Text classification - IMDB Dataset¶

['assin',
 'ar_res_reviews',
 'ambig_qa',
 'bianet',
 'ag_news',
 'ajgt_twitter_ar',
 'aeslc',
 'bc2gm_corpus',
 'air_dialogue',
 'acronym_identification',
 'afrikaans_ner_corpus',
 'allegro_reviews',
 'ade_corpus_v2',
 'adversarial_qa',
 'alt',
 'billsum',
 'amazon_polarity',
 'amttl',
 'ascent_kb',
 'big_patent',
 'bn_hate_speech',
 'bswac',
 'capes',
 'arsentd_lev',
 'bible_para',
 'amazon_reviews_multi',
 'ai2_arc',
 'beans',
 'anli',
 'arabic_pos_dialect',
 'best2009',
 'boolq',
 'ar_sarcasm',
 'asnq',
 'bnl_newspapers',
 'amazon_us_reviews',
 'arxiv_dataset',
 'banking77',
 'bookcorpus',
 'bprec',
 'c3',
 'allocine',
 'aslg_pc12',
 'biosses',
 'bbc_hindi_nli',
 'americas_nli',
 'biomrc',
 'blimp',
 'aqua_rat',
 'aquamuse',
 'ar_cov19',
 'arabic_speech_corpus',
 'arcd',
 'blended_skill_talk',
 'cail2018',
 'blbooksgenre',
 'bookcorpusopen',
 'bsd_ja_en',
 'atomic',
 'autshumato',
 'blog_authorship_corpus',
 'caner',
 'app_reviews',
 'arabic_billion_words',
 'art',
 'asset',
 'blbooks',
 'brwac',
 'c4',
 'ami',
 'assin2',
 'babi_qa',
 'bbaw_egyptian',
 'bing_coronavirus_query_set',
 'break_data',
 'chr_en',
 'covid_qa_deepset',
 'cifar10',
 'cifar100',
 'cdsc',
 'catalonia_independence',
 'circa',
 'cc100',
 'cawac',
 'clue',
 'cdt',
 'codah',
 'casino',
 'clickbait_news_bg',
 'cc_news',
 'ccaligned_multilingual',
 'cedr',
 'code_x_glue_cc_cloze_testing_maxmin',
 'code_x_glue_cc_code_refinement',
 'conv_ai',
 'conv_ai_3',
 'conv_questions',
 'covost2',
 'cmrc2018',
 'commonsense_qa',
 'code_x_glue_ct_code_to_text',
 'conceptnet5',
 'cord19',
 'cnn_dailymail',
 'code_x_glue_cc_code_to_code_trans',
 'com_qa',
 'covid_tweets_japanese',
 'civil_comments',
 'code_x_glue_tc_text_to_code',
 'coqa',
 'cosmos_qa',
 'cfq',
 'code_x_glue_cc_clone_detection_poj104',
 'cos_e',
 'craigslist_bargains',
 'cats_vs_dogs',
 'common_voice',
 'cbt',
 'code_search_net',
 'climate_fever',
 'code_x_glue_cc_clone_detection_big_clone_bench',
 'clinc_oos',
 'code_x_glue_tc_nl_code_search_adv',
 'cmu_hinglish_dog',
 'code_x_glue_tt_text_to_text',
 'coached_conv_pref',
 'cppe-5',
 'cornell_movie_dialog',
 'coarse_discourse',
 'code_x_glue_cc_cloze_testing_all',
 'code_x_glue_cc_code_completion_token',
 'code_x_glue_cc_defect_detection',
 'competition_math',
 'conllpp',
 'covid_qa_castorini',
 'common_gen',
 'common_language',
 'compguesswhat',
 'conll2003',
 'code_x_glue_cc_code_completion_line',
 'consumer-finance-complaints',
 'conv_ai_2',
 'counter',
 'conll2000',
 'conll2002',
 'covid_qa_ucsd',
 'crawl_domain',
 'dbrd',
 'dart',
 'daily_dialog',
 'curiosity_dialogs',
 'cryptonite',
 'crd3',
 'fashion_mnist',
 'danish_political_comments',
 'ecb',
 'docred',
 'doc2dial',
 'crime_and_punish',
 'crows_pairs',
 'dane',
 'electricity_load_diagrams',
 'eli5',
 'empathetic_dialogues',
 'europa_ecdc_tm',
 'cuad',
 'datacommons_factcheck',
 'definite_pronoun_resolution',
 'dream',
 'emo',
 'ehealth_kd',
 'emotone_ar',
 'fquad',
 'eli5_category',
 'europarl_bilingual',
 'fake_news_english',
 'gem',
 'discovery',
 'dialog_re',
 'dutch_social',
 'emea',
 'enriched_web_nlg',
 'factckbr',
 'finer',
