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Pretrained Functions
Get started right now with a pretrained model curated by the Nyckel team. These can get you going immediately, without having to bring your own data. Go from 0 to using an AI model in seconds.
Financial News Titles
Classify text data into Negative, Neutral & Positive. Trained on the financial-classification dataset.
Arabian Tweets
Classify text data as NonSarcastic vs. Sarcastic. Trained on the ar_sarcasm dataset.
BBC News Articles
Classify text data into Africa, Asia, Australia, and 71 other labels. Trained on the bbc_news_march_2023 dataset.
Gendered Blog Posts
Classify text data as Female vs. Male. Trained on the blog_authorship_corpus dataset.
Catalan Wikipedia Texts
Classify text data into Administració, Aeronàutica, Agricultura, and 64 other labels. Trained on the CaWikiTC dataset.
CNN News Articles
Classify text data into Business, Entertainment, Health, and 3 other labels. Trained on the CNN_News_Articles_2011-2022 dataset.
IMDB Movie Reviews
Classify text data as Negative vs. Positive. Trained on the IMDB sentiments dataset.
Human Face Emotions
Classify image data into Anger, Contempt, Disgust, and 5 other labels. Trained on the visual-emotional-analysis dataset.
Abortion Stance Tweets
Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.
Politifact Statements
Classify text data into BarelyTrue, False, HalfTrue, and 3 other labels. Trained on the liar dataset.
English Migrant & LBGT News Facebook Comments
Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-en dataset.
Wildfires
Classify image data as False vs. True. Trained on the openfire dataset.
Street Views
Classify image data into Ad, Ae, Ar, and 53 other labels. Trained on the random_streetview_images_pano_v0.0.2 dataset.
Ironic Tweets
Classify text data as Irony vs. NonIrony. Trained on the tweet_eval dataset.
Smoky Skies
Classify image data into Cloud, Other & Smoke. Trained on the smokedataset dataset.
Questions
Classify text data into Abbr:abb, Abbr:exp, Desc:def, and 47 other labels. Trained on the TREC dataset.
Bean Plant Diseases
Classify image data into AngularLeafSpot, BeanRust & Healthy. Trained on the NaCRRI_beans dataset.
Tweet Emoji Reactions
Classify text data into ☀, 🇺🇸, ✨, and 17 other labels. Trained on the tweet_eval dataset.
Polish Product Reviews
Classify text data into 1.0, 2.0, 3.0, and 2 other labels. Trained on the allegro_reviews dataset.
Norwegian Parliament Speeches
Classify text data as Fremskrittspartiet vs. Sosialistiskvenstreparti. Trained on the notram dataset.
German News Articles
Classify text data into Etat, Inland, International, and 6 other labels. Trained on the 10kGNAD dataset.
Rice Grains
Classify image data into Arborio, Basmati, Ipsala, and 2 other labels. Trained on the rice_image dataset.
Depths of Field
Classify image data as Bokeh vs. No Bokeh. Trained on the depth-of-field dataset.
Medical Questions
Classify text data as Medical vs. Not Medical. Trained on the medicalquestions dataset.
Indonesian News
Classify text data into Bisnis, Bola, News, and 2 other labels. Trained on the indonews dataset.
Questionable Health Claims
Classify text data into False, Mixture, True & Unproven. Trained on the health_fact dataset.
Art Styles
Classify image data into AbstractExpressionism, ActionPainting, AnalyticalCubism, and 24 other labels. Trained on the wikiart dataset.
Chickens vs Muffins vs Dogs
Classify image data into Chicken, Dog & Muffin. Trained on the dog_food dataset.
Corporate Climate Disclosure Opportunities
Classify text data into Neutral, Opportunity & Risk. Trained on the climate_sentiment dataset.
Indian Foods
Classify image data into Burger, ButterNaan, Chai, and 17 other labels. Trained on the indian_food_images dataset.
CSGO Weapons
Classify image data into Ak47, Awp, Famas, and 8 other labels. Trained on the csgo-weapon-classification dataset.
Tweet Emotions
Classify text data into Anger, Fear, Joy, and 3 other labels. Trained on the emotion dataset.
Yelp Reviews
Classify text data into 1 star, 2 star, 3 stars, and 2 other labels. Trained on the yelp_review_full dataset.
