_ids:semantic-segmentation", "task_ids:panoptic-segmentation", "annotations_creators:crowdsourced", "language_creators:found", "multilinguality:monolingual", "source_datasets:original", "language:en", "license:cc-by-4.0", "image-captioning", "object-detection", "keypoint-detection", "stuff-segmentation", "panoptic-segmentation", "arxiv:1405.0312", "region:us" ]
shunk031
0
453
2023-09-09T08:15:05
--- annotations_creators: - crowdsourced language: - en language_creators: - found license: - cc-by-4.0 multilinguality: - monolingual pretty_name: MSCOCO size_categories: [] source_datasets: - original tags: - image-captioning - object-detection - keypoint-detection - stuff-segmentation - panoptic-segmentation task_categories: - image-segmentation - object-detection - other task_ids: - instance-segmentation - semantic-segmentation - panoptic-segmentation --- # Dataset Card for MSCOCO [![CI](https://github.com/shunk031/huggingface-datasets_MSCOCO/actions/workflows/ci.yaml/badge.svg)](https://github.com/shunk031/huggingface-datasets_MSCOCO/actions/workflows/ci.yaml) ## Table of Contents - [Dataset Card Creation Guide](#dataset-card-creation-guide) - [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) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://cocodataset.org/#home - **Repository:** https://github.com/shunk031/huggingface-datasets_MSCOCO - **Paper (Preprint):** https://arxiv.org/abs/1405.0312 - **Paper (ECCV2014):** https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48 - **Leaderboard (Detection):** https://cocodataset.org/#detection-leaderboard - **Leaderboard (Keypoint):** https://cocodataset.org/#keypoints-leaderboard - **Leaderboard (Stuff):** https://cocodataset.org/#stuff-leaderboard - **Leaderboard (Panoptic):** https://cocodataset.org/#panoptic-leaderboard - **Leaderboard (Captioning):** https://cocodataset.org/#captions-leaderboard - **Point of Contact:** info@cocodataset.org ### Dataset Summary > COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: > - Object segmentation > - Recognition in context > - Superpixel stuff segmentation > - 330K images (>200K labeled) > - 1.5 million object instances > - 80 object categories > - 91 stuff categories > - 5 captions per image > - 250,000 people with keypoints ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances #### 2014 - captioning dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2014, coco_task="captions", ) ``` - instances dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2014, coco_task="instances", decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask. ) ``` - person keypoints dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2014, coco_task="person_keypoints", decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask. ) ``` #### 2017 - captioning dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2017, coco_task="captions", ) ``` - instances dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2017, coco_task="instances", decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask. ) ``` - person keypoints dataset ```python import datasets as ds dataset = ds.load_dataset( "shunk031/MSCOCO", year=2017, coco_task="person_keypoints", decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask. ) ``` ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 > The annotations in this dataset along with this website belong to the COCO Consortium and are licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode). > > ## Images > The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset. > > ## Software > Copyright (c) 2015, COCO Consortium. All rights reserved. Redistribution and use software in source and binary form, with or without modification, are permitted provided that the following conditions are met: > - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. > - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. > - Neither the name of the COCO Consortium nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. > > THIS SOFTWARE AND ANNOTATIONS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ### Citation Information ```bibtex @inproceedings{lin2014microsoft, title={Microsoft coco: Common objects in context}, author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence}, booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13}, pages={740--755}, year={2014}, organization={Springer} } ``` ### Contributions Thanks to [COCO Consortium](https://cocodataset.org/#people) for creating this dataset.
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mdd
2023-06-01T14:59:51.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:dialogue-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:1M<n<10M", "source_datasets:original", "language:en", "license:cc-by-3.0", "arxiv:1511.06931", "region:us" ]
null
The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion).
@misc{dodge2016evaluating, title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston}, year={2016}, eprint={1511.06931}, archivePrefix={arXiv}, primaryClass={cs.CL} }
3
452
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - en license: - cc-by-3.0 multilinguality: - monolingual size_categories: - 100K<n<1M - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - dialogue-modeling paperswithcode_id: mdd pretty_name: Movie Dialog dataset (MDD) dataset_info: - config_name: task1_qa features: - name: dialogue_turns sequence: - name: speaker dtype: int32 - name: utterance dtype: string splits: - name: train num_bytes: 8621120 num_examples: 96185 - name: test num_bytes: 894590 num_examples: 9952 - name: validation num_bytes: 892540 num_examples: 9968 download_size: 135614957 dataset_size: 10408250 - config_name: task2_recs features: - name: dialogue_turns sequence: - name: speaker dtype: int32 - name: utterance dtype: string splits: - name: train num_bytes: 205936579 num_examples: 1000000 - name: test num_bytes: 2064509 num_examples: 10000 - name: validation num_bytes: 2057290 num_examples: 10000 download_size: 135614957 dataset_size: 210058378 - config_name: task3_qarecs features: - name: dialogue_turns sequence: - name: speaker dtype: int32 - name: utterance dtype: string splits: - name: train num_bytes: 356789364 num_examples: 952125 - name: test num_bytes: 1730291 num_examples: 4915 - name: validation num_bytes: 1776506 num_examples: 5052 download_size: 135614957 dataset_size: 360296161 - config_name: task4_reddit features: - name: dialogue_turns sequence: - name: speaker dtype: int32 - name: utterance dtype: string splits: - name: train num_bytes: 497864160 num_examples: 945198 - name: test num_bytes: 5220295 num_examples: 10000 - name: validation num_bytes: 5372702 num_examples: 10000 - name: cand_valid num_bytes: 1521633 num_examples: 10000 - name: cand_test num_bytes: 1567235 num_examples: 10000 download_size: 192209920 dataset_size: 511546025 config_names: - task1_qa - task2_recs - task3_qarecs - task4_reddit --- # Dataset Card for MDD ## 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:**[The bAbI project](https://research.fb.com/downloads/babi/) - **Repository:** - **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.06931.pdf) - **Leaderboard:** - **Point of Contact:** ### Dataset Summary The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The data is present in English language as written by users on OMDb and MovieLens websites. ## Dataset Structure ### Data Instances An instance from the `task3_qarecs` config's `train` split: ``` {'dialogue_turns': {'speaker': [0, 1, 0, 1, 0, 1], 'utterance': ["I really like Jaws, Bottle Rocket, Saving Private Ryan, Tommy Boy, The Muppet Movie, Face/Off, and Cool Hand Luke. I'm looking for a Documentary movie.", 'Beyond the Mat', 'Who is that directed by?', 'Barry W. Blaustein', 'I like Jon Fauer movies more. Do you know anything else?', 'Cinematographer Style']}} ``` An instance from the `task4_reddit` config's `cand-valid` split: ``` {'dialogue_turns': {'speaker': [0], 'utterance': ['MORTAL KOMBAT !']}} ``` ### Data Fields For all configurations: - `dialogue_turns`: a dictionary feature containing: - `speaker`: an integer with possible values including `0`, `1`, indicating which speaker wrote the utterance. - `utterance`: a `string` feature containing the text utterance. ### Data Splits The splits and corresponding sizes are: |config |train |test |validation|cand_valid|cand_test| |:--|------:|----:|---------:|----:|----:| |task1_qa|96185|9952|9968|-|-| |task2_recs|1000000|10000|10000|-|-| |task3_qarecs|952125|4915|5052|-|-| |task4_reddit|945198|10000|10000|10000|10000| The `cand_valid` and `cand_test` are negative candidates for the `task4_reddit` configuration which is used in ranking true positive against these candidates and hits@k (or another ranking metric) is reported. (See paper) ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization The construction of the tasks depended on some existing datasets: 1) MovieLens. The data was downloaded from: http://grouplens.org/datasets/movielens/20m/ on May 27th, 2015. 2) OMDB. The data was downloaded from: http://beforethecode.com/projects/omdb/download.aspx on May 28th, 2015. 3) For `task4_reddit`, the data is a processed subset (movie subreddit only) of the data available at: https://www.reddit.com/r/datasets/comments/3bxlg7 #### Who are the source language producers? Users on MovieLens, OMDB website and reddit websites, among others. ### 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 Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston (at Facebook Research). ### Licensing Information ``` Creative Commons Attribution 3.0 License ``` ### Citation Information ``` @misc{dodge2016evaluating, title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems}, author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston}, year={2016}, eprint={1511.06931}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset.
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Dahoas/prompted_hf_cot_gsm8k
2023-10-16T10:36:06.000Z
[ "region:us" ]
Dahoas
null
null
0
449
2023-10-12T10:20:39
--- dataset_info: features: - name: question dtype: string - name: answer dtype: string - name: prompt dtype: string - name: response dtype: string splits: - name: train num_bytes: 17216169 num_examples: 7217 - name: test num_bytes: 3184819 num_examples: 1319 - name: val num_bytes: 613398 num_examples: 256 download_size: 10146546 dataset_size: 21014386 --- # Dataset Card for "prompted_hf_cot_gsm8k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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llm-book/ner-wikipedia-dataset
2023-07-25T17:19:14.000Z
[ "task_categories:token-classification", "size_categories:1K<n<10K", "language:ja", "license:cc-by-sa-3.0", "region:us" ]
llm-book
null
@inproceedings{omi-2021-wikipedia, title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築", author = "近江 崇宏", booktitle = "言語処理学会第27回年次大会", year = "2021", url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf", }
0
448
2023-04-15T10:43:21
--- language: - ja license: - cc-by-sa-3.0 size_categories: - 1K<n<10K task_categories: - token-classification --- # Dataset Card for llm-book/ner-wikipedia-dataset 書籍『大規模言語モデル入門』で使用する、ストックマーク株式会社により作成された「Wikipediaを用いた日本語の固有表現抽出データセット」(Version 2.0)です。 Githubリポジトリ[stockmarkteam/ner-wikipedia-dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)で公開されているデータセットを利用しています。 ### Citation ```bibtex @inproceedings{omi-2021-wikipedia, title = "Wikipediaを用いた日本語の固有表現抽出のデータセットの構築", author = "近江 崇宏", booktitle = "言語処理学会第27回年次大会", year = "2021", url = "https://anlp.jp/proceedings/annual_meeting/2021/pdf_dir/P2-7.pdf", } ``` ### Licence Wikipedia日本語版と同じCC-BY-SA 3.0のライセンスに従います。
707
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euirim/goodwiki
2023-09-11T04:56:26.000Z
[ "task_categories:text-generation", "task_categories:summarization", "size_categories:10K<n<100K", "language:en", "license:mit", "region:us" ]
euirim
null
null
21
448
2023-09-09T08:31:30
--- license: mit task_categories: - text-generation - summarization language: - en pretty_name: GoodWiki size_categories: - 10K<n<100K --- # GoodWiki Dataset GoodWiki is a 179 million token dataset of English Wikipedia articles collected on **September 4, 2023**, that have been marked as [Good](https://en.wikipedia.org/wiki/Wikipedia:Good_articles) or [Featured](https://en.wikipedia.org/wiki/Wikipedia:Featured_articles) by Wikipedia editors. The dataset provides these articles in [GitHub-flavored Markdown](https://github.github.com/gfm/) format, preserving layout features like lists, code blocks, math, and block quotes, unlike many other public Wikipedia datasets. Articles are accompanied by a short description of the page as well as any associated categories. Thanks to a careful conversion process from wikicode, the markup language used by Wikipedia, articles in GoodWiki are generally faithful reproductions of the corresponding original Wikipedia pages, minus references, files, infoboxes, and tables. Curated template transclusion and HTML tag handling have minimized instances where entire words and phrases are missing mid-sentence. The hope is that this more comprehensive data will play a small role in improving open-source NLP efforts in language modeling, summarization, and instruction tuning. GoodWiki is more than 1.5 times larger (when compared using the same tokenizer) than the widely used [WikiText-103](https://huggingface.co/datasets/wikitext) dataset by Merity et al., even after excluding article descriptions. Also limited to articles marked as Good or Featured, WikiText inspired GoodWiki. The code used to build this dataset can be found on [GitHub](https://github.com/euirim/goodwiki). ## Table of Contents * [Composition](#composition) * [Languages](#languages) * [Markdown Details](#markdown-details) * [Methodology](#methodology) * [Alternatives Considered](#alternatives-considered) * [Limitations](#limitations) * [Future Work](#future-work) * [License](#license) * [Citation](#citation) * [Feedback and Contributions](#feedback-and-contributions) ## Composition The dataset consists of **44,754 rows** in a **482.7 MB** snappy-compressed Parquet file. Each row consists of the following fields: * `pageid` (`int64`): The Wikipedia id of the article. * `title` (`string`): The title of the article. * `revid` (`int64`): The Wikipedia id of the revision used. * `description` (`string | null`): Plaintext short description/summary of the article written by Wikipedia contributors. * `categories` (`list[string]`): The article's Wikipedia categories. * `markdown` (`string`): The content of the article in GitHub-flavored Markdown format. Here's an example row in JSON format: ```json { "pageid": 40961074, "title": "Attarsiya", "revid": 1164804042, "description": "Military leader of Ahhiya", "categories": [ "Ancient Anatolia", "Greek military leaders", "Mycenaean Greeks" ], "markdown": "Attarsiya was a 15th–14th century BCE military leader of Ahhiya. In the Hittite archives of circa 1400 BCE, he is described as a \"man of Ahhiya\", a country identified with the Achaeans and Mycenaean Greece. The campaigns of Attarsiya, as well as his conflict with the Hittite vassal, Madduwatta, represent the first recorded Mycenaean Greek military activity on the Anatolian mainland, as well as the first conflict between Achaeans and Hittites...", } ``` The markdown field contains a total of **179,198,101 tokens** tokenized using HuggingFace's pretrained `facebook/opt-350m` tokenizer. It also contains **811,791,686 characters** and **132,691,055 words**. Even with the markdown formatting, GoodWiki can also be used as a plaintext dataset as markdown formatting syntax is fairly minimal. ### Languages While articles are taken exclusively from English Wikipedia, they sometimes contain small snippets from other languages as well as recurring use of the [International Phonetic Alphabet](https://en.wikipedia.org/wiki/International_Phonetic_Alphabet) in article ledes. Some articles include code blocks in pseudocode as well as in popular programming languages. ### Markdown Details GoodWiki articles follow the GitHub-flavored Markdown spec, including for blockquotes, code blocks, and lists. Bolding, italicizing, underlining, and strikethroughs have been removed as they introduce a lot of noise especially in math/computing articles. Some markdown details are worth highlighting: #### Math Content in math templates and XML tags are enclosed in markdown with `$` delimiters. For example, ```xml <math>O(n^2)</math> ``` becomes: `$O(n^2)$`. #### Super/Subscript Superscripts and subscripts are denoted using `<sup></sup>` and `<sub></sub>` tags respectively. #### \$ and \# Dollar signs and hashes are escaped with `\` to avoid interfering with math and heading syntax. ## Methodology On the evening of September 4, 2023 PT, we downloaded the wikicode of articles associated with the [Good](https://en.wikipedia.org/wiki/Category:Good_articles) and [Featured](https://en.wikipedia.org/wiki/Category:Featured_articles) categories in the main namespace (`ns=0`) on Wikipedia via the [Query API](https://www.mediawiki.org/wiki/API:Query). After some preprocessing including removing comments, applying magic code, and removing unrecognized or unnecessary template tags, we sent the resulting code to Wikipedia's [Expandtemplates API](https://www.mediawiki.org/wiki/API:Expandtemplates). This endpoint [transcludes](https://en.wikipedia.org/wiki/Help:Transclusion) template tags, turning them into HTML and plaintext. We chose the templates to transclude by counting all the templates used across the dataset and selecting the ones that are not rare, not used for citations, and not used for asides like infoboxes and tables. The Expandtemplates output is then postprocessed. During this phase, we remove sections associated with references (e.g. `Sources Cited`), extract text from wikilinks and external links, delete media links, and handle [HTML tags](https://en.wikipedia.org/wiki/Help:HTML_in_wikitext). The postprocessed output is then converted to GitHub-flavored Markdown using [Pandoc](https://pandoc.org/). We also discarded articles detected by Pandoc to have corrupt wikicode (`n=125`). The markdown output is then cleaned using regular expressions to remove excessive spacing, empty list items, unnecessary escaping, and resolve other problems with Pandoc's conversion. We normalized the markdown output unicode to a composed form (NFKC). ### Alternatives Considered #### Converting End-To-End Using Pandoc While Pandoc can in theory convert raw wikicode to markdown, it is **not** a complete wikicode parser and therefore often produces errant output without preprocessing. Furthermore, direct conversion of raw wikicode would lose a lot of the content attached to wikicode templates as Pandoc cannot perform transclusion. #### Using TextExtracts API Wikipedia has a [TextExtracts](https://www.mediawiki.org/wiki/Extension:TextExtracts#API) API that directly outputs a limited HTML or plaintext output of a page given that page's title. In practice, I've found the HTML output generated by this endpoint to often contain malformed or incomplete HTML with injected references that are difficult to parse. The plaintext output was also often poor, including reference artifacts and missing content. Other caveats are listed [here](https://www.mediawiki.org/wiki/Extension:TextExtracts#API) and were the reasons why this approach was discarded. #### Transcluding All Templates During the preprocessing process, we eliminate templates outside of a given subset. We did this because we found that transcluding all templates injected a lot of noise in the output, including janky HTML, styles, references, and unnecessary content. This noise made parsing difficult and error-prone, resulting in poor quality markdown littered with artifacts similar to those visible in the TextExtracts output. Transcluding a subset largely solved these issues while still preserving as much content as possible. ## Limitations * Chemical equations sometimes include formatting issues like unnecessary line-breaks. These equations, however, are rare. * In articles about ancient civilizations and languages, rare Unicode characters are occasionally included in the markdown. It might be worth removing these characters during the tokenization process. * In rare cases, book/article names may be missing from the markdown as they are considered citations in the wikicode. * Inflation data is missing from some articles. These articles use the `Inflation` template tag to include this information, which works poorly with the Extracttemplates API. * Articles may feature empty sections due to table/box removal. * Some code blocks are denoted using indents instead of formal code blocks. This is due to the original wikicode not denoting them as such. * Template subset allowing transclusion will probably need to be updated for use in future data dumps. The list of templates used on Wikipedia is constantly evolving. ## Future Work Time permitting, we hope to apply this careful conversion/generation process on all of English Wikipedia which will require our conversion script to be much faster and better parallelized. We also hope to extract other information from pages like entries in infoboxes that could be useful for question answering and instruction tuning applications. If you're interested in helping out, please reach out! ## License The dataset and accompanying [code](https://github.com/euirim/goodwiki) are licensed under an **MIT license**. Pandoc, which must be downloaded separately, is GPL-licensed. While this project is permissively licensed, we hope that you contribute any improvements you make to this dataset. ## Citation If you use the GoodWiki Dataset in your research or projects, please cite it using the following citation: ```tex @misc{GoodWiki, title = {GoodWiki Dataset}, author = {Choi, Euirim}, howpublished = {\url{https://www.github.com/euirim/goodwiki}}, month = {September}, year = {2023} } ``` ## Feedback and Contributions Contributions via pull requests and discussions are welcome. If you don't know how you could help improve this project, please look at the [Future Work](#future-work) section. Was this dataset useful for your work? Please let us know. We'd love to feature your project :)
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ai4bharat/IndicQA
2023-06-20T03:03:32.000Z
[ "task_categories:question-answering", "task_ids:closed-domain-qa", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:multilingual", "size_categories:n<1K", "source_datasets:original", "language:as", "language:bn", "language:gu", "language:hi", "language:kn", "language:ml", "language:mr", "language:or", "language:pa", "language:ta", "language:te", "license:cc-by-4.0", "region:us" ]
ai4bharat
\
\
1
447
2022-09-15T04:52:16
--- annotations_creators: - expert-generated language: - as - bn - gu - hi - kn - ml - mr - or - pa - ta - te language_creators: - found license: - cc-by-4.0 multilinguality: - multilingual pretty_name: IndicQA size_categories: - n<1K source_datasets: - original tags: [] task_categories: - question-answering task_ids: - closed-domain-qa --- # Dataset Card for [Dataset Name] ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary [More Information Needed] ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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KETI-AIR/kor_corpora
2021-09-16T07:32:28.000Z
[ "region:us" ]
KETI-AIR
null
null
0
445
2022-03-02T23:29:22
Entry not found
15
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vietgpt/wikipedia_vi
2023-09-16T05:11:18.000Z
[ "task_categories:text-generation", "size_categories:1M<n<10M", "language:vi", "LM", "region:us" ]
vietgpt
null
null
4
445
2023-02-21T20:39:38
--- dataset_info: features: - name: id dtype: int64 - name: revid dtype: string - name: url dtype: string - name: title dtype: string - name: text dtype: string splits: - name: train num_bytes: 1053551922.960177 num_examples: 1284930 download_size: 569515706 dataset_size: 1053551922.960177 task_categories: - text-generation language: - vi size_categories: - 1M<n<10M tags: - LM --- # Wikipedia - Source: https://huggingface.co/datasets/wikipedia - Num examples: 1,281,412 - Language: Vietnamese ```python from datasets import load_dataset load_dataset("tdtunlp/wikipedia_vi") ```
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sradc/chunked-shuffled-wikipedia20220301en-bookcorpusopen
2023-07-17T20:33:04.000Z
[ "language:en", "region:us" ]
sradc
null
null
1
445
2023-05-03T17:40:58
--- language: en dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 26076989556 num_examples: 33536113 download_size: 17380043798 dataset_size: 26076989556 --- # Dataset Card for "wikipedia20220301en-bookcorpusopen-chunked-shuffled" ``` num_examples: 33.5 million download_size: 15.3 GB dataset_size: 26.1 GB ``` This dataset combines [wikipedia20220301.en](https://huggingface.co/datasets/wikipedia) and [bookcorpusopen](https://huggingface.co/datasets/bookcorpusopen), and splits the data into smaller chunks, of size ~820 chars (such that each item will be at least ~128 tokens for the average tokenizer). The order of the items in this dataset has been shuffled, meaning you don't have to use `dataset.shuffle`, which is slower to iterate over. The logic only splits on spaces, so the chunks are likely to be slightly larger than 820 chars. The dataset has been normalized into lower case, with accents and non-english characters removed. Items with less than 200 chars or more than 1000 chars have been removed. This dataset is processed for convenience, at the expense of losing some percentage of the tokens due to truncation, (assuming the training minibatches are truncated to 128 tokens).
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TrainingDataPro/email-spam-classification
2023-09-14T16:37:38.000Z
[ "task_categories:text-classification", "language:en", "license:cc-by-nc-nd-4.0", "finance", "code", "region:us" ]
TrainingDataPro
null
null
1
445
2023-07-25T12:09:29
--- license: cc-by-nc-nd-4.0 task_categories: - text-classification language: - en tags: - finance - code --- # Email Spam Classification The dataset consists of a collection of emails categorized into two major classes: **spam** and **not spam**. It is designed to facilitate the development and evaluation of spam detection or email filtering systems. **The spam emails** in the dataset are typically unsolicited and unwanted messages that aim to promote products or services, spread malware, or deceive recipients for various malicious purposes. These emails often contain misleading subject lines, excessive use of advertisements, unauthorized links, or attempts to collect personal information. The **non-spam emails** in the dataset are genuine and legitimate messages sent by individuals or organizations. They may include personal or professional communication, newsletters, transaction receipts, or any other non-malicious content. The dataset encompasses emails of varying *lengths, languages, and writing styles*, reflecting the inherent heterogeneity of email communication. This diversity aids in training algorithms that can generalize well to different types of emails, making them robust against different spammer tactics and variations in non-spam email content. ### The dataset's possible applications: - spam detection - fraud detection - email filtering systems - customer support automation - natural language processing ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F4d1fdedb2827152696dd0c0af05fd8da%2Ff.png?generation=1690286497115141&alt=media) # Get the dataset ### This is just an example of the data Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) to discuss your requirements, learn about the price and buy the dataset. # File with the extension .csv includes the following information: - **title**: title of the email, - **text**: text of the email, - **type**: type of the email # Email spam might be collected in accordance with your requirements. ## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=email-spam-classification) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
2,566
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opus_wikipedia
2023-06-01T14:59:51.000Z
[ "task_categories:translation", "annotations_creators:found", "language_creators:found", "multilinguality:multilingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "source_datasets:original", "language:ar", "language:bg", "language:cs", "language:de", "language:el", "language:en", "language:es", "language:fa", "language:fr", "language:he", "language:hu", "language:it", "language:nl", "language:pl", "language:pt", "language:ro", "language:ru", "language:sl", "language:tr", "language:vi", "license:unknown", "region:us" ]
null
This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Please cite the following publication if you use the data: Krzysztof Wołk and Krzysztof Marasek: Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs., Procedia Technology, 18, Elsevier, p.126-132, 2014 20 languages, 36 bitexts total number of files: 114 total number of tokens: 610.13M total number of sentence fragments: 25.90M
@InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} }
4
443
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ar - bg - cs - de - el - en - es - fa - fr - he - hu - it - nl - pl - pt - ro - ru - sl - tr - vi license: - unknown multilinguality: - multilingual size_categories: - 100K<n<1M - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: null pretty_name: OpusWikipedia dataset_info: - config_name: ar-en features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - en splits: - name: train num_bytes: 45207715 num_examples: 151136 download_size: 16097997 dataset_size: 45207715 - config_name: ar-pl features: - name: id dtype: string - name: translation dtype: translation: languages: - ar - pl splits: - name: train num_bytes: 304851676 num_examples: 823715 download_size: 104585718 dataset_size: 304851676 - config_name: en-sl features: - name: id dtype: string - name: translation dtype: translation: languages: - en - sl splits: - name: train num_bytes: 30479739 num_examples: 140124 download_size: 11727538 dataset_size: 30479739 - config_name: en-ru features: - name: id dtype: string - name: translation dtype: translation: languages: - en - ru splits: - name: train num_bytes: 167649057 num_examples: 572717 download_size: 57356138 dataset_size: 167649057 - config_name: en-vi features: - name: id dtype: string - name: translation dtype: translation: languages: - en - vi splits: - name: train num_bytes: 7571598 num_examples: 58116 download_size: 2422413 dataset_size: 7571598 config_names: - ar-en - ar-pl - en-ru - en-sl - en-vi --- # Dataset Card for OpusWikipedia ## 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:** http://opus.nlpl.eu/Wikipedia.php - **Repository:** None - **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf - **Leaderboard:** [More Information Needed] - **Point of Contact:** [More Information Needed] ### Dataset Summary This is a corpus of parallel sentences extracted from Wikipedia by Krzysztof Wołk and Krzysztof Marasek. Tha dataset contains 20 languages and 36 bitexts. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs, e.g. ```python dataset = load_dataset("opus_wikipedia", lang1="it", lang2="pl") ``` You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Wikipedia.php ### Supported Tasks and Leaderboards [More Information Needed] ### Languages The languages in the dataset are: - ar - bg - cs - de - el - en - es - fa - fr - he - hu - it - nl - pl - pt - ro - ru - sl - tr - vi ## Dataset Structure ### Data Instances ``` { 'id': '0', 'translation': { "ar": "* Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics.", "en": "*Encyclopaedia of Mathematics online encyclopaedia from Springer, Graduate-level reference work with over 8,000 entries, illuminating nearly 50,000 notions in mathematics." } } ``` ### Data Fields - `id` (`str`): Unique identifier of the parallel sentence for the pair of languages. - `translation` (`dict`): Parallel sentences for the pair of languages. ### Data Splits The dataset contains a single `train` split. ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 ```bibtex @article{WOLK2014126, title = {Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs}, journal = {Procedia Technology}, volume = {18}, pages = {126-132}, year = {2014}, note = {International workshop on Innovations in Information and Communication Science and Technology, IICST 2014, 3-5 September 2014, Warsaw, Poland}, issn = {2212-0173}, doi = {https://doi.org/10.1016/j.protcy.2014.11.024}, url = {https://www.sciencedirect.com/science/article/pii/S2212017314005453}, author = {Krzysztof Wołk and Krzysztof Marasek}, keywords = {Comparable corpora, machine translation, NLP}, } ``` ```bibtex @InProceedings{TIEDEMANN12.463, author = {J{\"o}rg Tiedemann}, title = {Parallel Data, Tools and Interfaces in OPUS}, booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)}, year = {2012}, month = {may}, date = {23-25}, address = {Istanbul, Turkey}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-7-7}, language = {english} } ``` ### Contributions Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset.
