Classify text data into Negative, Neutral & Positive. Trained on the financial-classification dataset.

Classify text data as NonSarcastic vs. Sarcastic. Trained on the ar_sarcasm dataset.

Classify text data into Africa, Asia, Australia, and 71 other labels. Trained on the bbc_news_march_2023 dataset.

Classify text data as Female vs. Male. Trained on the blog_authorship_corpus dataset.

Classify text data into Administració, Aeronàutica, Agricultura, and 64 other labels. Trained on the CaWikiTC dataset.

Classify text data into Business, Entertainment, Health, and 3 other labels. Trained on the CNN_News_Articles_2011-2022 dataset.

Classify text data as Negative vs. Positive. Trained on the IMDB sentiments dataset.

Classify image data into Anger, Contempt, Disgust, and 5 other labels. Trained on the visual-emotional-analysis dataset.

Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.

Classify text data into BarelyTrue, False, HalfTrue, and 3 other labels. Trained on the liar dataset.

Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-en dataset.

Classify image data as False vs. True. Trained on the openfire dataset.

Classify image data into Ad, Ae, Ar, and 53 other labels. Trained on the random_streetview_images_pano_v0.0.2 dataset.

Classify text data as Irony vs. NonIrony. Trained on the tweet_eval dataset.

Classify image data into Cloud, Other & Smoke. Trained on the smokedataset dataset.

Classify text data into Abbr:abb, Abbr:exp, Desc:def, and 47 other labels. Trained on the TREC dataset.

Classify image data into AngularLeafSpot, BeanRust & Healthy. Trained on the NaCRRI_beans dataset.

Classify text data into ☀, 🇺🇸, ✨, and 17 other labels. Trained on the tweet_eval dataset.

Classify text data into 1.0, 2.0, 3.0, and 2 other labels. Trained on the allegro_reviews dataset.

Classify text data as Fremskrittspartiet vs. Sosialistiskvenstreparti. Trained on the notram dataset.

Classify text data into Etat, Inland, International, and 6 other labels. Trained on the 10kGNAD dataset.

Classify image data into Arborio, Basmati, Ipsala, and 2 other labels. Trained on the rice_image dataset.

Classify image data as Bokeh vs. No Bokeh. Trained on the depth-of-field dataset.

Classify text data as Medical vs. Not Medical. Trained on the medicalquestions dataset.

Classify text data into Bisnis, Bola, News, and 2 other labels. Trained on the indonews dataset.

Classify text data into False, Mixture, True & Unproven. Trained on the health_fact dataset.

Classify image data into AbstractExpressionism, ActionPainting, AnalyticalCubism, and 24 other labels. Trained on the wikiart dataset.

Classify image data into Chicken, Dog & Muffin. Trained on the dog_food dataset.

Classify text data into Neutral, Opportunity & Risk. Trained on the climate_sentiment dataset.

Classify image data into Burger, ButterNaan, Chai, and 17 other labels. Trained on the indian_food_images dataset.

Classify image data into Ak47, Awp, Famas, and 8 other labels. Trained on the csgo-weapon-classification dataset.

Classify text data into Anger, Fear, Joy, and 3 other labels. Trained on the emotion dataset.

Classify text data into 1 star, 2 star, 3 stars, and 2 other labels. Trained on the yelp_review_full dataset.

Classify image data as 3dPrinted vs. Not3dPrinted. Trained on the 3d-printed-or-not dataset.

Classify image data into "coltsfoot", Alpineseaholly, Anthurium, and 99 other labels. Trained on the oxford-flowers dataset.

Classify image data into Banded, Blotchy, Braided, and 44 other labels. Trained on the DTD dataset.

Classify text data into Chemistry;metallurgy, Electricity, Fixedconstructions, and 6 other labels. Trained on the BIGPATENT dataset.

Classify image data into Calling, Clapping, Cycling, and 12 other labels. Trained on the HAR dataset.

Classify image data into Ancistrus, Apistogramma, Astyanax, and 30 other labels. Trained on the amazonian_fish_classifier_data dataset.

Classify text data into Affordable and Clean Energy, Clean Water and Sanitation, Climate Action, and 12 other labels. Trained on the OSGD-CD dataset.