3d Printed Prototypes
Classify image data as 3dPrinted vs. Not3dPrinted. Trained on the 3d-printed-or-not dataset.
Flowers
Classify image data into "coltsfoot", Alpineseaholly, Anthurium, and 99 other labels. Trained on the oxford-flowers dataset.
Describable Textures
Classify image data into Banded, Blotchy, Braided, and 44 other labels. Trained on the DTD dataset.
US Patents
Classify text data into Chemistry;metallurgy, Electricity, Fixedconstructions, and 6 other labels. Trained on the BIGPATENT dataset.
Human Actions
Classify image data into Calling, Clapping, Cycling, and 12 other labels. Trained on the HAR dataset.
Amazonian Fishes
Classify image data into Ancistrus, Apistogramma, Astyanax, and 30 other labels. Trained on the amazonian_fish_classifier_data dataset.
Sustainable Development Goals
Classify text data into Affordable and Clean Energy, Clean Water and Sanitation, Climate Action, and 12 other labels. Trained on the OSGD-CD dataset.
French Product Sheets
Classify text data into Achats, Activationdesventes, Administrationscolaire, and 220 other labels. Trained on the product-sheets-in-french dataset.
Alzheimer Brain Scans
Classify image data into Milddemented, Moderatedemented, Nondemented & Verymilddemented. Trained on the Alzheimer dataset.
Rocks vs Glaciers
Classify image data as Cordillera vs. Glaciar. Trained on the rock-glacier-detection dataset.
Poems
Classify text data into Mixed, Negative, NoImpact & Positive. Trained on the poem_sentiment dataset.
Social Media Toxicity (Demo)
Classify text data as Not Toxic vs. Toxic. Trained on the Nyckel curated dataset.
Countries
Classify image data into Afghanistan, Åland Islands, Albania, and 208 other labels. Trained on the country211 dataset.
SMS Emotions
Classify text data into Angry, Happy, Others & Sad. Trained on the EmoContext dataset.
Snacks
Classify image data into Apple, Banana, Cake, and 17 other labels. Trained on the snacks dataset.
Hebrew President Facebook Post Comments
Classify text data into Negative, OffTopic & Positive. Trained on the hebrew_sentiment dataset.
Evaporated Spirit Drops
Classify image data into Background, ChinoImpuro, Cuishe, and 6 other labels. Trained on the spirit-patterns dataset.
DBpedia Articles
Classify text data into Album, Animal, Artist, and 11 other labels. Trained on the ontology dataset.
Arabic Poetry Meters
Classify text data into Baseet, Hazaj, Kamel, and 11 other labels. Trained on the metrec dataset.
Materials
Classify image data into Fabric, Foliage, Glass, and 7 other labels. Trained on the FMD dataset.
Feminism Stance Tweets
Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.
Sneakers
Classify image data into Adidas, Converse & Nike. Trained on the shoe-classification dataset.
Swahili News
Classify text data into Afya, Burudani, Kimataifa, and 3 other labels. Trained on the swahili_news dataset.
Vegetables
Classify image data into Bean, BitterGourd, BottleGourd, and 12 other labels. Trained on the Vegetable dataset.
Tree Species
Classify image data into Acaciaauriculiformis耳果相思, Acaciaconfusamerr.台灣相思, Acaciamangiumwilld.大葉相思, and 38 other labels. Trained on the TreeHK40 dataset.
Curbside Wastebags
Classify image data as Clean vs. Garbage. Trained on the garbage_detection dataset.
Rough Textures
Classify image data into 20eurobill, Africanherbs, Aniseed(stars), and 247 other labels. Trained on the ALOT dataset.
Wellformed Google Queries
Classify text data into 0/5, 1/5, 2/5, and 3 other labels. Trained on the google_wellformed_query dataset.
Strawberry Plant Diseases
Classify image data into Healthy, Leafspot & Otherdisease. Trained on the strawberry-disease dataset.
Medical Case Reports
Classify text data as DrugRelatedAdverseEffect vs. NoDrugRelatedAdverseEffect. Trained on the ade_corpus_v2 dataset.
Clickbaity Headlines
Classify text data as Clickbait vs. Not Clickbait. Trained on the clickbait_notclickbait_dataset dataset.