 'food101',
 'duorc',
 'e2e_nlg',
 'eu_regulatory_ir',
 'event2Mind',
 'exams',
 'fever',
 'deal_or_no_dialog',
 'cs_restaurants',
 'disfl_qa',
 'esnli',
 'dyk',
 'eth_py150_open',
 'ecthr_cases',
 'euronews',
 'evidence_infer_treatment',
 'flores',
 'flue',
 'eduge',
 'emotion',
 'eraser_multi_rc',
 'farsi_news',
 'gap',
 'dengue_filipino',
 'diplomacy_detection',
 'drop',
 'e2e_nlg_cleaned',
 'dbpedia_14',
 'disaster_response_messages',
 'eitb_parcc',
 'discofuse',
 'ethos',
 'doqa',
 'fake_news_filipino',
 'eurlex',
 'freebase_qa',
 'few_rel',
 'financial_phrasebank',
 'europa_eac_tm',
 'harem',
 'gigaword',
 'glue',
 'hatexplain',
 'hellaswag',
 'has_part',
 'great_code',
 'gutenberg_time',
 'generics_kb',
 'giga_fren',
 'go_emotions',
 'generated_reviews_enth',
 'gooaq',
 'hate_speech_offensive',
 'german_legal_entity_recognition',
 'glucose',
 'jigsaw_unintended_bias',
 'lama',
 'mbpp',
 'md_gender_bias',
 'med_hop',
 'ms_terms',
 'newspop',
 'pass',
 'proto_qa',
 'ptb_text_only',
 'ronec',
 'samsum',
 'sanskrit_classic',
 'scan',
 'sepedi_ner',
 'social_bias_frames',
 'spider',
 'tashkeela',
 'time_dial',
 'germaner',
 'germeval_14',
 'hate_speech_filipino',
 'hausa_voa_ner',
 'hebrew_this_world',
 'id_nergrit_corpus',
 'inquisitive_qg',
 'kilt_tasks',
 'kor_ner',
 'labr',
 'laroseda',
 'lj_speech',
 'math_qa',
 'multi_nli',
 'mutual_friends',
 'mwsc',
 'narrativeqa_manual',
 'natural_questions',
 'nkjp-ner',
 'norne',
 'numer_sense',
 'ollie',
 'onestop_qa',
 'opus_ubuntu',
 'para_crawl',
 'per_sent',
 'polyglot_ner',
 'quora',
 'riddle_sense',
 'smartdata',
 'snips_built_in_intents',
 'squad_it',
 'ted_iwlst2013',
 'thaiqa_squad',
 'google_wellformed_query',
 'hate_speech_portuguese',
 'id_newspapers_2018',
 'hotpot_qa',
 'id_panl_bppt',
 'interpress_news_category_tr',
 'kan_hope',
 'journalists_questions',
 'kor_sae',
 'lince',
 'masakhaner',
 'm_lama',
 'mocha',
 'medal',
 'newsph',
 'mkb',
 'opus_montenegrinsubs',
 'muchocine',
 'parsinlu_reading_comprehension',
 'myanmar_news',
 'paws-x',
 'ncbi_disease',
 'ro_sts',
 'norwegian_ner',
 'scielo',
 'opus100',
 'scitldr',
 'opus_books',
 'senti_ws',
 'sharc',
 'opus_finlex',
 'orange_sum',
 'sick',
 'piaf',
 'tatoeba',
 'qa_zre',
 'the_pile',
 'qangaroo',
 'tilde_model',
 'sem_eval_2014_task_1',
 'times_of_india_news_headlines',
 'sharc_modified',
 'siswati_ner_corpus',
 'sms_spam',
 'sofc_materials_articles',
 'spc',
 'sst',
 'stsb_mt_sv',
 'swda',
 'swedish_medical_ner',
 'hate_speech_pl',
 'hausa_voa_topics',
 'hope_edi',
 'imdb_urdu_reviews',
 'jfleg',
 'kor_3i4k',
 'kor_nli',
 'lm1b',
 'mac_morpho',
 'mdd',
 'msr_zhen_translation_parity',
 'newsroom',
 'numeric_fused_head',
 'openslr',
 'pec',
 'poleval2019_mt',
 'py_ast',
 'qed_amara',
 'refresd',
 'squadshifts',
 'superb',
 'svhn',
 'swag',
 'tab_fact',
 'tamilmixsentiment',
 'text2log',
 'the_pile_books3',
 'humicroedit',
 'irc_disentangle',
 'klue',
 'lener_br',
 'liar',
 'liveqa',
 'menyo20k_mt',
 'movie_rationales',
 'opus_fiskmo',
 'peer_read',
 'pib',
 'qasc',
 'quac',
 'ro_sent',
 's2orc',
 'sem_eval_2018_task_1',
 'sogou_news',
 'spanish_billion_words',
 'super_glue',
 'ted_multi',