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embedding-data/sentence-compression
2022-08-02T03:02:47.000Z
[ "task_categories:sentence-similarity", "task_ids:semantic-similarity-classification", "language:en", "license:mit", "region:us" ]
embedding-data
null
null
10
442
2022-07-07T22:58:31
--- license: mit language: - en paperswithcode_id: embedding-data/sentence-compression pretty_name: sentence-compression task_categories: - sentence-similarity - paraphrase-mining task_ids: - semantic-similarity-classification --- # Dataset Card for "sentence-compression" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression) - **Repository:** [More Information Needed](https://github.com/google-research-datasets/sentence-compression) - **Paper:** [More Information Needed](https://www.aclweb.org/anthology/D13-1155/) - **Point of Contact:** [Katja Filippova](altun@google.com) - **Size of downloaded dataset files:** - **Size of the generated dataset:** - **Total amount of disk used:** 14.2 MB ### Dataset Summary Dataset with pairs of equivalent sentences. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from using the dataset. Disclaimer: The team releasing sentence-compression did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team. ### Supported Tasks - [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity. ### Languages - English. ## Dataset Structure Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value". ``` {"set": [sentence_1, sentence_2]} {"set": [sentence_1, sentence_2]} ... {"set": [sentence_1, sentence_2]} ``` This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences. ### Usage Example Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with: ```python from datasets import load_dataset dataset = load_dataset("embedding-data/sentence-compression") ``` The dataset is loaded as a `DatasetDict` and has the format: ```python DatasetDict({ train: Dataset({ features: ['set'], num_rows: 180000 }) }) ``` Review an example `i` with: ```python dataset["train"][i]["set"] ``` ### Curation Rationale [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/google-research-datasets/sentence-compression) #### Who are the source language producers? [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Annotations #### Annotation process [More Information Needed](https://github.com/google-research-datasets/sentence-compression) #### Who are the annotators? [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Personal and Sensitive Information [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Discussion of Biases [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Other Known Limitations [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Licensing Information [More Information Needed](https://github.com/google-research-datasets/sentence-compression) ### Contributions
4,878
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skytnt/anime-segmentation
2022-10-03T01:35:40.000Z
[ "task_categories:image-segmentation", "task_ids:semantic-segmentation", "size_categories:10K<n<100K", "source_datasets:original", "license:cc0-1.0", "region:us" ]
skytnt
A segmentation dataset for anime character
null
18
441
2022-09-30T05:27:06
--- annotations_creators: [] language: [] language_creators: [] license: - cc0-1.0 multilinguality: [] pretty_name: Anime Segmentation size_categories: - 10K<n<100K source_datasets: - original tags: [] task_categories: - image-segmentation task_ids: - semantic-segmentation --- ## Dataset Description A segmentation dataset for anime character My project: [anime-segmentation](https://github.com/SkyTNT/anime-segmentation) ### Dataset Summary | Dir | Description | Format | Images | | ---- | ---- | ---- | ---- | | bg | background images | jpg | 8057 | | fg | foreground images, transparent background | png | 11802 | | imgs | real images with background and foreground| jpg | 1111 | | masks| labels for imgs | jpg | 1111 | Total size: 18GB ### Collection Method Collect background from [character_bg_seg_data](https://github.com/ShuhongChen/bizarre-pose-estimator#download) Collect foreground from danbooru website. Collect imgs and masks from [AniSeg](https://github.com/jerryli27/AniSeg#about-the-models) and danbooru website. I use [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to restore the background images. I clean the dataset using [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) first then manually, to make sue all foreground is anime character. ### Contributions Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset. Thanks to [@ShuhongChen](https://github.com/ShuhongChen) for [character_bg_seg_data](https://github.com/ShuhongChen/bizarre-pose-estimator#download) Thanks to [@jerryli27](https://github.com/jerryli27) for [AniSeg](https://github.com/jerryli27/AniSeg#about-the-models)
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Multimodal-Fatima/FGVC_Aircraft_train
2023-05-04T05:30:31.000Z
[ "region:us" ]
Multimodal-Fatima
null
null
0
441
2022-11-13T05:05:42
--- dataset_info: features: - name: image dtype: image - name: family dtype: class_label: names: '0': A300 '1': A310 '2': A320 '3': A330 '4': A340 '5': A380 '6': ATR-42 '7': ATR-72 '8': An-12 '9': BAE 146 '10': BAE-125 '11': Beechcraft 1900 '12': Boeing 707 '13': Boeing 717 '14': Boeing 727 '15': Boeing 737 '16': Boeing 747 '17': Boeing 757 '18': Boeing 767 '19': Boeing 777 '20': C-130 '21': C-47 '22': CRJ-200 '23': CRJ-700 '24': Cessna 172 '25': Cessna 208 '26': Cessna Citation '27': Challenger 600 '28': DC-10 '29': DC-3 '30': DC-6 '31': DC-8 '32': DC-9 '33': DH-82 '34': DHC-1 '35': DHC-6 '36': DR-400 '37': Dash 8 '38': Dornier 328 '39': EMB-120 '40': Embraer E-Jet '41': Embraer ERJ 145 '42': Embraer Legacy 600 '43': Eurofighter Typhoon '44': F-16 '45': F/A-18 '46': Falcon 2000 '47': Falcon 900 '48': Fokker 100 '49': Fokker 50 '50': Fokker 70 '51': Global Express '52': Gulfstream '53': Hawk T1 '54': Il-76 '55': King Air '56': L-1011 '57': MD-11 '58': MD-80 '59': MD-90 '60': Metroliner '61': PA-28 '62': SR-20 '63': Saab 2000 '64': Saab 340 '65': Spitfire '66': Tornado '67': Tu-134 '68': Tu-154 '69': Yak-42 - name: manufacturer dtype: class_label: names: '0': ATR '1': Airbus '2': Antonov '3': Beechcraft '4': Boeing '5': Bombardier Aerospace '6': British Aerospace '7': Canadair '8': Cessna '9': Cirrus Aircraft '10': Dassault Aviation '11': Dornier '12': Douglas Aircraft Company '13': Embraer '14': Eurofighter '15': Fairchild '16': Fokker '17': Gulfstream Aerospace '18': Ilyushin '19': Lockheed Corporation '20': Lockheed Martin '21': McDonnell Douglas '22': Panavia '23': Piper '24': Robin '25': Saab '26': Supermarine '27': Tupolev '28': Yakovlev '29': de Havilland - name: label dtype: class_label: names: '0': 707-320 '1': 727-200 '2': 737-200 '3': 737-300 '4': 737-400 '5': 737-500 '6': 737-600 '7': 737-700 '8': 737-800 '9': 737-900 '10': 747-100 '11': 747-200 '12': 747-300 '13': 747-400 '14': 757-200 '15': 757-300 '16': 767-200 '17': 767-300 '18': 767-400 '19': 777-200 '20': 777-300 '21': A300B4 '22': A310 '23': A318 '24': A319 '25': A320 '26': A321 '27': A330-200 '28': A330-300 '29': A340-200 '30': A340-300 '31': A340-500 '32': A340-600 '33': A380 '34': ATR-42 '35': ATR-72 '36': An-12 '37': BAE 146-200 '38': BAE 146-300 '39': BAE-125 '40': Beechcraft 1900 '41': Boeing 717 '42': C-130 '43': C-47 '44': CRJ-200 '45': CRJ-700 '46': CRJ-900 '47': Cessna 172 '48': Cessna 208 '49': Cessna 525 '50': Cessna 560 '51': Challenger 600 '52': DC-10 '53': DC-3 '54': DC-6 '55': DC-8 '56': DC-9-30 '57': DH-82 '58': DHC-1 '59': DHC-6 '60': DHC-8-100 '61': DHC-8-300 '62': DR-400 '63': Dornier 328 '64': E-170 '65': E-190 '66': E-195 '67': EMB-120 '68': ERJ 135 '69': ERJ 145 '70': Embraer Legacy 600 '71': Eurofighter Typhoon '72': F-16A/B '73': F/A-18 '74': Falcon 2000 '75': Falcon 900 '76': Fokker 100 '77': Fokker 50 '78': Fokker 70 '79': Global Express '80': Gulfstream IV '81': Gulfstream V '82': Hawk T1 '83': Il-76 '84': L-1011 '85': MD-11 '86': MD-80 '87': MD-87 '88': MD-90 '89': Metroliner '90': Model B200 '91': PA-28 '92': SR-20 '93': Saab 2000 '94': Saab 340 '95': Spitfire '96': Tornado '97': Tu-134 '98': Tu-154 '99': Yak-42 - name: id dtype: int64 - name: clip_tags_ViT_L_14 sequence: string - name: LLM_Description_gpt3_downstream_tasks_ViT_L_14 sequence: string - name: blip_caption dtype: string - name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14 sequence: string - name: Attributes_ViT_L_14_text_davinci_003_full sequence: string - name: Attributes_ViT_L_14_text_davinci_003_fgvc sequence: string - name: clip_tags_ViT_L_14_with_openai_classes sequence: string - name: clip_tags_ViT_L_14_wo_openai_classes sequence: string - name: clip_tags_ViT_L_14_simple_specific dtype: string - name: clip_tags_ViT_L_14_ensemble_specific dtype: string - name: clip_tags_ViT_B_16_simple_specific dtype: string - name: clip_tags_ViT_B_16_ensemble_specific dtype: string - name: clip_tags_ViT_B_32_simple_specific dtype: string - name: clip_tags_ViT_B_32_ensemble_specific dtype: string - name: Attributes_ViT_B_16_descriptors_text_davinci_003_full sequence: string - name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full sequence: string - name: clip_tags_LAION_ViT_H_14_2B_simple_specific dtype: string - name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific dtype: string splits: - name: train num_bytes: 931613762.0 num_examples: 3334 download_size: 925638163 dataset_size: 931613762.0 --- # Dataset Card for "FGVC_Aircraft_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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nlpai-lab/openassistant-guanaco-ko
2023-06-01T10:44:35.000Z
[ "task_categories:text-generation", "task_categories:question-answering", "task_categories:summarization", "size_categories:1K<n<10K", "language:ko", "license:apache-2.0", "region:us" ]
nlpai-lab
null
null
4
441
2023-06-01T06:54:34
--- license: apache-2.0 task_categories: - text-generation - question-answering - summarization language: - ko size_categories: - 1K<n<10K --- ### Dataset Summary Korean translation of Guanaco via the DeepL API Note: There are cases where multilingual data has been converted to monolingual data during batch translation to Korean using the API. Below is Guanaco's README. ---- This dataset is a subset of the Open Assistant dataset, which you can find here: https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples. This dataset was used to train Guanaco with QLoRA. For further information, please see the original dataset. License: Apache 2.0
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cfq
2023-04-05T09:42:18.000Z
[ "task_categories:question-answering", "task_categories:other", "task_ids:open-domain-qa", "task_ids:closed-domain-qa", "annotations_creators:no-annotation", "language_creators:expert-generated", "multilinguality:monolingual", "size_categories:100K<n<1M", "source_datasets:original", "language:en", "license:cc-by-4.0", "compositionality", "arxiv:1912.09713", "region:us" ]
null
The CFQ dataset (and it's splits) for measuring compositional generalization. See https://arxiv.org/abs/1912.09713.pdf for background. Example usage: data = datasets.load_dataset('cfq/mcd1')
@inproceedings{Keysers2020, title={Measuring Compositional Generalization: A Comprehensive Method on Realistic Data}, author={Daniel Keysers and Nathanael Sch\"{a}rli and Nathan Scales and Hylke Buisman and Daniel Furrer and Sergii Kashubin and Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and Olivier Bousquet}, booktitle={ICLR}, year={2020}, url={https://arxiv.org/abs/1912.09713.pdf}, }
2
440
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Compositional Freebase Questions size_categories: - 100K<n<1M source_datasets: - original task_categories: - question-answering - other task_ids: - open-domain-qa - closed-domain-qa paperswithcode_id: cfq tags: - compositionality dataset_info: - config_name: mcd1 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 37408806 num_examples: 95743 - name: test num_bytes: 5446503 num_examples: 11968 download_size: 267599061 dataset_size: 42855309 - config_name: mcd2 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39424657 num_examples: 95743 - name: test num_bytes: 5314019 num_examples: 11968 download_size: 267599061 dataset_size: 44738676 - config_name: mcd3 features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 38316345 num_examples: 95743 - name: test num_bytes: 5244503 num_examples: 11968 download_size: 267599061 dataset_size: 43560848 - config_name: question_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 39989433 num_examples: 98999 - name: test num_bytes: 5781561 num_examples: 10340 download_size: 267599061 dataset_size: 45770994 - config_name: question_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41217350 num_examples: 95654 - name: test num_bytes: 5179936 num_examples: 11909 download_size: 267599061 dataset_size: 46397286 - config_name: query_complexity_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40270175 num_examples: 100654 - name: test num_bytes: 5634924 num_examples: 9512 download_size: 267599061 dataset_size: 45905099 - config_name: query_pattern_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 40811284 num_examples: 94600 - name: test num_bytes: 5268358 num_examples: 12589 download_size: 267599061 dataset_size: 46079642 - config_name: random_split features: - name: question dtype: string - name: query dtype: string splits: - name: train num_bytes: 41279218 num_examples: 95744 - name: test num_bytes: 5164923 num_examples: 11967 download_size: 267599061 dataset_size: 46444141 --- # Dataset Card for "cfq" ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/google-research/google-research/tree/master/cfq](https://github.com/google-research/google-research/tree/master/cfq) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** https://arxiv.org/abs/1912.09713 - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.14 GB - **Size of the generated dataset:** 362.07 MB - **Total amount of disk used:** 2.50 GB ### Dataset Summary The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can also be used for semantic parsing. ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages English (`en`). ## Dataset Structure ### Data Instances #### mcd1 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 42.90 MB - **Total amount of disk used:** 310.49 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 a ns:people.person .\n?x0 ns:influence.influence_node.influenced M1 .\n?x0 ns:influence.influence_node.influenced M2 .\n?x0 ns:people.person.spouse_s/ns:people.marriage.spouse|ns:fictional_universe.fictional_character.married_to/ns:fictional_universe.marriage_of_fictional_characters.spouses ?x1 .\n?x1 a ns:film.cinematographer .\nFILTER ( ?x0 != ?x1 )\n}', 'question': 'Did a person marry a cinematographer , influence M1 , and influence M2' } ``` #### mcd2 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 44.77 MB - **Total amount of disk used:** 312.38 MB An example of 'train' looks as follows. ``` { 'query': 'SELECT count(*) WHERE {\n?x0 ns:people.person.parents|ns:fictional_universe.fictional_character.parents|ns:organization.organization.parent/ns:organization.organization_relationship.parent ?x1 .\n?x1 a ns:people.person .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM1 ns:business.employer.employees/ns:business.employment_tenure.person M4 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person ?x0 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M2 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M3 .\nM5 ns:business.employer.employees/ns:business.employment_tenure.person M4\n}', 'question': "Did M1 and M5 employ M2 , M3 , and M4 and employ a person 's child" } ``` #### mcd3 - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 43.60 MB - **Total amount of disk used:** 311.20 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_complexity_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 45.95 MB - **Total amount of disk used:** 313.55 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` #### query_pattern_split - **Size of downloaded dataset files:** 267.60 MB - **Size of the generated dataset:** 46.12 MB - **Total amount of disk used:** 313.72 MB An example of 'train' looks as follows. ``` { "query": "SELECT /producer M0 . /director M0 . ", "question": "Who produced and directed M0?" } ``` ### Data Fields The data fields are the same among all splits and configurations: - `question`: a `string` feature. - `query`: a `string` feature. ### Data Splits | name | train | test | |---------------------------|-------:|------:| | mcd1 | 95743 | 11968 | | mcd2 | 95743 | 11968 | | mcd3 | 95743 | 11968 | | query_complexity_split | 100654 | 9512 | | query_pattern_split | 94600 | 12589 | | question_complexity_split | 98999 | 10340 | | question_pattern_split | 95654 | 11909 | | random_split | 95744 | 11967 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @inproceedings{Keysers2020, title={Measuring Compositional Generalization: A Comprehensive Method on Realistic Data}, author={Daniel Keysers and Nathanael Sch"{a}rli and Nathan Scales and Hylke Buisman and Daniel Furrer and Sergii Kashubin and Nikola Momchev and Danila Sinopalnikov and Lukasz Stafiniak and Tibor Tihon and Dmitry Tsarkov and Xiao Wang and Marc van Zee and Olivier Bousquet}, booktitle={ICLR}, year={2020}, url={https://arxiv.org/abs/1912.09713.pdf}, } ``` ### Contributions Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@brainshawn](https://github.com/brainshawn) for adding this dataset.