Anime Ratings
Classify image data into R15, R18 & Safe. Trained on the anime_rating dataset.
AI Generated Images
Classify image data as Fake vs. Real. Trained on the kodIIm_14 dataset.
Hyperpartisan News
Classify text data as False vs. True. Trained on the hyperpartisan_news_detection dataset.
Music Emotions
Classify image data into High Valance High Arousal, High Valence Low Arousal, Low Valance High Arousal & Low Valence Low Arousal. Trained on the emopia_mel dataset.
EU Parliament Danish Texts
Classify text data into Negativ, Neutral & Positiv. Trained on the europarl dataset.
Korean Entertaiment News Comments
Classify text data as GenderBias vs. NoGenderBias. Trained on the kor_hate dataset.
Videogame Gameplays
Classify image data into Amongus, Apexlegends, Fortnite, and 7 other labels. Trained on the gameplay-images dataset.
News Articles (Demo)
Classify text data into Business, Sci/tech, Sports & World. Trained on the Nyckel curated dataset.
French Movie Reviews
Classify text data as Negative vs. Positive. Trained on the allocine dataset.
Muscle Fibers
Classify image data as Control vs. Sick. Trained on the MyoQuant-SDH dataset.
Endometriosis Lesions
Classify image data into 6.1.1.1EndoPeritoneum, 6.1.1.2EndoOvar, 6.1.1.3EndoTie, and 2 other labels. Trained on the GLENDA dataset.
Korean Utterance Intents
Classify text data into Command, Fragment, IntonationDependentutterance, and 4 other labels. Trained on the kor_3i4k dataset.
Home Assistant Commands
Classify text data into AlarmQuery, AlarmRemove, AlarmSet, and 65 other labels. Trained on the NLU-Evaluation-Data dataset.
Botox Treated Faces
Classify image data as After vs. Before. Trained on the botox-injections-before-and-after dataset.
Pet Breeds
Classify image data into Abyssinian, Americanbulldog, Americanpitbullterrier, and 34 other labels. Trained on the Oxford-IIIT-pets dataset.
Cats vs Dogs
Classify image data as Cat vs. Dog. Trained on the cats_and_dogs dataset.
Artificial Girlfriends
Classify image data into Beautiful, Exceptional, Kawaii & Perfect. Trained on the Artificial-super-girlfriend-for-fine-tuning dataset.
Romainian Sports News
Classify text data into Abuse, Insult, Other & Profanity. Trained on the ro-offense dataset.
Gendered Faces
Classify image data as Man vs. Woman. Trained on the GenderClassify dataset.
Turkish SMS
Classify text data as Legitimate vs. Spam. Trained on the turkishSMS-ds dataset.
Machine Parts
Classify image data into 03011205, 03026206, 0305908, and 99 other labels. Trained on the topex-printer dataset.
Depression Related Reddit Posts
Classify text data as Depression vs. NotDepression. Trained on the depression-reddit-cleaned dataset.
Correct English Sentences
Classify text data as Acceptable vs. Unacceptable. Trained on the CoLA dataset.
European Satellite Landscapes
Classify image data into Annualcrop, Forest, Herbaceousvegetation, and 7 other labels. Trained on the EuroSAT dataset.
Nail Biting
Classify image data as Biting vs. NoBiting. Trained on the nailbiting_classification dataset.
Movie Shot Types
Classify image data into Ambiguous, Closeup, Detail, and 5 other labels. Trained on the types-of-film-shots dataset.
Offensive Tweets
Classify text data as NonOffensive vs. Offensive. Trained on the tweet_eval dataset.
Arabian Book Reviews
Classify text data into 1 star, 2 star, 3 star, and 2 other labels. Trained on the labr dataset.
Hungarian Migrant & LBGT News Facebook Comments
Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-hr dataset.
Gastrointestinal Endoscopy
Classify image data into Barretts, BarrettsShortSegment, Bbps01, and 20 other labels. Trained on the HyperKvasir dataset.
Hillary Clinton Stance Tweets
Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.
RESISC Aerial Landscapes
Classify image data into Airplane, Airport, Baseballdiamond, and 42 other labels. Trained on the NWPU-RESISC45 dataset.