 'hansards',
 'hard',
 'hate_offensive',
 'indic_glue',
 'kor_nlu',
 'makhzan',
 'math_dataset',
 'matinf',
 'mlsum',
 'msra_ner',
 'nlu_evaluation_data',
 'ohsumed',
 'openwebtext',
 'opus_elhuyar',
 'persian_ner',
 'poem_sentiment',
 'prachathai67k',
 'psc',
 'pubmed_qa',
 'quartz',
 're_dial',
 'rotten_tomatoes',
 'scene_parse_150',
 'schema_guided_dstc8',
 'sciq',
 'scitail',
 'swahili',
 'thai_toxicity_tweet',
 'the_pile_openwebtext2',
 'hendrycks_test',
 'hippocorpus',
 'hrwac',
 'hyperpartisan_news_detection',
 'imppres',
 'large_spanish_corpus',
 'multi_booked',
 'opus_paracrawl',
 'paws',
 'pn_summary',
 'quail',
 'red_caps',
 'ropes',
 'simple_questions_v2',
 'squad_adversarial',
 'squad_es',
 'squad_kor_v2',
 'story_cloze',
 'swahili_news',
 'taskmaster1',
 'tep_en_fa_para',
 'hindi_discourse',
 'gnad10',
 'hda_nli_hindi',
 'imdb',
 'hebrew_sentiment',
 'lambada',
 'id_clickbait',
 'lc_quad',
 'id_puisi',
 'lst20',
 'isixhosa_ner_corpus',
 'iwslt2017',
 'mc4',
 'jnlpba',
 'kde4',
 'multi_eurlex',
 'multi_para_crawl',
 'kor_hate',
 'multi_re_qa',
 'kor_qpair',
 'multi_woz_v22',
 'linnaeus',
 'oclar',
 'mrqa',
 'offenseval_dravidian',
 'msr_text_compression',
 'ofis_publik',
 'multilingual_librispeech',
 'omp',
 'neural_code_search',
 'openai_humaneval',
 'offcombr',
 'php',
 'opus_dogc',
 'reasoning_bg',
 'poleval2019_cyberbullying',
 'scicite',
 'qasper',
 'sem_eval_2010_task_8',
 'race',
 'sem_eval_2020_task_11',
 'scifact',
 'sesotho_ner_corpus',
 'setimes',
 'setswana_ner_corpus',
 'species_800',
 'style_change_detection',
 'subjqa',
 'tanzil',
 'taskmaster2',
 'the_pile_stack_exchange',
 'hlgd',
 'greek_legal_code',
 'hrenwac_para',
 'iapp_wiki_qa_squad',
 'guardian_authorship',
 'id_liputan6',
 'hate_speech18',
 'head_qa',
 'igbo_english_machine_translation',
 'hover',
 'isizulu_ner_corpus',
 'jeopardy',
 'kannada_news',
 'ms_marco',
 'limit',
 'msr_genomics_kbcomp',
 'mc_taco',
 'mt_eng_vietnamese',
 'norec',
 'multi_news',
 'opus_dgt',
 'multi_nli_mismatch',
 'opus_infopankki',
 'multi_x_science_sum',
 'polemo2',
 'narrativeqa',
 'quarel',
 'quoref',
 'ncslgr',
 'reddit_tifu',
 'openbookqa',
 'roman_urdu',
 'opus_euconst',
 'peoples_daily_ner',
 'silicone',
 'polsum',
 'snow_simplified_japanese_corpus',
 'so_stacksample',
 'pubmed',
 'social_i_qa',
 'reuters21578',
 'saudinewsnet',
 'stsb_multi_mt',
 'thaisum',
 'sent_comp',
 'squad_v2',
 'srwac',
 'tapaco',
 'taskmaster3',
 'ted_talks_iwslt',
 'health_fact',
 'ilist',
 'kd_conv',
 'librispeech_asr',
 'librispeech_lm',
 'metrec',
 'mkqa',
 'mlqa',
 'multidoc2dial',
 'news_commentary',
 'newsph_nli',
 'opus_rf',
 'opus_tedtalks',
 'opus_wikipedia',
 'oscar',
 'pg19',
 'qanta',
 'russian_super_glue',
 'senti_lex',
 'sentiment140',
 'squad',
 'squad_v1_pt',
 'stereoset',
 'swiss_judgment_prediction',
 'telugu_books',
 'hind_encorp',
 'hkcancor',
 'hybrid_qa',
 'indonli',
 'kilt_wikipedia',
 'kor_sarcasm',
 'medical_questions_pairs',
 'moroco',
 'nell',
 'newsgroup',
 'nli_tr',
 'offenseval2020_tr',
 'onestop_english',
 'open_subtitles',
 'opinosis',
 'opus_gnome',
 'para_pat',
 'qa_srl',
 'reclor',
 'reddit',
 'sberquad',