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togethercomputer/RedPajama-Data-V2
2023-10-31T12:03:06.000Z
[ "task_categories:text-generation", "language:en", "language:de", "language:fr", "language:es", "language:it", "arxiv:2302.03169", "arxiv:2302.13971", "arxiv:2204.02311", "arxiv:2112.06905", "arxiv:1910.10683", "arxiv:2305.13169", "arxiv:2306.01116", "arxiv:2112.11446", "region:us" ]
togethercomputer
RedPajama V2: an Open Dataset for Training Large Language Models
null
125
440
2023-10-26T01:15:21
--- task_categories: - text-generation language: - en - de - fr - es - it pretty_name: Red Pajama V2 Dataset --- ### Getting Started RedPajama-V2 is an open dataset for training large language models. The dataset includes over 100B text documents coming from 84 CommonCrawl snapshots and processed using the [CCNet](https://github.com/facebookresearch/cc_net) pipeline. Out of these, there are 30B documents in the corpus that additionally come with quality signals. In addition, we also provide the ids of duplicated documents which can be used to create a dataset with 20B deduplicated documents. Check out our [blog post](https://together.ai/blog/redpajama-data-v2) for more details on the build process, dataset structure and schema. To familiarize yourself with the dataset, you can load the sample dataset using: ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="sample") ``` To download a the dataset for a specific combination of `{partition} x {snapshot_id} x {language}` (e.g., English and German data from the `head_middle` partition of the 2023-06 and the 2022-49 dumps), you can run the following command. _Note that this will downlaod the entire dumps and requires ~1TB disk space per dump_. ```python from datasets import load_dataset ds = load_dataset("togethercomputer/RedPajama-Data-V2", name="default", partition="head_middle", snapshots=["2023-06", "2022-49"], languages=["en", "de"]) ``` Alternatively, you can also directly download the files using the following instructions, using English data from the `2023-06` snapshot and the `head_middle` partition as an example. The full set of CC snapshots included in the dataset is given in `_CC_SNAPSHOT_IDS`, and the available partitions are `tail` and `head_middle`. The available language tags are `en`, `de`, `fr`, `es`, `it`. To download the plain text data, available for both the `head_middle` and `tail` partitions, you can run ```bash CC_SNAPSHOT="2023-06" LANG="en" PARTITION="head_middle" BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0" listings_tag="${LANG}-${CC_SNAPSHOT}-${PARTITION}" mkdir listings wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt" listings_file="listings/${listings_tag}.txt" # download documents while read line; do url="${BASE_URL}/documents/${line}.json.gz" dest="documents/${line}.json.gz" mkdir -p $(dirname $dest) wget "$url" -O "$dest" done <"$listings_file" ``` In addition, for the `head_middle` partition, you can also download the quality signals, minhash signatures and duplicate ids using the following commands: ```bash CC_SNAPSHOT="2023-06" LANG="en" BASE_URL="https://data.together.xyz/redpajama-data-v2/v1.0.0" listings_tag="${LANG}-${CC_SNAPSHOT}-head_middle" mkdir listings wget "${BASE_URL}/listings/${listings_tag}.txt" -O "listings/${listings_tag}.txt" listings_file="listings/${listings_tag}.txt" # download quality signals while read line; do url="${BASE_URL}/quality_signals/${line}.signals.json.gz" dest="quality_signals/${line}.signals.json.gz" mkdir -p $(dirname $dest) wget "$url" -O "$dest" done <"$listings_file" # download other components COMPS=("minhash" "duplicates") for comp in "${COMPS[@]}"; do while read line; do url="${BASE_URL}/${comp}/${line}.${comp}.parquet" dest="${comp}/${line}.${comp}.parquet" mkdir -p $(dirname $dest) wget "$url" -O "$dest" done <"$listings_file" done ``` A full set of scripts to recreate the dataset, including the quality signals, can be found [here](https://github.com/togethercomputer/RedPajama-Data). ### Applying Filtering Rules You can use the quality signals to filter the raw RedPajama-V2 dataset for a given set of rules. For example, consider the following set of rules used in Gopher: ```python def gopher_rules_pass(sample) -> bool: """ function returns True if the sample complies with Gopher rules """ signals = json.loads(sample["quality_signals"]) # rule 1: number of words between 50 and 10'000 word_count = signals["rps_doc_word_count"][0][2] if word_count < 50 or word_count > 10_000: return False # rule 2: mean word length between 3 and 10 mean_word_length = signals["rps_doc_mean_word_length"][0][2] if mean_word_length < 3 or mean_word_length > 10: return False # rule 2: symbol to word ratio below 0.1 symbol_word_ratio = signals["rps_doc_symbol_to_word_ratio"][0][2] if symbol_word_ratio > 0.1: return False # rule 3: 90% of lines need to start without a bullet point n_lines = signals["ccnet_nlines"][0][2] n_lines_bulletpoint_start = sum(map(lambda ln: ln[2], signals["rps_lines_start_with_bulletpoint"])) if n_lines_bulletpoint_start / n_lines > 0.9: return False # rule 4: the ratio between characters in the most frequent 2-gram and the total number # of characters must be below 0.2 top_2_gram_frac = signals["rps_doc_frac_chars_top_2gram"][0][2] if top_2_gram_frac > 0.2: return False # rule 5: ... return True ``` Filtering the RedPajama-V2 dataset with this set of rules is then as easy as: ```python ds_iterator = load_dataset( "togethercomputer/RedPajama-Data-V2", snapshots=["2023-14"], languages=["en"], name="default", streaming=True ) filtered_dataset = [] for sample in ds_iterator["train"]: if not gopher_rules_pass(sample): continue filtered_dataset.append(sample) ``` ### Dataset Summary RedPajama-V2 is an open dataset for training large language models and includes over 100B text documents. Out of these, 30B documents come with quality annotations. Out of these, there are 20B unique documents. #### Quality Annotations | Annotation Tag | Description | Category | Reference | |------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------|-------------------------------------------------------------------------------------------------------------------------------| | ccnet_bucket | head, middle or tail bucket of the perplexity score | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_language_score | score of the language identification model | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_length | number of characters | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_nlines | number of lines | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_original_length | number of characters before in-document line deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_original_nlines | number of lines before in-document line deduplication | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | ccnet_perplexity | perplexity of an LM trained on Wikipedia | CCNet | [CCNet](https://github.com/facebookresearch/cc_net) | | rps_doc_books_importance | Given a bag of {1,2}-wordgram model trained on Books p, and a model trained on the source domain q, This is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) | | rps_doc_openwebtext_importance | Given a bag of {1,2}-wordgram model trained on OpenWebText p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) | | rps_doc_wikipedia_importance | Given a bag of {1,2}-wordgram model trained on Wikipedia articles p, and a model trained on the source domain q, this is the logarithm of the ratio p(doc)/q(doc). | ML Heuristics | [Importance Resampling (Xie et al.)](https://arxiv.org/abs/2302.03169) | | rps_doc_ml_wikiref_score | Fasttext classifier prediction for the document being a Wikipedia reference. This is the same fasttext model used in the RedPajama-1T dataset. Only applies to English data.. | ML Heuristics | [LLaMA](https://arxiv.org/abs/2302.13971), [RedPajama-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | | rps_doc_ml_palm_score | Fasttext classifier prediction for the document being a Wikipedia article, OpenWebText sample or a RedPajama-V1 book. Only for English data. | ML Heuristics | [PALM](https://arxiv.org/abs/2204.02311), [GLaM](https://arxiv.org/abs/2112.06905) | | rps_doc_ml_wikipedia_score | Fasttext classifier prediction for the document being a Wikipedia article. This is used for non-English data | ML Heuristics | - | | rps_doc_curly_bracket | The ratio between the number of occurrences of '{' or '}' and the number of characters in the raw text. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) | | rps_doc_frac_all_caps_words | The fraction of words in the content that only consist of uppercase letters. This is based on the raw content. | Natural Language | [Pretrainer’s Guide](https://arxiv.org/abs/2305.13169) | | rps_doc_frac_lines_end_with_ellipsis | The fraction of lines that end with an ellipsis, where an ellipsis is defined as either "..." or "…". | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_no_alph_words | The fraction of words that contain no alphabetical character. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_lorem_ipsum | The ratio between the number of occurrences of 'lorem ipsum' and the number of characters in the content after normalisation. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) | | rps_doc_mean_word_length | The mean length of words in the content after normalisation. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_stop_word_fraction | The ratio between the number of stop words and the number of words in the document. Stop words are obtained from the [stopwords-json](https://github.com/6/stopwords-json) repo. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_symbol_to_word_ratio | The ratio of symbols to words in the content.. Symbols are defined "#", "...", and "…". | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_unique_words | The fraction of unique words in the content. This is also known as the degeneracy of a text sample. Calculated based on the normalised content. | Natural Language | [Pretrainer’s Guide](https://arxiv.org/abs/2305.13169) | | rps_doc_unigram_entropy | The entropy of the unigram distribution of the content. This measures the diversity of the content and is computed using sum(-x / total * log(x / total)) where the sum is taken over counts of unique words in the normalised content. | Natural Language | - | | rps_doc_word_count | The number of words in the content after normalisation. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_lines_ending_with_terminal_punctution_mark | Indicates whether a line ends with a terminal punctuation mark. A terminal punctation mark is defined as one of: ".", "!", "?", "”". | Natural Language | [C4](https://arxiv.org/abs/1910.10683) | | rps_lines_javascript_counts | The number of occurrences of the word "javascript" in each line. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) | | rps_lines_num_words | The number of words in each line. This is computed based on the normalised text. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) , [RefinedWeb](https://arxiv.org/abs/2306.01116) | | rps_lines_numerical_chars_fraction | The ratio between the number of numerical characters and total number of characters in each line. This is based on the normalised content. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116) | | rps_lines_start_with_bulletpoint | Whether the lines that start with a bullet point symbol. The following set of unicodes are considered a bullet point: \u2022 (bullet point), \u2023 (triangular bullet point), \u25B6 (black right pointing triangle), \u25C0 (black left pointing triangle), \u25E6 (white bullet point), \u25A0 (black square), \u25A1 (white square), \u25AA (black small square), \u25AB (white small square), \u2013 (en dash). | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_lines_uppercase_letter_fraction | The ratio between the number of uppercase letters and total number of characters in each line. This is based on the raw text. | Natural Language | [RefinedWeb](https://arxiv.org/abs/2306.01116) | | rps_doc_num_sentences | The number of sentences in the content. This is calculated using the regular expression `r'\b[^.!?]+[.!?]*'`. | Natural Language | [C4](https://arxiv.org/abs/1910.10683) | | rps_doc_frac_chars_dupe_10grams | The fraction of characters in duplicate word 10grams. This operates on the lower-cased, punctuation removed content. It is also ensured that characters in overlapping ngrams are only counted once. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_dupe_5grams | The fraction of characters in duplicate word 5grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_dupe_6grams | The fraction of characters in duplicate word 6grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_dupe_7grams | The fraction of characters in duplicate word 7grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_dupe_8grams | The fraction of characters in duplicate word 8grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_dupe_9grams | The fraction of characters in duplicate word 9grams. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_top_2gram | The fraction of characters in the top word 2gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_top_3gram | The fraction of characters in the top word 3gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_frac_chars_top_4gram | The fraction of characters in the top word 4gram. | Repetitiveness | [RefinedWeb](https://arxiv.org/abs/2306.01116), [Gopher](https://arxiv.org/abs/2112.11446) | | rps_doc_ldnoobw_words | The number of sequences of words that are contained in the List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words blocklist. The blocklist is obtained from the [LDNOOBW](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words) repo. | toxicity | [C4](https://arxiv.org/abs/1910.10683) | | rps_doc_ut1_blacklist | A categorical id corresponding to the list of categories of the domain of the document. Categories are obtained from the UT1 blacklist. The list is obtained from [UT-Capitole](https://dsi.ut-capitole.fr/blacklists/). | toxicictiy | [RefinedWeb](https://arxiv.org/abs/2306.01116) | | minhash_signature_0.7 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.7. The signature is based on 128 hash functions and grouped into 14 bands and 9 rows for LSH. | Deduplication | | minhash_signature_0.8 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.8. The signature is based on 128 hash functions and grouped into 9 bands and 13 rows for LSH. | Deduplication | | minhash_signature_0.9 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 0.9. The signature is based on 128 hash functions and grouped into 5 bands and 25 rows for LSH.. | Deduplication | | minhash_signature_1.0 | Banded minhash signature of the document, for fuzzy deduplication at Jaccard similarity 1.0. The signature is based on 128 hash functions and grouped into 1 band and 128 rows for LSH. | Deduplication | #### Document and Token Counts for the Annotated and deduplicated `head_middle` part of the dataset | | # Documents | Estimated Token count (deduped) | |-------|-------------|---------------------------------| | en | 14.5B | 20.5T | | de | 1.9B | 3.0T | | fr | 1.6B | 2.7T | | es | 1.8B | 2.8T | | it | 0.9B | 1.5T | | Total | 20.8B | 30.4T | ### Languages English, German, French, Italian, Spanish ## Dataset Structure The dataset is structured into four components, each following the same key structure: ``` ├── documents ├── 2018-43 ├── 0000 ├── en_head.json.gz ├── ... ├── it_middle.json.gz ├── quality_signals ├── 2018-43 ├── 0000 ├── en_head.signals.json.gz ├── ... ├── it_middle.json.gz ├── duplicates ├── 2018-43 ├── 0000 ├── en_head.duplicates.parquet ├── ... ├── it_middle.duplicates.parquet ├── minhash ├── 2018-43 ├── 0000 ├── en_head.minhash.parquet ├── ... ├── it_middle.minhash.parquet ``` Documents files, which contain the text, folow the schema defined by CCNet: ```json { "url": "...", "date_download": "2014-08-20T06:48:26Z", "digest": "sha1:46OPKWZ7MAG5624VYYA3U3YH2MJ727B6", "length": 