Swedish Website Reviews
Classify text data as Negative vs. Positive. Trained on the swedish_reviews dataset.
Fake Or Real
Classify image data into FakeImages, Images & RealImages. Trained on the Fake or Real: AI-generated Image Discrimination Competition dataset.
Portuguese Hate Speech
Classify text data as Hate vs. NoHate. Trained on the hate_speech_portuguese dataset.
Brazilian Tweets
Classify text data as NotToxic vs. Toxic. Trained on the told-br dataset.
Corporate Climate Disclosure Actions
Classify text data as No vs. Yes. Trained on the climate_commitments_actions dataset.
Recycling Materials
Classify image data into Aluminium, Batteries, Cardboard, and 8 other labels. Trained on the recycling dataset.
Tiny ImageNet
Classify image data into N01443537, N01629819, N01641577, and 197 other labels. Trained on the tiny_imagenet dataset.
Bengali Hate Speech
Classify text data into Genderabusive, Geopolitical, Personal, and 2 other labels. Trained on the bn_hate_speech dataset.
Fast-Foods
Classify image data into Bakedpotato, Burger, Crispychicken, and 7 other labels. Trained on the fast_food_image_classification dataset.
Movie Shot Scales
Classify image data into Cs, Ecs, Fs, and 2 other labels. Trained on the ShotType dataset.
News Articles
Classify text data into Business, Sci/tech, Sports & World. Trained on the ag_news dataset.
Academic Citation Intents
Classify text data into Background, Method & Result. Trained on the scicite dataset.
German Book Reviews
Classify text data as Negative vs. Positive. Trained on the DBRD dataset.
Vietnamese Celebrities
Classify image data into Casĩakiraphan, Casĩanhthơ, Casĩbằngkiều, and 221 other labels. Trained on the face-celeb-vietnamese dataset.
Merced Aerial Landscapes
Classify image data into Agricultural, Airplane, Baseballdiamond, and 18 other labels. Trained on the UCMerced-landuse dataset.
Portuguese Fake News
Classify text data into Aindaécedoparadizer, Deolho, Discutível, and 11 other labels. Trained on the factckbr dataset.
Tweet Sentiments
Classify text data into Negative, Neutral & Positive. Trained on the tweet_eval dataset.
Hindi BBC News
Classify text data into Entertainment, India, International, and 3 other labels. Trained on the bbc_hindi_nli dataset.
White Supremacist Hate Speech
Classify text data into Hate, Idk/skip, Nohate & Relation. Trained on the hate-speech-dataset dataset.
Twitter Financial News Topics
Classify text data into Analyst Update, Company | Product News, Currencies, and 17 other labels. Trained on the twitter-financial-news-topic dataset.
SIRI-WHU Aerial Landscapes
Classify image data into Agriculture, Commercial, Harbor, and 9 other labels. Trained on the SIRI-WHU dataset.
Dog Emotions
Classify image data into 0, 1, 2 & 3. Trained on the Dog_Emotion_Dataset_v2 dataset.
Slovenian Migrant & LBGT News Facebook Comments
Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-sl dataset.
Climate Change Stance Tweets
Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.
Hex & Ascii Payloads
Classify text data as Attack vs. Normal. Trained on the Malicious_packets dataset.
Donuts vs Croissants
Classify image data as Croissant vs. Donut. Trained on the donut-vs-croissant dataset.
Bengali Authors
Classify text data into Bongkim, HumayunAhmed, ManikBandhopaddhay, and 13 other labels. Trained on the BAAD16 dataset.
Watermarked Scenes
Classify image data as NoWatermark vs. Watermark. Trained on the scenery_watermarks dataset.
Rotten Tomatoes Movie Reviews
Classify text data as Negative vs. Positive. Trained on the rotten_tomatoes dataset.
Breast Histopathology
Classify image data as Benign vs. Invasive Ductal Carcinoma. Trained on the breast-histopathology-images dataset.
Pokemon Characters
Classify image data into Abra, Aerodactyl, Alakazam, and 147 other labels. Trained on the pokedex dataset.
Hate Tweets
Classify text data as Hate vs. NonHate. Trained on the tweet_eval dataset.
Arabic Hotel Reviews
Classify text data into 1 star, 2 stars, 3 stars, and 2 other labels. Trained on the HARD dataset.