 'scb_mt_enth_2020',
 'search_qa',
 'sede',
 'snli',
 'speech_commands',
 'swedish_ner_corpus',
 'swedish_reviews',
 'telugu_news',
 'hans',
 'igbo_ner',
 'interpress_news_category_tr_lite',
 'jigsaw_toxicity_pred',
 'kinnews_kirnews',
 'lex_glue',
 'medical_dialog',
 'miam',
 'msr_sqa',
 'nsmc',
 'opus_memat',
 'opus_openoffice',
 'piqa',
 'pragmeval',
 'qa4mre',
 'qed',
 'selqa',
 'squad_kor_v1',
 'thainer',
 'grail_qa',
 'hebrew_projectbenyehuda',
 'igbo_monolingual',
 'indonlu',
 'kelm',
 'meta_woz',
 'metooma',
 'mnist',
 'nchlt',
 'newsqa',
 'nq_open',
 'opus_xhosanavy',
 'recipe_nlg',
 'ro_sts_parallel',
 'scientific_papers',
 'ted_hrlr',
 'timit_asr',
 'un_ga',
 'totto',
 'tlc',
 'wiki_hop',
 'trec',
 'tweets_ar_en_parallel',
 'told-br',
 'tmu_gfm_dataset',
 'udhr',
 'tsac',
 'ubuntu_dialogs_corpus',
 'tuple_ie',
 'turku_ner_corpus',
 'urdu_fake_news',
 'wiki_bio',
 'wiki_source',
 'wikiann',
 'wili_2018',
 'wmt18',
 'x_stance',
 'trivia_qa',
 'ttc4900',
 'twi_wordsim353',
 'wiki_split',
 'wmt16',
 'xglue',
 'xquad',
 'xsum',
 'vivos',
 'wiki_movies',
 'wikihow',
 'wisesight_sentiment',
 'xquad_r',
 'yoruba_bbc_topics',
 'turkish_ner',
 'tweet_qa',
 'wiki_qa',
 'wisesight1000',
 'wmt_t2t',
 'wnut_17',
 'wrbsc',
 'xcsr',
 'xnli',
 'turk',
 'twi_text_c3',
 'un_pc',
 'universal_dependencies',
 'weibo_ner',
 'wiki_qa_ar',
 'wikipedia',
 'tiny_shakespeare',
 'wiki_auto',
 'wiki_lingua',
 'wikisql',
 'wmt14',
 'wmt20_mlqe_task3',
 'turkish_shrinked_ner',
 'tydiqa',
 'universal_morphologies',
 'wikicorpus',
 'wikitext',
 'turkish_movie_sentiment',
 'turkish_product_reviews',
 'tweet_eval',
 'wiki_snippets',
 'wino_bias',
 'winogrande',
 'wongnai_reviews',
 'woz_dialogue',
 'xcopa',
 'yelp_polarity',
 'zest',
 'um005',
 'urdu_sentiment_corpus',
 'web_of_science',
 'winograd_wsc',
 'wmt17',
 'yahoo_answers_topics',
 'turkic_xwmt',
 'tweets_hate_speech_detection',
 'vctk',
 'wiki40b',
 'wiki_asp',
 'wmt15',
 'wmt20_mlqe_task2',
 'xor_tydi_qa',
 'xsum_factuality',
 'xtreme',
 'yahoo_answers_qa',
 'wiki_dpr',
 'wiqa',
 'yoruba_text_c3',
 'yoruba_wordsim353',
 'tunizi',
 'web_nlg',
 'wider_face',
 'wiki_atomic_edits',
 'wiki_summary',
 'xed_en_fi',
 'youtube_caption_corrections',
 'wi_locness',
 'wikitext_tl39',
 'wmt20_mlqe_task1',
 'yelp_review_full',
 'un_multi',
 'web_questions',
 'wmt19',
 'yoruba_gv_ner',
 'AConsApart/anime_subtitles_DialoGPT',
 'AI-it/korean-hate-speech',
 'Abirate/french_book_reviews',
 'ARTeLab/fanpage',
 '0n1xus/pytorrent-standalone',
 '0n1xus/codexglue',
 'ASCCCCCCCC/amazon_zh_simple',
 'AlgoveraAI/CryptoPunks',
 'Adnan/Urdu_News_Headlines',
 'ARKseal/YFCC14M_subset_webdataset',
 'Abdo1Kamr/Arabic_Hadith',
 'Akshith/aa',
 'AndrewMcDowell/de_corpora_parliament_processed',
 'AHussain0418/day2_data',
 'AlekseyKorshuk/horror-scripts',
 'AI-Sweden/SuperLim',
 'AI-it/khs_service_test',
 'ARTeLab/mlsum-it',
 'ARTeLab/ilpost',
 'Abirate/code_net_dev_dataset',
 'AhmadSawal/qa',
 'Akshith/g_rock',
 'Aisha/BAAD16',
 'AhmedSSoliman/CoNaLa',
 'Aisha/BAAD6',
 'AlexMaclean/wikipedia-deletion-compressions',
 'AlexZapolskii/zapolskii-amazon',
 'ASCCCCCCCC/amazon_zh',
 'Akila/ForgottenRealmsWikiDataset',