1095, "nlines": 8, "source_domain": "...", "title": "...", "raw_content": "Dear ...", "cc_segment": "crawl-data/CC-MAIN-2014-35/...", "original_nlines": 11, "original_length": 1174, "line_ids": [ 0, 1, 3, 4, 6, 7, 8, 9 ], "language": "en", "language_score": 0.92, "perplexity": 217.2, "bucket": "head" } ``` The quality signals follow the schema ```json { "id": "2018-43/0000/en_head.json.gz/0", "id_int": 7972430436813205988, "metadata": { "cc_segment": "crawl-data/...", "cc_net_source": "2018-43/0000/en_head.json.gz", "url": "...", "source_domain": "...", "language": "en", "snapshot_id": "2018-43" }, "quality_signals": { "ccnet_original_length": [ [ 0, 7033, 8711.0 ] ], ..., "rps_doc_stop_word_fraction": [ [ 0, 7033, 0.45121107 ] ], "rps_lines_num_words": [ [ 0, 25, 2 ], ..., [ 6980, 7033, 10 ] ] } } ``` where signal scores are encoded as a list of tuples `(start, end, score)`, where `start` and `end` are the locations in the `raw_content` string where the `score` applies. ## Dataset Creation The dataset is based on 84 snapshots provided by Common Crawl. Each snapshot was processed using the CCNet pipeline and split into `head` `middle` `tail` buckets, depending on the perplexity score. In a second step, the documents in the `head` and `middle` buckets were annotated with the quality signals described above. Finally, the documents were deduplicated based on the text, using a Bloomfilter. The duplicates were kept in the dataset, but are marked in the `duplicates` component. ## Citation To cite RedPajama, please use: ``` @software{together2023redpajama, author = {Together Computer}, title = {RedPajama: an Open Dataset for Training Large Language Models}, month = October, year = 2023, url = {https://github.com/togethercomputer/RedPajama-Data} } ``` ## Acknowledgements We are appreciative to so many partners and collaborators that together are pushing forward the frontier of open LLM models. - Thank you to the OLMo team at AI2 and friends at OpenGPT-X for the insightful discussions about datasets and data quality! Also for everyone who builds on the RedPajama dataset, including Cerebras for their SlimPajama efforts, and the over 500 models built on RedPajam to date by the open-source AI community. - We are grateful to the great team at EleutherAI for paving the path on open training datasets with The Pile and for open-sourcing code we use in training some of the RedPajama models. - Thank you to our partners of RedPajama-v1, including Ontocord.ai, MILA Québec AI Institute, ETH DS3Lab, Université de Montréal, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION. ## License Please refer to the [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use) for the data. The code used to load and process the dataset is licensed under the Apache 2.0 license. <!-- ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed] -->
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QingyiSi/Alpaca-CoT
2023-09-14T08:52:10.000Z
[ "language:en", "language:zh", "language:ml", "license:apache-2.0", "Instruction", "Cot", "region:us" ]
QingyiSi
null
null
517
438
2023-03-25T14:58:30
--- language: - en - zh - ml tags: - Instruction - Cot license: apache-2.0 datasets: - dataset1 - dataset2 --- # Instruction-Finetuning Dataset Collection (Alpaca-CoT) This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the [code](https://github.com/PhoebusSi/alpaca-CoT) of Alpaca model. We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in [https://github.com/PhoebusSi/alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT). If you think this dataset collection is helpful to you, please `like` this dataset and `star` our [github project](https://github.com/PhoebusSi/alpaca-CoT)! You are in a warm welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train Alpaca model with these datasets and open source the model checkpoints. # Contribute Welcome to join us and become a contributor to this project! If you want to share some datasets, adjust the data in the following format: ``` example.json [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` Folder should be like this: ``` Alpaca-CoT | |----example | | | |----example.json | | | ----example_context.json ... ``` Create a new pull request in [Community ](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/discussions) and publish your branch when you are ready. We will merge it as soon as we can. # Data Usage and Resources ## Data Format All data in this folder is formatted into the same templates, where each sample is as follows: ``` [ {"instruction": instruction string, "input": input string, # (may be empty) "output": output string} ] ``` ## alpaca #### alpaca_data.json > This dataset is published by [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca). It contains 52K English instruction-following samples obtained by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### alpaca_data_cleaned.json > This dataset is obtained [here](https://github.com/tloen/alpaca-lora). It is a revised version of `alpaca_data.json` by stripping of various tokenization artifacts. ## alpacaGPT4 #### alpaca_gpt4_data.json > This dataset is published by [Instruction-Tuning-with-GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM). It contains 52K English instruction-following samples generated by GPT-4 using Alpaca prompts for fine-tuning LLMs. #### alpaca_gpt4_data_zh.json > This dataset is generated by GPT-4 using Chinese prompts translated from Alpaca by ChatGPT. <!-- ## belle_cn #### belle_data_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 0.5M Chinese instruction-following samples, which is also generated by [Self-Instruction](https://github.com/yizhongw/self-instruct) techniques. #### belle_data1M_cn.json This dataset is published by [BELLE](https://github.com/LianjiaTech/BELLE). It contains 1M Chinese instruction-following samples. The data of `belle_data_cn.json` and `belle_data1M_cn.json` are not duplicated. --> ## Chain-of-Thought #### CoT_data.json > This dataset is obtained by formatting the combination of 9 CoT datasets published by [FLAN](https://github.com/google-research/FLAN). It contains 9 CoT tasks involving 74771 samples. #### CoT_CN_data.json > This dataset is obtained by tranlating `CoT_data.json` into Chinese, using Google Translate(en2cn). #### formatted_cot_data folder > This folder contains the formatted English data for each CoT dataset. #### formatted_cot_data folder > This folder contains the formatted Chinese data for each CoT dataset. ## CodeAlpaca #### code_alpaca.json > This dataset is published by [codealpaca](https://github.com/sahil280114/codealpaca). It contains code generation task involving 20022 samples. ## finance #### finance_en.json > This dataset is collected from [here](https://huggingface.co/datasets/gbharti/finance-alpaca). It contains 68912 financial related instructions in English. ## firefly #### firefly.json > his dataset is collected from [here](https://github.com/yangjianxin1/Firefly). It contains 1649398 chinese instructions in 23 nlp tasks. ## GPT4all #### gpt4all.json > This dataset is collected from [here](https://github.com/nomic-ai/gpt4all). It contains 806199 en instructions in code, storys and dialogs tasks. #### gpt4all_without_p3.json > gpt4all without Bigscience/P3, contains 437605 samples. ## GPTeacher #### GPTeacher.json > This dataset is collected from [here](https://github.com/teknium1/GPTeacher). It contains 29013 en instructions generated by GPT-4, General-Instruct - Roleplay-Instruct - Code-Instruct - and Toolformer. ## Guanaco #### GuanacoDataset.json > This dataset is collected from [here](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). It contains 534610 en instructions generated by text-davinci-003 upon 175 tasks from the Alpaca model by providing rewrites of seed tasks in different languages and adding new tasks specifically designed for English grammar analysis, natural language understanding, cross-lingual self-awareness, and explicit content recognition. #### Guanaco_additional_Dataset.json > A new additional larger dataset for different languages. ## HC3 #### HC3_ChatGPT.json/HC3_Human.json > This dataset is collected from [here](https://huggingface.co/datasets/Hello-SimpleAI/HC3). It contains 37175 en/zh instructions generated by ChatGPT and human. #### HC3_ChatGPT_deduplication.json/HC3_Human_deduplication.json > HC3 dataset without deduplication instructions. ## instinwild #### instinwild_en.json & instinwild_cn.json > The two datasets are obtained [here](https://github.com/XueFuzhao/InstructionWild). It contains 52191 English and 51504 Chinese instructions, which are collected from Twitter, where users tend to share their interesting prompts of mostly generation, open QA, and mind-storm types. (Colossal AI used these datasets to train the ColossalChat model.) ## instruct #### instruct.json > The two datasets are obtained [here](https://huggingface.co/datasets/swype/instruct). It contains 888969 English instructions, which are caugmentation performed using the advanced NLP tools provided by AllenAI. ## Natural Instructions #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://github.com/allenai/natural-instructions). It contains 5040134 instructions, which are collected from diverse nlp tasks ## prosocial dialog #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/allenai/prosocial-dialog). It contains 165681 English instructions, which are produuced by GPT-3 rewrites questions and humans feedback ## xP3 #### natural-instructions-1700tasks.zip > This dataset is obtained [here](https://huggingface.co/datasets/bigscience/xP3). It contains 78883588 instructions, which are collected by prompts & datasets across 46 of languages & 16 NLP tasks ## Chinese-instruction-collection > all datasets of Chinese instruction collection ## combination #### alcapa_plus_belle_data.json > This dataset is the combination of English `alpaca_data.json` and Chinese `belle_data_cn.json`. #### alcapa_plus_cot_data.json > This dataset is the combination of English `alpaca_data.json` and CoT `CoT_data.json`. #### alcapa_plus_belle_cot_data.json > This dataset is the combination of English `alpaca_data.json`, Chinese `belle_data_cn.json` and CoT `CoT_data.json`. ## Citation Please cite the repo if you use the data collection, code, and experimental findings in this repo. ``` @misc{alpaca-cot, author = {Qingyi Si, Zheng Lin }, school = {Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China}, title = {Alpaca-CoT: An Instruction Fine-Tuning Platform with Instruction Data Collection and Unified Large Language Models Interface}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/PhoebusSi/alpaca-CoT}}, } ``` Cite the original Stanford Alpaca, BELLE and FLAN papers as well, please.
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NathanGavenski/CartPole-v1
2023-11-01T18:24:38.000Z
[ "size_categories:10M<n<100M", "license:mit", "Imitation Learning", "Expert Trajectory", "region:us" ]
NathanGavenski
null
null
2
438
2023-10-24T17:30:02
--- license: mit tags: - Imitation Learning - Expert Trajectory pretty_name: CartPole-v1 Expert Dataset size_categories: - 10M<n<100M --- # CartPole-v1 - Imitation Learning Datasets This is a dataset created by [Imitation Learning Datasets](https://github.com/NathanGavenski/IL-Datasets) project. It was created by using Stable Baselines weights from a PPO policy from [HuggingFace](https://huggingface.co/sb3/ppo-CartPole-v1). ## Description The dataset consists of 1,000 episodes with an average episodic reward of 500. Each entry consists of: ``` obs (list): observation with length 4. action (int): action (0 or 1). reward (float): reward point for that timestep. episode_returns (bool): if that state was the initial timestep for an episode. ``` ## Usage Feel free to download and use the `teacher.jsonl` dataset as you please. If you are interested in using our PyTorch Dataset implementation, feel free to check the [IL Datasets](https://github.com/NathanGavenski/IL-Datasets/blob/main/src/imitation_datasets/dataset/dataset.py) project. There, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace. The Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary). ## Citation Coming soon.
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brwac
2022-11-03T16:16:00.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:no-annotation", "language_creators:found", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:original", "language:pt", "license:unknown", "region:us" ]
null
The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, which was made public for research purposes. The current corpus version, released in January 2017, is composed by 3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available solely for academic research purposes, and you agreed not to use it for any commercial applications. Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC
@inproceedings{wagner2018brwac, title={The brwac corpus: A new open resource for brazilian portuguese}, author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} }
8
437
2022-03-02T23:29:22
--- annotations_creators: - no-annotation language_creators: - found language: - pt license: - unknown multilinguality: - monolingual size_categories: - 1M<n<10M source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: brwac pretty_name: BrWaC dataset_info: features: - name: doc_id dtype: string - name: title dtype: string - name: uri dtype: string - name: text sequence: - name: paragraphs sequence: string splits: - name: train num_bytes: 18828421452 num_examples: 3530796 download_size: 0 dataset_size: 18828421452 --- # Dataset Card for BrWaC ## 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:** [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - **Repository:** [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC) - **Paper:** [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/) - **Point of Contact:** [Jorge A. Wagner Filho](mailto:jawfilho@inf.ufrgs.br) ### Dataset Summary The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework, which was made public for research purposes. The current corpus version, released in January 2017, is composed by 3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available solely for academic research purposes, and you agreed not to use it for any commercial applications. Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Portuguese ## Dataset Structure ### Data Instances An example from the BrWaC dataset looks as follows: ``` { "doc_id": "netg-1afc73", "text": { "paragraphs": [ [ "Conteúdo recente" ], [ "ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\"" ], [ "31 de Agosto de 2015, 7:07 , por paulo soavinski - | No one following this article yet." ], [ "Visualizado 202 vezes" ], [ "JORNAL ELETRÔNICO DA ILHA DO MEL" ], [ "Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.", "Na faixa de areia ela aparece disseminada e não chama muito a atenção.", "No Buraco do Aipo, com muitas pedras, ela aparece concentrada.", "É fácil saber que esta espuma estranha está lá, quando venta.", "Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.", "Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site." ] ] }, "title": "ESPUMA MARROM CHAMADA ‟NINGUÉM MERECE‟ - paulo soavinski", "uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece" } ``` ### Data Fields - `doc_id`: The document ID - `title`: The document title - `uri`: URI where the document was extracted from - `text`: A list of document paragraphs (with a list of sentences in it as a list of strings) ### Data Splits The data is only split into train set with size of 3530796 samples. ## 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 ``` @inproceedings{wagner2018brwac, title={The brwac corpus: A new open resource for brazilian portuguese}, author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline}, booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year={2018} } ``` ### Contributions Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset.