Twitter Financial News
Classify text data into Bearish, Bullish & Neutral. Trained on the twitter-financial-news-sentiment dataset.
Magic The Gathering Cards
Classify image data into Elf, Goblin, Knight & Zombie. Trained on the croupier-mtg dataset.
Atheism Stance Tweets
Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.
Sarcastic Headlines
Classify text data as Not Sarcastic vs. Sarcastic. Trained on the sarcastic_news_headlines dataset.
French Parliament QA
Classify text data into Administration, Agriculture, Agroalimentaire, and 185 other labels. Trained on the QR-AN dataset.
Scanned Sentiments
Classify image data as Negative vs. Positive. Trained on the rendered-sst2 dataset.
Myanmar News
Classify text data into Business, Entertainment, Politic & Sport. Trained on the myanmar_news dataset.
Romainian Facebook Comments
Classify text data into Abuse, Insult, Other & Profanity. Trained on the ro-fb-offense dataset.
Memes
Classify image data as 0 vs. 1. Trained on the memes-1500 dataset.
Spammy Emails
Classify text data as Notspam vs. Spam. Trained on the email-spam-classification dataset.
Corporate Climate Disclosure Specificity
Classify text data as NonSpecific vs. Specific. Trained on the climate_specificity dataset.
Artificial Weapons
Classify image data as NoWeapon vs. Weapon. Trained on the artificial_weapon dataset.
Asian Trees
Classify image data into AraucariaExcelsa, ChineseFanPalm, ChineseHackberry, and 12 other labels. Trained on the NTU-Tree dataset.
Catalan Restaurant Reviews
Classify text data into Bo, Dolent, Moltbo, and 2 other labels. Trained on the GuiaCat dataset.
Movie Review Sentences
Classify text data as Negative vs. Positive. Trained on the Stanford Sentiment Treebank dataset.
Indoor Scenes
Classify image data into AirportInside, Artstudio, Auditorium, and 64 other labels. Trained on the MIT-scenes dataset.
TCFD recommendations
Classify text data into Governance, Metrics, None, and 2 other labels. Trained on the tcfd_recommendations dataset.
Spoken Dialogues
Classify text data into Commissive, Directive, Inform & Question. Trained on the SILICONE dataset.
Document Types
Classify image data into Advertisement, Budget, Email, and 13 other labels. Trained on the RVL-CDIP dataset.
SMS Spam (Demo)
Classify text data as Ham vs. Spam. Trained on the Nyckel curated dataset.
ArXiv Papers
Classify text data into Cs.ai, Cs.ce, Cs.cv, and 8 other labels. Trained on the arxiv-classification dataset.
Dementia Brain Scans
Classify image data into MildDemented, ModerateDemented, NonDemented & VeryMildDemented. Trained on the Dementia dataset.
Pubmed Sections
Classify text data into Background, Conclusions, Methods, and 2 other labels. Trained on the pubmed-20k-rct dataset.
Banking Customer Queries
Classify text data into ActivateMyCard, AgeLimit, ApplePayOrGooglePay, and 74 other labels. Trained on the banking77 dataset.
Meals
Classify image data into ApplePie, BabyBackRibs, Baklava, and 98 other labels. Trained on the food101 dataset.
Arabic Sign Language Signs
Classify image data into Ain, Al, Aleff, and 29 other labels. Trained on the ArASL-database-grayscale dataset.
Artists
Classify image data into AlbrechtDurer, AlekseySavrasov, AlfredSisley, and 126 other labels. Trained on the wikiart dataset.
Filipino Hate Tweets
Classify text data as Hate vs. NoHate. Trained on the hate_speech_filipino dataset.
Art Genres
Classify image data into AbstractPainting, Cityscape, GenrePainting, and 8 other labels. Trained on the wikiart dataset.
Polish Unfair Contractual Terms
Classify text data as BezpiecznePostanowienieUmowne vs. KlauzulaAbuzywna. Trained on the abusive-clauses-pl dataset.
Reuters News Articles
Classify text data into Acq, Crude, Earn, and 5 other labels. Trained on the reuters-21578 dataset.
SMS Spam
Classify text data as Ham vs. Spam. Trained on the sms_spam dataset.