Classify text data into Achats, Activationdesventes, Administrationscolaire, and 220 other labels. Trained on the product-sheets-in-french dataset.

Classify image data into Milddemented, Moderatedemented, Nondemented & Verymilddemented. Trained on the Alzheimer dataset.

Classify image data as Cordillera vs. Glaciar. Trained on the rock-glacier-detection dataset.

Classify text data into Mixed, Negative, NoImpact & Positive. Trained on the poem_sentiment dataset.

Classify text data as Not Toxic vs. Toxic. Trained on the Nyckel curated dataset.

Classify image data into Afghanistan, Åland Islands, Albania, and 208 other labels. Trained on the country211 dataset.

Classify text data into Angry, Happy, Others & Sad. Trained on the EmoContext dataset.

Classify image data into Apple, Banana, Cake, and 17 other labels. Trained on the snacks dataset.

Classify text data into Negative, OffTopic & Positive. Trained on the hebrew_sentiment dataset.

Classify image data into Background, ChinoImpuro, Cuishe, and 6 other labels. Trained on the spirit-patterns dataset.

Classify text data into Album, Animal, Artist, and 11 other labels. Trained on the ontology dataset.

Classify text data into Baseet, Hazaj, Kamel, and 11 other labels. Trained on the metrec dataset.

Classify image data into Fabric, Foliage, Glass, and 7 other labels. Trained on the FMD dataset.

Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.

Classify image data into Adidas, Converse & Nike. Trained on the shoe-classification dataset.

Classify text data into Afya, Burudani, Kimataifa, and 3 other labels. Trained on the swahili_news dataset.

Classify image data into Bean, BitterGourd, BottleGourd, and 12 other labels. Trained on the Vegetable dataset.

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.

Classify image data as Clean vs. Garbage. Trained on the garbage_detection dataset.

Classify image data into 20eurobill, Africanherbs, Aniseed(stars), and 247 other labels. Trained on the ALOT dataset.

Classify text data into 0/5, 1/5, 2/5, and 3 other labels. Trained on the google_wellformed_query dataset.

Classify image data into Healthy, Leafspot & Otherdisease. Trained on the strawberry-disease dataset.

Classify text data as DrugRelatedAdverseEffect vs. NoDrugRelatedAdverseEffect. Trained on the ade_corpus_v2 dataset.

Classify text data as Clickbait vs. Not Clickbait. Trained on the clickbait_notclickbait_dataset dataset.

Classify image data into R15, R18 & Safe. Trained on the anime_rating dataset.

Classify image data as Fake vs. Real. Trained on the kodIIm_14 dataset.

Classify text data as False vs. True. Trained on the hyperpartisan_news_detection dataset.

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.

Classify text data into Negativ, Neutral & Positiv. Trained on the europarl dataset.

Classify text data as GenderBias vs. NoGenderBias. Trained on the kor_hate dataset.

Classify image data into Amongus, Apexlegends, Fortnite, and 7 other labels. Trained on the gameplay-images dataset.

Classify text data into Business, Sci/tech, Sports & World. Trained on the Nyckel curated dataset.

Classify text data as Negative vs. Positive. Trained on the allocine dataset.

Classify image data as Control vs. Sick. Trained on the MyoQuant-SDH dataset.

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.

Classify text data into Command, Fragment, IntonationDependentutterance, and 4 other labels. Trained on the kor_3i4k dataset.

Classify text data into AlarmQuery, AlarmRemove, AlarmSet, and 65 other labels. Trained on the NLU-Evaluation-Data dataset.

Classify image data as After vs. Before. Trained on the botox-injections-before-and-after dataset.

Classify image data into Abyssinian, Americanbulldog, Americanpitbullterrier, and 34 other labels. Trained on the Oxford-IIIT-pets dataset.

Classify image data as Cat vs. Dog. Trained on the cats_and_dogs dataset.

Classify image data into Beautiful, Exceptional, Kawaii & Perfect. Trained on the Artificial-super-girlfriend-for-fine-tuning dataset.

Classify text data into Abuse, Insult, Other & Profanity. Trained on the ro-offense dataset.