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german_legal_entity_recognition
2023-01-25T14:30:49.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:n<1K", "source_datasets:original", "language:de", "license:cc-by-4.0", "region:us" ]
null
\
@inproceedings{leitner2019fine, author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider}, title = {{Fine-grained Named Entity Recognition in Legal Documents}}, booktitle = {Semantic Systems. The Power of AI and Knowledge Graphs. Proceedings of the 15th International Conference (SEMANTiCS 2019)}, year = 2019, editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria Maleshkova and Tassilo Pellegrini and Harald Sack and York Sure-Vetter}, keywords = {aip}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, number = {11702}, address = {Karlsruhe, Germany}, month = 9, note = {10/11 September 2019}, pages = {272--287}, pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
1
437
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - de license: - cc-by-4.0 multilinguality: - monolingual size_categories: - n<1K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition paperswithcode_id: legal-documents-entity-recognition pretty_name: Legal Documents Entity Recognition dataset_info: - config_name: bag features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bfh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bgh features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bpatg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bsg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverfg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: bverwg features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 - config_name: all features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': B-AN '1': B-EUN '2': B-GRT '3': B-GS '4': B-INN '5': B-LD '6': B-LDS '7': B-LIT '8': B-MRK '9': B-ORG '10': B-PER '11': B-RR '12': B-RS '13': B-ST '14': B-STR '15': B-UN '16': B-VO '17': B-VS '18': B-VT '19': I-AN '20': I-EUN '21': I-GRT '22': I-GS '23': I-INN '24': I-LD '25': I-LDS '26': I-LIT '27': I-MRK '28': I-ORG '29': I-PER '30': I-RR '31': I-RS '32': I-ST '33': I-STR '34': I-UN '35': I-VO '36': I-VS '37': I-VT '38': O splits: - name: train download_size: 4392913 dataset_size: 0 --- # Dataset Card for Legal Documents Entity Recognition ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/elenanereiss/Legal-Entity-Recognition - **Repository:** None - **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20 - **Leaderboard:** [If the dataset supports an active leaderboard, add link here]() - **Point of Contact:** Georg Rehm (georg.rehm@dfki.de) ### Dataset Summary <div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400"> <p><b>Deprecated:</b> Dataset "german_legal_entity_recognition" is deprecated and will be deleted. Use <a href="https://huggingface.co/datasets/elenanereiss/german-ler">"elenanereiss/german-ler"</a> instead.</p> </div> ### 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 [More Information Needed] #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations [More Information Needed] #### 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 [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset.
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juletxara/xquad_xtreme
2022-10-12T08:43:41.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "annotations_creators:expert-generated", "language_creators:expert-generated", "multilinguality:multilingual", "size_categories:unknown", "source_datasets:extended|squad", "language:en", "language:es", "language:de", "language:el", "language:hi", "language:th", "language:ru", "language:tr", "language:ar", "language:vi", "language:zh", "language:ro", "license:cc-by-sa-4.0", "arxiv:1910.11856", "region:us" ]
juletxara
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages. We also include "translate-train", "translate-dev", and "translate-test" splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings.
@article{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} }
5
436
2022-05-30T10:49:17
--- pretty_name: XQuAD-XTREME annotations_creators: - expert-generated language_creators: - expert-generated language: - en - es - de - el - hi - th - ru - tr - ar - vi - zh - ro license: - cc-by-sa-4.0 multilinguality: - multilingual size_categories: - unknown source_datasets: - extended|squad task_categories: - question-answering task_ids: - extractive-qa paperswithcode_id: xquad --- # Dataset Card for XQuAD-XTREME ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [https://github.com/deepmind/xquad](https://github.com/deepmind/xquad) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 139.53 MB - **Size of the generated dataset:** 18.09 MB - **Total amount of disk used:** 157.62 MB ### Dataset Summary XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten language: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi and Romanian. Consequently, the dataset is entirely parallel across 12 languages. We also include "translate-train", "translate-dev", and "translate-test" splits for each non-English language from XTREME (Hu et al., 2020). These can be used to run XQuAD in the "translate-train" or "translate-test" settings. https://proceedings.mlr.press/v119/hu20b/hu20b.pdf ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Dataset Structure ### Data Instances #### ar - **Size of downloaded dataset files:** 12.68 MB - **Size of the generated dataset:** 1.64 MB - **Total amount of disk used:** 14.33 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### de - **Size of downloaded dataset files:** 12.68 MB - **Size of the generated dataset:** 1.23 MB - **Total amount of disk used:** 13.91 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### el - **Size of downloaded dataset files:** 12.68 MB - **Size of the generated dataset:** 2.11 MB - **Total amount of disk used:** 14.79 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### en - **Size of downloaded dataset files:** 12.68 MB - **Size of the generated dataset:** 1.07 MB - **Total amount of disk used:** 13.75 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` #### es - **Size of downloaded dataset files:** 12.68 MB - **Size of the generated dataset:** 1.22 MB - **Total amount of disk used:** 13.90 MB An example of 'test' looks as follows. ``` This example was too long and was cropped: { "answers": { "answer_start": [527], "text": ["136"] }, "context": "\"Die Verteidigung der Panthers gab nur 308 Punkte ab und belegte den sechsten Platz in der Liga, während sie die NFL mit 24 Inte...", "id": "56beb4343aeaaa14008c925c", "question": "Wie viele Sacks erzielte Jared Allen in seiner Karriere?" } ``` ### Data Fields The data fields are the same among all splits. #### ar - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### de - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### el - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### en - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. #### es - `id`: a `string` feature. - `context`: a `string` feature. - `question`: a `string` feature. - `answers`: a dictionary feature containing: - `text`: a `string` feature. - `answer_start`: a `int32` feature. ### Data Splits | name | validation | | -------- | ---------: | | ar | 1190 | | de | 1190 | | el | 1190 | | en | 1190 | | es | 1190 | ## Dataset Creation ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data #### Initial Data Collection and Normalization [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the source language producers? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations #### Annotation process [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) #### Who are the annotators? [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Discussion of Biases [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Other Known Limitations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information ``` @article{Artetxe:etal:2019, author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama}, title = {On the cross-lingual transferability of monolingual representations}, journal = {CoRR}, volume = {abs/1910.11856}, year = {2019}, archivePrefix = {arXiv}, eprint = {1910.11856} } ``` ### Contributions Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset.
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iohadrubin/wikitext-103-raw-v1
2022-08-14T13:41:10.000Z
[ "region:us" ]
iohadrubin
null
null
2
435
2022-08-14T13:40:34
Entry not found
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peoples_daily_ner
2023-01-25T14:42:22.000Z
[ "task_categories:token-classification", "task_ids:named-entity-recognition", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:zh", "license:unknown", "region:us" ]
null
People's Daily NER Dataset is a commonly used dataset for Chinese NER, with text from People's Daily (人民日报), the largest official newspaper. The dataset is in BIO scheme. Entity types are: PER (person), ORG (organization) and LOC (location).
null
6
434
2022-03-02T23:29:22
--- annotations_creators: - expert-generated language_creators: - found language: - zh license: - unknown multilinguality: - monolingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - token-classification task_ids: - named-entity-recognition pretty_name: People's Daily NER dataset_info: features: - name: id dtype: string - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-PER '2': I-PER '3': B-ORG '4': I-ORG '5': B-LOC '6': I-LOC config_name: peoples_daily_ner splits: - name: train num_bytes: 14972456 num_examples: 20865 - name: validation num_bytes: 1676741 num_examples: 2319 - name: test num_bytes: 3346975 num_examples: 4637 download_size: 8385672 dataset_size: 19996172 --- # Dataset Card for People's Daily NER ## 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/OYE93/Chinese-NLP-Corpus/tree/master/NER/People's%20Daily) - **Repository:** [Github](https://github.com/OYE93/Chinese-NLP-Corpus/) - **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 No citation available for this dataset. ### Contributions Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.
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Cohere/wikipedia-22-12-simple-embeddings
2023-03-22T16:56:34.000Z
[ "task_categories:text-retrieval", "task_ids:document-retrieval", "multilinguality:multilingual", "language:en", "license:apache-2.0", "region:us" ]
Cohere
null
null
39
434
2023-01-13T23:25:25
--- language: - en multilinguality: - multilingual size_categories: [] source_datasets: [] tags: [] task_categories: - text-retrieval license: - apache-2.0 task_ids: - document-retrieval --- # Wikipedia (simple English) embedded with cohere.ai `multilingual-22-12` encoder We encoded [Wikipedia (simple English)](https://simple.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model. To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Embeddings We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/). ## Further languages We provide embeddings of Wikipedia in many different languages: [ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings), You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12). ## Loading the dataset You can either load the dataset like this: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train") ``` Or you can also stream it without downloading it before: ```python from datasets import load_dataset docs = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True) for doc in docs: docid = doc['id'] title = doc['title'] text = doc['text'] emb = doc['emb'] ``` ## Search A full search example: ```python #Run: pip install cohere datasets from datasets import load_dataset import torch import cohere co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com #Load at max 1000 documents + embeddings max_docs = 1000 docs_stream = load_dataset(f"Cohere/wikipedia-22-12-simple-embeddings", split="train", streaming=True) docs = [] doc_embeddings = [] for doc in docs_stream: docs.append(doc) doc_embeddings.append(doc['emb']) if len(docs) >= max_docs: break doc_embeddings = torch.tensor(doc_embeddings) query = 'Who founded Youtube' response = co.embed(texts=[query], model='multilingual-22-12') query_embedding = response.embeddings query_embedding = torch.tensor(query_embedding) # Compute dot score between query embedding and document embeddings dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1)) top_k = torch.topk(dot_scores, k=3) # Print results print("Query:", query) for doc_id in top_k.indices[0].tolist(): print(docs[doc_id]['title']) print(docs[doc_id]['text'], "\n") ``` ## Performance You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance)
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kaitchup/ultrachat-100k-flattened
2023-10-19T15:13:49.000Z
[ "region:us" ]
kaitchup
null
null
2
434
2023-10-19T15:07:12
--- configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 632072903 num_examples: 100000 - name: test num_bytes: 32563073 num_examples: 5140 download_size: 330831956 dataset_size: 664635976 --- # Dataset Card for "ultrachat-100k-flattened" A random sample of 100k dialogues from [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat). The dialogues are flattened into one single sequence of dialogue turns where each turn is introduced by one of the following roles: * Assistant * User This conversion and subsampling of ultrachat was made to facilitate and speed up training with HuggingFace's TRL.
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igbo_monolingual
2023-06-01T14:59:53.000Z
[ "task_categories:text-generation", "task_categories:fill-mask", "task_ids:language-modeling", "task_ids:masked-language-modeling", "annotations_creators:found", "language_creators:found", "multilinguality:monolingual", "size_categories:1K<n<10K", "size_categories:n<1K", "source_datasets:original", "language:ig", "license:unknown", "arxiv:2004.00648", "region:us" ]
null
A dataset is a collection of Monolingual Igbo sentences.
@misc{ezeani2020igboenglish, title={Igbo-English Machine Translation: An Evaluation Benchmark}, author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple}, year={2020}, eprint={2004.00648}, archivePrefix={arXiv}, primaryClass={cs.CL} }
1
433
2022-03-02T23:29:22
--- annotations_creators: - found language_creators: - found language: - ig license: - unknown multilinguality: - monolingual size_categories: - 1K<n<10K - n<1K source_datasets: - original task_categories: - text-generation - fill-mask task_ids: - language-modeling - masked-language-modeling paperswithcode_id: null pretty_name: Igbo Monolingual Dataset dataset_info: - config_name: eze_goes_to_school features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 128309 num_examples: 1 download_size: 8260947 dataset_size: 128309 - config_name: bbc-igbo features: - name: source dtype: string - name: title dtype: string - name: description dtype: string - name: date dtype: string - name: headline dtype: string - name: content dtype: string - name: tags sequence: string splits: - name: train num_bytes: 3488908 num_examples: 1297 download_size: 8260947 dataset_size: 3488908 - config_name: igbo-radio features: - name: source dtype: string - name: headline dtype: string - name: author dtype: string - name: date dtype: string - name: description dtype: string - name: content dtype: string splits: - name: train num_bytes: 1129644 num_examples: 440 download_size: 8260947 dataset_size: 1129644 - config_name: jw-ot-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 3489314 num_examples: 39 download_size: 8260947 dataset_size: 3489314 - config_name: jw-nt-igbo features: - name: format dtype: string - name: title dtype: string - name: chapters sequence: - name: title dtype: string - name: content dtype: string splits: - name: train num_bytes: 1228779 num_examples: 27 download_size: 8260947 dataset_size: 1228779 - config_name: jw-books features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 9456342 num_examples: 48 download_size: 8260947 dataset_size: 9456342 - config_name: jw-teta features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 991111 num_examples: 37 download_size: 8260947 dataset_size: 991111 - config_name: jw-ulo_nche features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 1952360 num_examples: 55 download_size: 8260947 dataset_size: 1952360 - config_name: jw-ulo_nche_naamu features: - name: title dtype: string - name: content dtype: string - name: format dtype: string - name: date dtype: string splits: - name: train num_bytes: 7248017 num_examples: 88 download_size: 8260947 dataset_size: 7248017 config_names: - bbc-igbo - eze_goes_to_school - igbo-radio - jw-books - jw-nt-igbo - jw-ot-igbo - jw-teta - jw-ulo_nche - jw-ulo_nche_naamu --- # Dataset Card for Igbo Monolingual Dataset ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_monoling - **Paper:** https://arxiv.org/abs/2004.00648 ### Dataset Summary A dataset is a collection of Monolingual Igbo sentences. ### Supported Tasks and Leaderboards [More Information Needed] ### Languages Igbo (ig) ## Dataset Structure ### Data Instances Here is an example from the bb-igbo config: ``` {'content': 'Ike Ekweremmadụ\n\nIke ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n\'ala Naijiria agwụla Ekweremmadụ\n\nOsote onye-isi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ike agwụla ndị Sịnatị iji otu nkeji darajụụ akwanyere ndị egburu n\'ime oke ọgbaghara dị na Naịjirịa oge ọ bula.\n\nEkweremadu katọrọ mwakpọ na ogbugbu ndị Naịjirịa aka ha dị ọcha nke ndị Fulani na-achị ehi mere, kwuo na ike agwụla ndị ome- iwu ịkwanyere ha ugwu n\'otu nkeji\'\n\nCheta n\'otu ịzụka gara-aga ka emere akwam ozu mmadụ ruru iri asaa egburu na Local Gọọmenti Logo na Guma nke Benue Steeti, e be ihe kariri mmadụ iri ise ka akụkọ kwuru n\'egburu na Taraba Steeti.\n\nEkweremadu gosiri iwe gbasara ogbugbu ndị mmadụ na nzukọ ndị ome-iwu n\'ụbọchị taa, kwuo na Naịjirịa ga-ebu ụzọ nwe udo na nchekwa, tupu e kwuowa okwu iwulite obodo.\n\nỌ sịrị: "Ndị ome-iwu abụghị sọ ọsọ ndị ihe a metụtara, kama ndị Naịjirịa niile.\n\n\'Ike agwụla anyị iji otu nkeji dị jụụ maka nkwanye ugwu. Ihe anyị chọrọ bụ udo na nchekwa tupu echewa echịchị nwuli obodo."', 'date': '2018-01-19T17:07:38Z', 'description': "N'ihi oke ogbugbu ndị mmadụ na Naịjirịa gbagburu gburu, osota onyeisi ndị ome-iwu Naịjirịa bụ Ike Ekweremadu ekwuola na ihe Naịjiria chọrọ bụ nchekwa tara ọchịchị, tupu ekwuwa okwu ihe ọzọ.", 'headline': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu', 'source': 'https://www.bbc.com/igbo/42712250', 'tags': [], 'title': 'Ekweremadu: Ike agwụla ndị ụlọ ome iwu'} ``` ### Data Fields For config 'eze_goes_to_school': - format, title, chapters For config 'bbc-igbo' : - source, title, description, date (Missing date values replaced with empty strings), headline, content, tags (Missing tags replaced with empty list) For config 'igbo-radio': - source, headline, author, date, description, content For config 'jw-ot-igbo': - format, title, chapters For config 'jw-nt-igbo': - format, title, chapters For config 'jw-books': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-teta': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche': - title, content, format, date (Missing date values replaced with empty strings) For config 'jw-ulo_nche_naamu': - title, content, format, date (Missing date values replaced with empty strings) ### Data Splits | bbc-igbo | eze_goes_to_school |igbo-radio| jw-books|jw-nt-igbo| jw-ot-igbo | jw-teta |jw-ulo_nche |jw-ulo_nche_naamu | ------------- |:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:|:-------------:| | 1297 | 1 | 440 | 48 | 27 | 39 | 37 | 55 | 88 ## 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 @misc{ezeani2020igboenglish, title={Igbo-English Machine Translation: An Evaluation Benchmark}, author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple}, year={2020}, eprint={2004.00648}, archivePrefix={arXiv}, primaryClass={cs.CL} } ### Contributions Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset.