 'AHussain0418/day4data',
 'AdWeeb/DravidianMT',
 'AlekseyDorkin/extended_tweet_emojis',
 'Aliseyfi/event_token_type',
 'Akshith/test',
 'Alvenir/nst-da-16khz',
 'AHussain0418/demo_data',
 'Abirate/english_quotes',
 'Abirate/code_net_dataset',
 'Abirate/code_net_test_final_dataset',
 'AlexMaclean/all-deletion-compressions',
 'AlekseyKorshuk/comedy-scripts',
 'Annabelleabbott/real-fake-news-workshop',
 'Anurag-Singh-creator/tasks',
 'BSC-TeMU/ancora-ca-ner',
 'Binbin/my_dataset',
 'DDSC/europarl',
 'DanL/scientific-challenges-and-directions-dataset',
 'Datatang/accented_english',
 'DoyyingFace/github-embeddings-doy',
 'GEM/CrossWOZ',
 'Atsushi/fungi_diagnostic_chars_comparison_japanese',
 'Cyberfish/text_error_correction',
 'Datatang/multi_language',
 'Emma121/testtest',
 'Enes3774/data',
 'FRTNX/cosuju',
 'CodedotAI/code_clippy',
 'DDSC/reddit-da-asr-preprocessed',
 'DeskDown/ALTDataset',
 'Anurag-Singh-creator/task',
 'Baybars/parla_text_corpus',
 'Bosio/pacman_descriptions',
 'Check/region_7',
 'Check/region_9',
 'Check/vverify',
 'Cyberfish/pos_tagger',
 'Chuu/Vhh',
 'Exr0n/wiki-entity-similarity',
 'DelgadoPanadero/Pokemon',
 'DiFronzo/Human_Activity_Recognition',
 'ESZER/H',
 'Firoj/CrisisBench',
 'Fraser/python-state-changes',
 'Arnold/hausa_common_voice',
 'BSC-TeMU/sts-ca',
 'BeIR/beir',
 'Annielytics/DoctorsNotes',
 'DrishtiSharma/as_opus100_processed',
 'DrishtiSharma/br_opus100_processed',
 'EMBO/biolang',
 'Finnish-NLP/mc4_fi_cleaned',
 'Francois/futures_es',
 'BSC-TeMU/viquiquad',
 'BritishLibraryLabs/EThOS-PhD-metadata',
 'Check/region_8',
 'BeIR/beir-corpus',
 'Crives/haha',
 'BSC-TeMU/SQAC',
 'Check/a_re_gi',
 'Chun/dataset',
 'DDSC/dkhate',
 'DDSC/partial-danish-gigaword-no-twitter',
 'DSCI511G1/COP26_Energy_Transition_Tweets',
 'Daniele/dante-corpus',
 'Darren/data',
 'Datatang/mixed_speech_chinese_english',
 'DDSC/angry-tweets',
 'Check/region_3',
 'DDSC/reddit-da',
 'Check/region_6',
 'ChristophSchuhmann/MS_COCO_2017_URL_TEXT',
 'Davlan/masakhanerV1',
 'Doohae/modern_music_re',
 'CyranoB/polarity',
 'DrishtiSharma/kk_opus100_processed',
 'Dumiiii/common-voice-romaniarss',
 'DeskDown/ALTDataset_en-to-fil-vi-id-ms-ja-khm',
 'DoyyingFace/github-issues-doy',
 'Fraser/mnist-text-no-spaces',
 'Fraser/mnist-text-small',
 'Fraser/short-jokes',
 'GEM/ART',
 'Emanuel/UD_Portuguese-Bosque',
 'Fraser/mnist-text-default',
 'Fraser/python-lines',
 'BenjaminGalliot/pangloss',
 'CAGER/rick',
 'CALM/arwiki',
 'Check/region_1',
 'CodedotAI/code_clippy_github',
 'Cropinky/flatearther',
 'Datatang/multi_language_conversation',
 'DoctorSlimm/yipee',
 'FRTNX/worldbank-projects',
 'Fraser/wiki_sentences',
 'BlakesOrb6/Fred-Flintstone',
 'CShorten/KerasBERT',
 'Davlan/conll2003_de_noMISC',
 'Dmitriy612/1',
 'DrishtiSharma/bg_opus100_processed',
 'DrishtiSharma/hi_opus100_processed',
 'DrishtiSharma/or_opus100_processed',
 'Eymen3455/xsum_tr',
 'GEM/OrangeSum',
 'ApiInferenceTest/asr_dummy',
 'CShorten/ZillowPrize',
 'CodedotAI/code-clippy-tfrecords',
 'Cropinky/rap_lyrics_english',
 'DDSC/twitter-sent',
 'Davlan/conll2003_noMISC',
 'Atsushi/fungi_trait_circus_database',
 'AryanLala/autonlp-data-Scientific_Title_Generator',