Classify image data as Man vs. Woman. Trained on the GenderClassify dataset.

Classify text data as Legitimate vs. Spam. Trained on the turkishSMS-ds dataset.

Classify image data into 03011205, 03026206, 0305908, and 99 other labels. Trained on the topex-printer dataset.

Classify text data as Depression vs. NotDepression. Trained on the depression-reddit-cleaned dataset.

Classify text data as Acceptable vs. Unacceptable. Trained on the CoLA dataset.

Classify image data into Annualcrop, Forest, Herbaceousvegetation, and 7 other labels. Trained on the EuroSAT dataset.

Classify image data as Biting vs. NoBiting. Trained on the nailbiting_classification dataset.

Classify image data into Ambiguous, Closeup, Detail, and 5 other labels. Trained on the types-of-film-shots dataset.

Classify text data as NonOffensive vs. Offensive. Trained on the tweet_eval dataset.

Classify text data into 1 star, 2 star, 3 star, and 2 other labels. Trained on the labr dataset.

Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-hr dataset.

Classify image data into Barretts, BarrettsShortSegment, Bbps01, and 20 other labels. Trained on the HyperKvasir dataset.

Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.

Classify image data into Airplane, Airport, Baseballdiamond, and 42 other labels. Trained on the NWPU-RESISC45 dataset.

Classify text data as Negative vs. Positive. Trained on the swedish_reviews dataset.

Classify image data into FakeImages, Images & RealImages. Trained on the Fake or Real: AI-generated Image Discrimination Competition dataset.

Classify text data as Hate vs. NoHate. Trained on the hate_speech_portuguese dataset.

Classify text data as NotToxic vs. Toxic. Trained on the told-br dataset.

Classify text data as No vs. Yes. Trained on the climate_commitments_actions dataset.

Classify image data into Aluminium, Batteries, Cardboard, and 8 other labels. Trained on the recycling dataset.

Classify image data into N01443537, N01629819, N01641577, and 197 other labels. Trained on the tiny_imagenet dataset.

Classify text data into Genderabusive, Geopolitical, Personal, and 2 other labels. Trained on the bn_hate_speech dataset.

Classify image data into Bakedpotato, Burger, Crispychicken, and 7 other labels. Trained on the fast_food_image_classification dataset.

Classify image data into Cs, Ecs, Fs, and 2 other labels. Trained on the ShotType dataset.

Classify text data into Business, Sci/tech, Sports & World. Trained on the ag_news dataset.

Classify text data into Background, Method & Result. Trained on the scicite dataset.

Classify text data as Negative vs. Positive. Trained on the DBRD dataset.

Classify image data into Casĩakiraphan, Casĩanhthơ, Casĩbằngkiều, and 221 other labels. Trained on the face-celeb-vietnamese dataset.

Classify image data into Agricultural, Airplane, Baseballdiamond, and 18 other labels. Trained on the UCMerced-landuse dataset.

Classify text data into Aindaécedoparadizer, Deolho, Discutível, and 11 other labels. Trained on the factckbr dataset.

Classify text data into Negative, Neutral & Positive. Trained on the tweet_eval dataset.

Classify text data into Entertainment, India, International, and 3 other labels. Trained on the bbc_hindi_nli dataset.

Classify text data into Hate, Idk/skip, Nohate & Relation. Trained on the hate-speech-dataset dataset.

Classify text data into Analyst Update, Company | Product News, Currencies, and 17 other labels. Trained on the twitter-financial-news-topic dataset.

Classify image data into Agriculture, Commercial, Harbor, and 9 other labels. Trained on the SIRI-WHU dataset.

Classify image data into 0, 1, 2 & 3. Trained on the Dog_Emotion_Dataset_v2 dataset.

Classify text data as Acceptable vs. Offensive. Trained on the FRENK-hate-sl dataset.

Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.

Classify text data as Attack vs. Normal. Trained on the Malicious_packets dataset.

Classify image data as Croissant vs. Donut. Trained on the donut-vs-croissant dataset.

Classify text data into Bongkim, HumayunAhmed, ManikBandhopaddhay, and 13 other labels. Trained on the BAAD16 dataset.