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HuggingFaceH4/testing_codealpaca_small
2023-04-12T21:57:24.000Z
[ "region:us" ]
HuggingFaceH4
null
null
3
433
2023-04-12T21:57:20
--- dataset_info: features: - name: prompt dtype: string - name: completion dtype: string splits: - name: train num_bytes: 31503 num_examples: 100 - name: test num_bytes: 29802 num_examples: 100 download_size: 44006 dataset_size: 61305 --- # Dataset Card for "testing_codealpaca_small" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
458
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sordonia/my-wiki-latex_mmlu_from_valid_all
2023-10-11T01:19:27.000Z
[ "region:us" ]
sordonia
null
null
0
433
2023-10-10T20:52:48
--- dataset_info: features: - name: subject dtype: string - name: docno dtype: int64 - name: score dtype: float64 - name: dfq dtype: int64 - name: text dtype: string - name: id dtype: string - name: url dtype: string - name: title dtype: string - name: revid dtype: string splits: - name: train num_bytes: 1139620543 num_examples: 137881 download_size: 0 dataset_size: 1139620543 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for "my-wiki-latex_mmlu_from_valid_all" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
729
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vitaliy-sharandin/synthetic-fraud-detection
2023-08-24T17:17:37.000Z
[ "region:us" ]
vitaliy-sharandin
null
null
1
432
2023-08-24T17:13:00
Entry not found
15
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mteb/mind_small
2022-08-04T23:00:59.000Z
[ "region:us" ]
mteb
null
null
0
431
2022-05-30T18:34:30
The `test` split is the `validation` split of [MIND](https://msnews.github.io/). Labels for the original `test` split are unavailable. Thus, we renamed it to test for consistency in the MTEB benchmark.
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ashraq/movielens_ratings
2022-06-29T17:29:31.000Z
[ "region:us" ]
ashraq
null
null
1
430
2022-06-24T17:20:41
Entry not found
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jxie/flickr8k
2023-06-25T22:25:03.000Z
[ "region:us" ]
jxie
null
null
0
430
2023-06-25T19:09:16
--- dataset_info: features: - name: image dtype: image - name: caption_0 dtype: string - name: caption_1 dtype: string - name: caption_2 dtype: string - name: caption_3 dtype: string - name: caption_4 dtype: string splits: - name: train num_bytes: 826721431.0 num_examples: 6000 - name: validation num_bytes: 138017615.0 num_examples: 1000 - name: test num_bytes: 136871307.0 num_examples: 1000 download_size: 274629589 dataset_size: 1101610353.0 --- # Dataset Card for "flickr8k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
687
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german-nlp-group/german_common_crawl
2023-10-03T14:50:28.000Z
[ "language:de", "region:us" ]
german-nlp-group
German Only Extract from Common Crawl This Dataset is for pretraining a German Language Model (Unsupervised) or tune a Multilingual Model specifically to German
@inproceedings{wenzek2020ccnet, title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data}, author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard}, booktitle={Proceedings of The 12th Language Resources and Evaluation Conference}, pages={4003--4012}, year={2020} }
7
429
2022-03-02T23:29:22
--- language: - de --- # Dataset Card for GermanCommonCrawl ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-instances) - [Data Splits](#data-instances) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** - **Repository:** https://github.com/German-NLP-Group/german-transformer-training - **Paper:** - **Leaderboard:** - **Point of Contact:** philipp.reissel@rwth-aachen.de ### Dataset Summary German Only Extract from Common Crawl Stats: Total Size after Deduplication: 142 Mio Pages / 194 GB (Gzipped) Total Size before Deduplcation: 263 Mio Pages / 392 GB (Gzipped) ### Supported Tasks and Leaderboards This Dataset is for pretraining a German Language Model (Unsupervised). ### Languages German only (Sometimes websites are partially in another Language). One can filter these out through the `language_score` attribute. ## Dataset Structure ### Data Instances ``` {'url': 'http://my-shop.ru/shop/books/545473.html', 'date_download': '2016-10-20T19:38:58Z', 'digest': 'sha1:F62EMGYLZDIKF4UL5JZYU47KWGGUBT7T', 'length': 1155, 'nlines': 4, 'source_domain': 'my-shop.ru', 'title': 'Grammatikalische Liebeslieder. Methodische Vorschläge', 'raw_content': 'Grammatikalische Liebeslieder. [....]', 'cc_segment': 'crawl-data/CC-MAIN-2016-44/segments/1476988717783.68/wet/CC-MAIN-20161020183837-00354-ip-10-171-6-4.ec2.internal.warc.wet.gz', 'original_nlines': 99, 'original_length': 2672, 'language': 'de', 'language_score': 1.0, 'perplexity': 283.0, 'bucket': 'head'}" ``` ### Data Fields ### Data Splits Train only ## Dataset Creation ### Curation Rationale Handling and Filtering of Common Crawl Data requires large scale Server Ressources at a location in the US (for downloading speed). The total computing time needed to create this dataset is above 100k CPU hours. To give others the opportunity to train models with this dataset easily we make it publicly available. In most use cases you see an improved Model Performance when extending the pre-training Data so one can achieve highest accuracies as this is probably the largest available dataset. ### Source Data It was filtered from the Common Crawl Snapshots of the following months: 1. 2015-48 2. 2016-18 3. 2016-44 4. 2017-33 5. 2017-30 6. 2017-30 7. 2017-39 8. 2017-51 9. 2018-09 10. 2018-17 11. 2018-30 12. 2018-39 13. 2018-51 14. 2019-09 15. 2019-18 16. 2019-30 17. 2019-47 18. 2020-10 #### Initial Data Collection and Normalization Filtering and deduplication of each month seperalety was performed with [CC_Net](https://github.com/facebookresearch/cc_net). The current datasets only contains the best part (head part) with the highest text quality (see CC_Net Paper for more details). Middle and tail part may be uploaded soon as well, or are available on request. Afterwards this Dataset was deduplicated again to filter out Websites which occur in multiple monthly snapshots. This deduplication removes all Websites which have either the same url or the same hash (this is to filter out websites which are accessible under multiple domains) #### 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 ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information ``` @inproceedings{wenzek2020ccnet, title={CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data}, author={Wenzek, Guillaume and Lachaux, Marie-Anne and Conneau, Alexis and Chaudhary, Vishrav and Guzm{\'a}n, Francisco and Joulin, Armand and Grave, {\'E}douard}, booktitle={Proceedings of The 12th Language Resources and Evaluation Conference}, pages={4003--4012}, year={2020} ```
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JasiekKaczmarczyk/maestro-v1-sustain-masked
2023-10-02T10:34:44.000Z
[ "region:us" ]
JasiekKaczmarczyk
null
null
0
429
2023-10-02T08:08:58
--- dataset_info: features: - name: midi_filename dtype: string - name: source dtype: string - name: pitch sequence: int16 length: 128 - name: dstart sequence: float32 length: 128 - name: duration sequence: float32 length: 128 - name: velocity sequence: int16 length: 128 - name: masking_spaces struct: - name: <Random Mask> sequence: bool length: 128 - name: <LH Mask> sequence: bool length: 128 - name: <RH Mask> sequence: bool length: 128 - name: <Harmonic Root Mask> sequence: bool length: 128 - name: <Harmonic Outliers Mask> sequence: bool length: 128 splits: - name: train num_bytes: 86282539 num_examples: 43738 - name: validation num_bytes: 9735862 num_examples: 4931 - name: test num_bytes: 11249478 num_examples: 5695 download_size: 40330447 dataset_size: 107267879 --- # Dataset Card for "maestro-v1-sustain-masked" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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pcuenq/oxford-pets
2022-08-06T16:01:34.000Z
[ "task_categories:image-classification", "source_datasets:https://www.robots.ox.ac.uk/~vgg/data/pets/", "license:cc-by-sa-4.0", "pets", "oxford", "region:us" ]
pcuenq
null
null
5
428
2022-08-06T15:59:02
--- tags: - pets - oxford license: cc-by-sa-4.0 license_details: https://www.robots.ox.ac.uk/~vgg/data/pets/ pretty_name: Oxford-IIIT Pet Dataset (no annotations) source_datasets: https://www.robots.ox.ac.uk/~vgg/data/pets/ task_categories: - image-classification --- # Oxford-IIIT Pet Dataset Images from [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/). Only images and labels have been pushed, segmentation annotations were ignored. - **Homepage:** https://www.robots.ox.ac.uk/~vgg/data/pets/ License: Same as the original dataset.
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webis/Touche23-ValueEval
2023-05-23T20:19:40.000Z
[ "task_categories:text-classification", "task_categories:zero-shot-classification", "task_ids:multi-label-classification", "size_categories:1K<n<10K", "language:en", "license:cc-by-4.0", "Human Values", "Value Detection", "Multi-Label", "region:us" ]
webis
Dataset for Touch\u00E9 / SemEval 2023 Task 4; ValueEval: Identification of Human Values behind Arguments: https://www.overleaf.com/6679855346wrdckzkdccxg Based on the original Webis-ArgValues-22 (https://doi.org/10.5281/zenodo.5657249) dataset accompanying the paper Identifying the Human Values behind Arguments (Kiesel et al. 2022b; https://webis.de/publications.html#kiesel_2022b), published at ACL'22.