 'Atsushi/fungi_indexed_mycological_papers_japanese',
 'BSC-TeMU/tecla',
 'Babelscape/wikineural',
 'BatuhanYilmaz/github-issues',
 'Check/region_2',
 'Check/region_4',
 'Check/regions',
 'Cropinky/wow_fishing_bobber',
 'Datatang/accented_mandarin',
 'DELith/github-issues',
 'Datatang/mandarin_chinese',
 'DrishtiSharma/sl_opus100_processed',
 'Doohae/klue-mrc-bm25',
 'Emon/sobuj',
 'Fraser/program-synthesis',
 'Bosio/pacman',
 'CAiRE/ASCEND',
 'Champion/vpc2020_clear_anon_speech',
 'Check/region_5',
 'Cheranga/test',
 'Datatang/chinese_dialect',
 'DrishtiSharma/mr_opus100_processed',
 'FIG-Loneliness/FIG-Loneliness',
 'Felix-ML/quoteli3',
 'Fraser/news-category-dataset',
 'Avishekavi/Avi',
 'BSC-TeMU/xquad-ca',
 'Babelscape/rebel-dataset',
 'ChadxxxxHall/Inter-vision',
 'DDSC/lcc',
 'DrishtiSharma/sr_opus100_processed',
 'EMBO/sd-nlp',
 'FL33TW00D/test-dataset',
 'GEM/BiSECT',
 'GEM/conversational_weather',
 'GEM/e2e_nlg',
 'GEM/cochrane-simplification',
 'GEM/common_gen',
 'GEM/dart',
 'GEM/totto',
 'GEM/wiki_lingua',
 'GEM/Taskmaster',
 'GEM/RiSAWOZ',
 'GEM/surface_realisation_st_2020',
 'GEM/turku_hockey_data2text',
 'GEM/indonlg',
 'GEM/SIMPITIKI',
 'GEM/schema_guided_dialog',
 'GEM/dstc10_track2_task2',
 'GalacticAI/Noirset',
 'Graphcore/vqa',
 'HarleyQ/WitcherDialogue',
 'HenryAI/KerasCodeExamples.txt',
 'Jikiwa/demo3',
 'Jikiwa/test-16344349440339',
 'Jikiwa/test-16344361893586',
 'KBLab/overlim',
 'GEM/cs_restaurants',
 'Graphcore/gqa-lxmert',
 'Graphcore/wikipedia-bert-128',
 'HenryAI/KerasAPIReference.txt',
 'HenryAI/KerasDeveloperGuides.txt',
 'Jack0508/demo',
 'Jikiwa/temp-repo-valid',
 'KETI-AIR/kor_corpora',
 'GEM/viggo',
 'GEM/xlsum',
 'GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645559101',
 'GroNLP/ik-nlp-22_slp',
 'HHousen/ParaSCI',
 'HUPD/hupd',
 'Harveenchadha/indic-voice',
 'HarveyBWest/mybot',
 'Jikiwa/stargazers',
 'Jikiwa/test-16344368182003',
 'Karavet/ARPA-Armenian-Paraphrase-Corpus',
 'GEM/mlb_data_to_text',
 'GonzaloA/fake_news',
 'IsaacBot/GP-Sentiment',
 'Jack0508/test',
 'Jikiwa/random_repo',
 'Gabriel/quora_swe',
 'Husain/intent-classification-en-fr',
 'Intel/WEC-Eng',
 'Ishwar/Senti',
 'JIWON/nil_dataset',
 'Jikiwa/demo2',
 'Jikiwa/test-16336477963335',
 'Jikiwa/test-16344362261113',
 'KTH/speechdat',
 'GEM/opusparcus',
 'GroNLP/ik-nlp-22_winemag',
 'HHousen/quora',
 'Jikiwa/test-16336486877862',
 'Jikiwa/test-16344347234752',
 'JustinE/Test',
 'GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915',
 'HHousen/msrp',
 'IFSTalfredoswald/MBTI',
 'Jack0508/vi-ko-TED-txt',
 'Jean-Baptiste/wikiner_fr',
 'Jikiwa/demo1',
 'KETI-AIR/klue',
 'Khanoooo/autonlp-data-Corona',
 'GEM/RotoWire_English-German',
 'GEM/SciDuet',
 'Jikiwa/pushedd-to-hub',
 'Jikiwa/test-16340052901609',
 'Jikiwa/test-16344364230608',
 'KETI-AIR/nikl',
 'GroNLP/ik-nlp-22_transqe',
 'Helsinki-NLP/tatoeba_mt',
 'IGESML/pubmed_neg',
 'JesseParvess/book_snippets_asr',
 'Jikiwa/pushed-to-hub',
 'Jikiwa/test-16344360501144',
 'JonathanSum/en_corpora_parliament_processed',
 'Karavet/ILUR-news-text-classification-corpus',
 'GEM/wiki_cat_sum',
 'Hellisotherpeople/DebateSum',
 ...]