Classify image data as NoWatermark vs. Watermark. Trained on the scenery_watermarks dataset.

Classify text data as Negative vs. Positive. Trained on the rotten_tomatoes dataset.

Classify image data as Benign vs. Invasive Ductal Carcinoma. Trained on the breast-histopathology-images dataset.

Classify image data into Abra, Aerodactyl, Alakazam, and 147 other labels. Trained on the pokedex dataset.

Classify text data as Hate vs. NonHate. Trained on the tweet_eval dataset.

Classify text data into 1 star, 2 stars, 3 stars, and 2 other labels. Trained on the HARD dataset.

Classify text data into Bearish, Bullish & Neutral. Trained on the twitter-financial-news-sentiment dataset.

Classify image data into Elf, Goblin, Knight & Zombie. Trained on the croupier-mtg dataset.

Classify text data into Against, Favor & None. Trained on the tweet_eval dataset.

Classify text data as Not Sarcastic vs. Sarcastic. Trained on the sarcastic_news_headlines dataset.

Classify text data into Administration, Agriculture, Agroalimentaire, and 185 other labels. Trained on the QR-AN dataset.

Classify image data as Negative vs. Positive. Trained on the rendered-sst2 dataset.

Classify text data into Business, Entertainment, Politic & Sport. Trained on the myanmar_news dataset.

Classify text data into Abuse, Insult, Other & Profanity. Trained on the ro-fb-offense dataset.

Classify image data as 0 vs. 1. Trained on the memes-1500 dataset.

Classify text data as Notspam vs. Spam. Trained on the email-spam-classification dataset.

Classify text data as NonSpecific vs. Specific. Trained on the climate_specificity dataset.

Classify image data as NoWeapon vs. Weapon. Trained on the artificial_weapon dataset.

Classify image data into AraucariaExcelsa, ChineseFanPalm, ChineseHackberry, and 12 other labels. Trained on the NTU-Tree dataset.

Classify text data into Bo, Dolent, Moltbo, and 2 other labels. Trained on the GuiaCat dataset.

Classify text data as Negative vs. Positive. Trained on the Stanford Sentiment Treebank dataset.

Classify image data into AirportInside, Artstudio, Auditorium, and 64 other labels. Trained on the MIT-scenes dataset.

Classify text data into Governance, Metrics, None, and 2 other labels. Trained on the tcfd_recommendations dataset.

Classify text data into Commissive, Directive, Inform & Question. Trained on the SILICONE dataset.

Classify image data into Advertisement, Budget, Email, and 13 other labels. Trained on the RVL-CDIP dataset.

Classify text data as Ham vs. Spam. Trained on the Nyckel curated dataset.

Classify text data into Cs.ai, Cs.ce, Cs.cv, and 8 other labels. Trained on the arxiv-classification dataset.

Classify image data into MildDemented, ModerateDemented, NonDemented & VeryMildDemented. Trained on the Dementia dataset.

Classify text data into Background, Conclusions, Methods, and 2 other labels. Trained on the pubmed-20k-rct dataset.

Classify text data into ActivateMyCard, AgeLimit, ApplePayOrGooglePay, and 74 other labels. Trained on the banking77 dataset.

Classify image data into ApplePie, BabyBackRibs, Baklava, and 98 other labels. Trained on the food101 dataset.

Classify image data into Ain, Al, Aleff, and 29 other labels. Trained on the ArASL-database-grayscale dataset.

Classify image data into AlbrechtDurer, AlekseySavrasov, AlfredSisley, and 126 other labels. Trained on the wikiart dataset.

Classify text data as Hate vs. NoHate. Trained on the hate_speech_filipino dataset.

Classify image data into AbstractPainting, Cityscape, GenrePainting, and 8 other labels. Trained on the wikiart dataset.

Classify text data as BezpiecznePostanowienieUmowne vs. KlauzulaAbuzywna. Trained on the abusive-clauses-pl dataset.

Classify text data into Acq, Crude, Earn, and 5 other labels. Trained on the reuters-21578 dataset.

Classify text data as Ham vs. Spam. Trained on the sms_spam dataset.