@Article{mirzakhmedova:2023a, author = {Nailia Mirzakhmedova and Johannes Kiesel and Milad Alshomary and Maximilian Heinrich and Nicolas Handkeand Xiaoni Cai and Valentin Barriere and Doratossadat Dastgheib and Omid Ghahroodi and {Mohammad Ali} Sadraeiand Ehsaneddin Asgari and Lea Kawaletz and Henning Wachsmuth and Benno Stein}, doi = {10.48550/arXiv.2301.13771}, journal = {CoRR}, month = jan, publisher = {arXiv}, title = {{The Touch{\'e}23-ValueEval Dataset for Identifying Human Values behind Arguments}}, volume = {abs/2301.13771}, year = 2023 }
3
427
2023-04-17T09:17:07
--- license: cc-by-4.0 task_categories: - text-classification - zero-shot-classification task_ids: - multi-label-classification language: - en tags: - Human Values - Value Detection - Multi-Label pretty_name: Human Value Detection Dataset size_categories: - 1K<n<10K --- # The Touch&eacute;23-ValueEval Dataset ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Dataset Usage](#dataset-usage) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Argument Instances](#argument-instances) - [Metadata Instances](#metadata-instances) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) ## Dataset Description - **Homepage:** [https://webis.de/data/touche23-valueeval.html](https://webis.de/data/touche23-valueeval.html) - **Repository:** [Zenodo](https://doi.org/10.5281/zenodo.6814563) - **Paper:** [The Touch&eacute;23-ValueEval Dataset for Identifying Human Values behind Arguments.](https://webis.de/downloads/publications/papers/mirzakhmedova_2023a.pdf) - **Leaderboard:** [https://touche.webis.de/](https://touche.webis.de/semeval23/touche23-web/index.html#results) - **Point of Contact:** [Webis Group](https://webis.de/people.html) ### Dataset Summary The Touch&eacute;23-ValueEval Dataset comprises 9324 arguments from six different sources. An arguments source is indicated with the first letter of its `Argument ID`: - `A`: [IBM-ArgQ-Rank-30kArgs](https://research.ibm.com/haifa/dept/vst/debating_data.shtml#Argument%20Quality) - `C`:Chinese question-answering website [Zhihu](https://www.zhihu.com) - `D`:[Group Discussion Ideas (GD IDEAS)](https://www.groupdiscussionideas.com) - `E`:[The Conference for the Future of Europe](https://futureu.europa.eu) - `F`:Contribution by the language.ml lab (Doratossadat, Omid, Mohammad, Ehsaneddin) [1]: arguments from the "Nahj al-Balagha" [2] and "Ghurar al-Hikam wa Durar ak-Kalim" [3] - `G`:[The New York Times](https://www.nytimes.com) The annotated labels are based on the value taxonomy published in [Identifying the Human Values behind Arguments](https://webis.de/publications.html#kiesel_2022b) (Kiesel et al. 2022) at ACL'22. [1] https://language.ml [2] https://en.wikipedia.org/wiki/Nahj_al-Balagha [3] https://en.wikipedia.org/wiki/Ghurar_al-Hikam_wa_Durar_al-Kalim ### Dataset Usage The default configuration name is `main`. ```python from datasets import load_dataset dataset = load_dataset("webis/Touche23-ValueEval") print(dataset['train'].info.description) for argument in iter(dataset['train']): print(f"{argument['Argument ID']}: {argument['Stance']} '{argument['Conclusion']}': {argument['Premise']}") ``` ### Supported Tasks and Leaderboards Human Value Detection ### Languages The [Argument Instances](#argument-instances) are all monolingual; it only includes English (mostly en-US) documents. The [Metadata Instances](#metadata-instances) for some dataset parts additionally state the arguments in their original language and phrasing. ## Dataset Structure ### Argument Instances Each argument instance has the following attributes: - `Argument ID`: The unique identifier for the argument within the dataset - `Conclusion`: Conclusion text of the argument - `Stance`: Stance of the `Premise` towards the `Conclusion; one of "in favor of", "against" - `Premise`: Premise text of the argument - `Labels`: The `Labels` for each example is an array of 1s (argument resorts to value) and 0s (argument does not resort to value). The order is the same as in the original files. Additionally, the labels are separated into *value-categories*, aka. level 2 labels of the value taxonomy (Kiesel et al. 2022b), and *human values*, aka. level 1 labels of the value taxonomy. This distinction is also reflected in the configuration names: - `<config>`: As the [Task](https://touche.webis.de/semeval23/touche23-web/) is focused mainly on the detection of value-categories, each base configuration ([listed below](#p-list-base-configs)) has the 20 value-categories as labels: ```python labels = ["Self-direction: thought", "Self-direction: action", "Stimulation", "Hedonism", "Achievement", "Power: dominance", "Power: resources", "Face", "Security: personal", "Security: societal", "Tradition", "Conformity: rules", "Conformity: interpersonal", "Humility", "Benevolence: caring", "Benevolence: dependability", "Universalism: concern", "Universalism: nature", "Universalism: tolerance", "Universalism: objectivity"] ``` - `<config>-level1`: The 54 human values from the level 1 of the value taxonomy are not used for the 2023 task (except for the annotation), but are still listed here for some might find them useful for understanding the value categories. Their order is also the same as in the original files. For more details see the [value-categories](#metadata-instances) configuration. <p id="p-list-base-configs">The configuration names (as replacements for <code>&lt;config&gt;</code>) in this dataset are:</p> - `main`: 8865 arguments (sources: `A`, `D`, `E`) with splits `train`, `validation`, and `test` (default configuration name) ```python dataset_main_train = load_dataset("webis/Touche23-ValueEval", split="train") dataset_main_validation = load_dataset("webis/Touche23-ValueEval", split="validation") dataset_main_test = load_dataset("webis/Touche23-ValueEval", split="test") ``` - `nahjalbalagha`: 279 arguments (source: `F`) with split `test` ```python dataset_nahjalbalagha_test = load_dataset("webis/Touche23-ValueEval", name="nahjalbalagha", split="test") ``` - `nyt`: 80 arguments (source: `G`) with split `test` ```python dataset_nyt_test = load_dataset("webis/Touche23-ValueEval", name="nyt", split="test") ``` - `zhihu`: 100 arguments (source: `C`) with split `validation` ```python dataset_zhihu_validation = load_dataset("webis/Touche23-ValueEval", name="zhihu", split="validation") ``` Please note that due to copyright reasons, there currently does not exist a direct download link to the arguments contained in the New york Times dataset. Accessing any of the `nyt` or `nyt-level1` configurations will therefore use the specifically created [nyt-downloader program](https://github.com/touche-webis-de/touche-code/tree/main/semeval23/human-value-detection/nyt-downloader) to create and access the arguments locally. See the program's [README](https://github.com/touche-webis-de/touche-code/blob/main/semeval23/human-value-detection/nyt-downloader/README.md) for further details. ### Metadata Instances The following lists all configuration names for metadata. Each configuration only has a single split named `meta`. - `ibm-meta`: Each row corresponds to one argument (IDs starting with `A`) from the [IBM-ArgQ-Rank-30kArgs](https://research.ibm.com/haifa/dept/vst/debating_data.shtml#Argument%20Quality) - `Argument ID`: The unique identifier for the argument - `WA`: the quality label according to the weighted-average scoring function - `MACE-P`: the quality label according to the MACE-P scoring function - `stance_WA`: the stance label according to the weighted-average scoring function - `stance_WA_conf`: the confidence in the stance label according to the weighted-average scoring function ```python dataset_ibm_metadata = load_dataset("webis/Touche23-ValueEval", name="ibm-meta", split="meta") ``` - `zhihu-meta`: Each row corresponds to one argument (IDs starting with `C`) from the Chinese question-answering website [Zhihu](https://www.zhihu.com) - `Argument ID`: The unique identifier for the argument - `Conclusion Chinese`: The original chinese conclusion statement - `Premise Chinese`: The original chinese premise statement - `URL`: Link to the original statement the argument was taken from ```python dataset_zhihu_metadata = load_dataset("webis/Touche23-ValueEval", name="zhihu-meta", split="meta") ``` - `gdi-meta`: Each row corresponds to one argument (IDs starting with `D`) from [GD IDEAS](https://www.groupdiscussionideas.com/) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the topic the argument was taken from ```python dataset_gdi_metadata = load_dataset("webis/Touche23-ValueEval", name="gdi-meta", split="meta") ``` - `cofe-meta`: Each row corresponds to one argument (IDs starting with `E`) from [the Conference for the Future of Europe](https://futureu.europa.eu) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the comment the argument was taken from ```python dataset_cofe_metadata = load_dataset("webis/Touche23-ValueEval", name="cofe-meta", split="meta") ``` - `nahjalbalagha-meta`: Each row corresponds to one argument (IDs starting with `F`). This file contains information on the 279 arguments in `nahjalbalagha` (or `nahjalbalagha-level1`) and 1047 additional arguments that were not labeled so far. This data was contributed by the language.ml lab. - `Argument ID`: The unique identifier for the argument - `Conclusion Farsi`: Conclusion text of the argument in Farsi - `Stance Farsi`: Stance of the `Premise` towards the `Conclusion`, in Farsi - `Premise Farsi`: Premise text of the argument in Farsi - `Conclusion English`: Conclusion text of the argument in English (translated from Farsi) - `Stance English`: Stance of the `Premise` towards the `Conclusion`; one of "in favor of", "against" - `Premise English`: Premise text of the argument in English (translated from Farsi) - `Source`: Source text of the argument; one of "Nahj al-Balagha", "Ghurar al-Hikam wa Durar ak-Kalim"; their Farsi translations were used - `Method`: How the premise was extracted from the source; one of "extracted" (directly taken), "deduced"; the conclusion are deduced ```python dataset_nahjalbalagha_metadata = load_dataset("webis/Touche23-ValueEval", name="nahjalbalagha-meta", split="meta") ``` - `nyt-meta`: Each row corresponds to one argument (IDs starting with `G`) from [The New York Times](https://www.nytimes.com) - `Argument ID`: The unique identifier for the argument - `URL`: Link to the article the argument was taken from - `Internet Archive timestamp`: Timestamp of the article's version in the Internet Archive that was used ```python dataset_nyt_metadata = load_dataset("webis/Touche23-ValueEval", name="nyt-meta", split="meta") ``` - `value-categories`: Contains a single JSON-entry with the structure of level 2 and level 1 values regarding the value taxonomy: ``` { "<value category>": { "<level 1 value>": [ "<exemplary effect a corresponding argument might target>", ... ], ... }, ... } ``` As this configuration contains just a single entry, an example usage could be: ```python value_categories = load_dataset("webis/Touche23-ValueEval", name="value-categories", split="meta")[0] ``` ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @Article{mirzakhmedova:2023a, author = {Nailia Mirzakhmedova and Johannes Kiesel and Milad Alshomary and Maximilian Heinrich and Nicolas Handke\ and Xiaoni Cai and Valentin Barriere and Doratossadat Dastgheib and Omid Ghahroodi and {Mohammad Ali} Sadraei\ and Ehsaneddin Asgari and Lea Kawaletz and Henning Wachsmuth and Benno Stein}, doi = {10.48550/arXiv.2301.13771}, journal = {CoRR}, month = jan, publisher = {arXiv}, title = {{The Touch{\'e}23-ValueEval Dataset for Identifying Human Values behind Arguments}}, volume = {abs/2301.13771}, year = 2023 } ```
12,059
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multi_re_qa
2023-06-01T14:59:53.000Z
[ "task_categories:question-answering", "task_ids:extractive-qa", "task_ids:open-domain-qa", "annotations_creators:expert-generated", "annotations_creators:found", "language_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:100K<n<1M", "size_categories:10K<n<100K", "size_categories:1K<n<10K", "size_categories:1M<n<10M", "source_datasets:extended|other-BioASQ", "source_datasets:extended|other-DuoRC", "source_datasets:extended|other-HotpotQA", "source_datasets:extended|other-Natural-Questions", "source_datasets:extended|other-Relation-Extraction", "source_datasets:extended|other-SQuAD", "source_datasets:extended|other-SearchQA", "source_datasets:extended|other-TextbookQA", "source_datasets:extended|other-TriviaQA", "language:en", "license:unknown", "arxiv:2005.02507", "region:us" ]
null
MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, including BioASQ, RelationExtraction, TextbookQA, contain only the test data
@misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} }
0
425
2022-03-02T23:29:22
--- annotations_creators: - expert-generated - found language_creators: - expert-generated - found language: - en license: - unknown multilinguality: - monolingual size_categories: - 100K<n<1M - 10K<n<100K - 1K<n<10K - 1M<n<10M source_datasets: - extended|other-BioASQ - extended|other-DuoRC - extended|other-HotpotQA - extended|other-Natural-Questions - extended|other-Relation-Extraction - extended|other-SQuAD - extended|other-SearchQA - extended|other-TextbookQA - extended|other-TriviaQA task_categories: - question-answering task_ids: - extractive-qa - open-domain-qa paperswithcode_id: multireqa pretty_name: MultiReQA dataset_info: - config_name: SearchQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 183902877 num_examples: 3163801 - name: validation num_bytes: 26439174 num_examples: 454836 download_size: 36991959 dataset_size: 210342051 - config_name: TriviaQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 107326326 num_examples: 1893674 - name: validation num_bytes: 13508062 num_examples: 238339 download_size: 21750402 dataset_size: 120834388 - config_name: HotpotQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 29516866 num_examples: 508879 - name: validation num_bytes: 3027229 num_examples: 52191 download_size: 6343389 dataset_size: 32544095 - config_name: SQuAD features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 16828974 num_examples: 95659 - name: validation num_bytes: 2012997 num_examples: 10642 download_size: 3003646 dataset_size: 18841971 - config_name: NaturalQuestions features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: train num_bytes: 28732767 num_examples: 448355 - name: validation num_bytes: 1418124 num_examples: 22118 download_size: 6124487 dataset_size: 30150891 - config_name: BioASQ features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 766190 num_examples: 14158 download_size: 156649 dataset_size: 766190 - config_name: RelationExtraction features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 217870 num_examples: 3301 download_size: 73019 dataset_size: 217870 - config_name: TextbookQA features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 4182675 num_examples: 71147 download_size: 704602 dataset_size: 4182675 - config_name: DuoRC features: - name: candidate_id dtype: string - name: response_start dtype: int32 - name: response_end dtype: int32 splits: - name: test num_bytes: 1483518 num_examples: 5525 download_size: 97625 dataset_size: 1483518 config_names: - BioASQ - DuoRC - HotpotQA - NaturalQuestions - RelationExtraction - SQuAD - SearchQA - TextbookQA - TriviaQA --- # Dataset Card for MultiReQA ## Table of Contents - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://github.com/google-research-datasets/MultiReQA - **Repository:** https://github.com/google-research-datasets/MultiReQA - **Paper:** https://arxiv.org/pdf/2005.02507.pdf - **Leaderboard:** - **Point of Contact:** ### Dataset Summary MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation) ### Supported Tasks and Leaderboards - Question answering (QA) - Retrieval question answering (ReQA) ### Languages Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC ## Dataset Structure ### Data Instances The general format is: ` { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ... ` An example from SearchQA: `{'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243} ` ### Data Fields ` { "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ... ` - **candidate_id:** The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task. - **response_start:** The start index of the sentence with respect to its original context. - **response_end:** The end index of the sentence with respect to its original context ### Data Splits Train and Dev splits are available only for the following datasets, - SearchQA - TriviaQA - HotpotQA - SQuAD - NaturalQuestions Test splits are available only for the following datasets, - BioASQ - RelationExtraction - TextbookQA The number of candidate sentences for each dataset in the table below. | | MultiReQA | | |--------------------|-----------|---------| | | train | test | | SearchQA | 629,160 | 454,836 | | TriviaQA | 335,659 | 238,339 | | HotpotQA | 104,973 | 52,191 | | SQuAD | 87,133 | 10,642 | | NaturalQuestions | 106,521 | 22,118 | | BioASQ | - | 14,158 | | RelationExtraction | - | 3,301 | | TextbookQA | - | 3,701 | ## Dataset Creation ### Curation Rationale MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the [MRQA shared task](https://mrqa.github.io/). The dataset was curated by converting existing QA datasets from [MRQA shared task](https://mrqa.github.io/) to the format of MultiReQA benchmark. ### Source Data #### Initial Data Collection and Normalization The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark. #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators The annotators/curators of the dataset are [mandyguo-xyguo](https://github.com/mandyguo-xyguo) and [mwurts4google](https://github.com/mwurts4google), the contributors of the official MultiReQA github repository ### Licensing Information [More Information Needed] ### Citation Information ``` @misc{m2020multireqa, title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models}, author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant}, year={2020}, eprint={2005.02507}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ### Contributions Thanks to [@Karthik-Bhaskar](https://github.com/Karthik-Bhaskar) for adding this dataset.
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