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 25000
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 25000
    })
    unsupervised: Dataset({
        features: ['text', 'label'],
        num_rows: 50000
    })
})

{'label': 0,
 'text': 'I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered "controversial" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\'t have much of a plot.'}

{'label': [0, 0, 0],
 'text': ['I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfunded, under-appreciated and misunderstood. I tried to like this, I really did, but it is to good TV sci-fi as Babylon 5 is to Star Trek (the original). Silly prosthetics, cheap cardboard sets, stilted dialogues, CG that doesn\'t match the background, and painfully one-dimensional characters cannot be overcome with a \'sci-fi\' setting. (I\'m sure there are those of you out there who think Babylon 5 is good sci-fi TV. It\'s not. It\'s clichéd and uninspiring.) While US viewers might like emotion and character development, sci-fi is a genre that does not take itself seriously (cf. Star Trek). It may treat important issues, yet not as a serious philosophy. It\'s really difficult to care about the characters here as they are not simply foolish, just missing a spark of life. Their actions and reactions are wooden and predictable, often painful to watch. The makers of Earth KNOW it\'s rubbish as they have to always say "Gene Roddenberry\'s Earth..." otherwise people would not continue watching. Roddenberry\'s ashes must be turning in their orbit as this dull, cheap, poorly edited (watching it without advert breaks really brings this home) trudging Trabant of a show lumbers into space. Spoiler. So, kill off a main character. And then bring him back as another actor. Jeeez! Dallas all over again.',
  "Worth the entertainment value of a rental, especially if you like action movies. This one features the usual car chases, fights with the great Van Damme kick style, shooting battles with the 40 shell load shotgun, and even terrorist style bombs. All of this is entertaining and competently handled but there is nothing that really blows you away if you've seen your share before.<br /><br />The plot is made interesting by the inclusion of a rabbit, which is clever but hardly profound. Many of the characters are heavily stereotyped -- the angry veterans, the terrified illegal aliens, the crooked cops, the indifferent feds, the bitchy tough lady station head, the crooked politician, the fat federale who looks like he was typecast as the Mexican in a Hollywood movie from the 1940s. All passably acted but again nothing special.<br /><br />I thought the main villains were pretty well done and fairly well acted. By the end of the movie you certainly knew who the good guys were and weren't. There was an emotional lift as the really bad ones got their just deserts. Very simplistic, but then you weren't expecting Hamlet, right? The only thing I found really annoying was the constant cuts to VDs daughter during the last fight scene.<br /><br />Not bad. Not good. Passable 4.",
  "its a totally average film with a few semi-alright action sequences that make the plot seem a little better and remind the viewer of the classic van dam films. parts of the plot don't make sense and seem to be added in to use up time. the end plot is that of a very basic type that doesn't leave the viewer guessing and any twists are obvious from the beginning. the end scene with the flask backs don't make sense as they are added in and seem to have little relevance to the history of van dam's character. not really worth watching again, bit disappointed in the end production, even though it is apparent it was shot on a low budget certain shots and sections in the film are of poor directed quality"]}

Dataset({
    features: ['text', 'label'],
    num_rows: 2000
})

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 1600
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 400
    })
})

Dataset({
    features: ['text', 'label'],
    num_rows: 400
})

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 1600
    })
    validation: Dataset({
        features: ['text', 'label'],
        num_rows: 400
    })
})

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 1600
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 25000
    })
    unsupervised: Dataset({
        features: ['text', 'label'],
        num_rows: 50000
    })
    validation: Dataset({
        features: ['text', 'label'],
        num_rows: 400
    })
})

Dataset({
    features: ['text', 'label'],
    num_rows: 400
})

{'label': [-1, -1, -1],
 'text': ['This is just a precious little diamond. The play, the script are excellent. I cant compare this movie with anything else, maybe except the movie "Leon" wonderfully played by Jean Reno and Natalie Portman. But... What can I say about this one? This is the best movie Anne Parillaud has ever played in (See please "Frankie Starlight", she\'s speaking English there) to see what I mean. The story of young punk girl Nikita, taken into the depraved world of the secret government forces has been exceptionally over used by Americans. Never mind the "Point of no return" and especially the "La femme Nikita" TV series. They cannot compare the original believe me! Trash these videos. Buy this one, do not rent it, BUY it. BTW beware of the subtitles of the LA company which "translate" the US release. What a disgrace! If you cant understand French, get a dubbed version. But you\'ll regret later :)',
  'When I say this is my favourite film of all time, that comment is not to be taken lightly. I probably watch far too many films than is healthy for me, and have loved quite a few of them. I first saw "La Femme Nikita" nearly ten years ago, and it still manages to be my absolute favourite. Why?<br /><br />This is more than an incredibly stylish and sexy thriller. Luc Besson\'s great flair for impeccable direction, fashion, and appropriate usage of music makes this a very watchable film. But it is Anne Parillaud\'s perfect rendering of a complex character who transforms from a heartless killer into a compassionate, vibrant young woman that makes this film beautiful. I can\'t keep my eyes off of her when she is on screen.<br /><br />I have seen several of Luc Besson\'s films including "Subway", "The Professional", and the irritating "Fifth Element", and "Nikita" is without a doubt, far superior to any of these. Although this film has tragic elements, it is ultimately extremely hopeful. It is the story of a person who is cruel and merciless, who ultimately comes to realize her own humanity and her own personal power. That, to me is extremely inspiring. If there is hope for Nikita, there is hope for all of us.',
  'I saw this movie because I am a huge fan of the TV series of the same name starring Roy Dupuis and Pet Wilson. The movie was really good and I saw how the TV show is based on the movie. A few episodes of the TV series came directly from the movie and their similarity was amazing. To keep things short, any fan of the movie has to watch the series and any fan of the series must see the original Nikita.']}

DatasetDict({
    train: Dataset({
        features: ['text', 'label'],
        num_rows: 1600
    })
    test: Dataset({
        features: ['text', 'label'],
        num_rows: 400
    })
    validation: Dataset({
        features: ['text', 'label'],
        num_rows: 400
    })
})

"Hey HULU.com is playing the Elvira late night horror show on their site and this movie is their under the Name Monsteroid, good fun to watch Elvira comment on this Crappy movie ....Have Fun with bad movies. Anyways this movie really has very little value other than to see how bad the 70's were for horror flicks Bad Effects, Bad Dialog, just bad movie making. Avoid this unless you want to laugh at it. While you are at HULU check out the other movies that are their right now there is 10 episodes and some are pretty decent movies with good plots and production and you can watch a lot of them in 480p as long as you have a decent speed connection."

"Hey HULU.com is playing the Elvira late night horror show on their site and this movie is their under the Name Monsteroid, good fun to watch Elvira comment on this Crappy movie ....Have Fun with bad movies. Anyways this movie really has very little value other than to see how bad the 70's were for horror flicks Bad Effects, Bad Dialog, just bad movie making. Avoid this unless you want to laugh at it. While you are at HULU check out the other movies that are their right now there is 10 episodes and some are pretty decent movies with good plots and production and you can watch a lot of them in 480p as long as you have a decent speed connection."

0    823
1    777
Name: label, dtype: int64

DatasetDict({
    train: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 1600
    })
    test: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 400
    })
    validation: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 400
    })
})

['AutoModel',
 'AutoModelForAudioClassification',
 'AutoModelForAudioFrameClassification',
 'AutoModelForAudioXVector',
 'AutoModelForCTC',
 'AutoModelForCausalLM',
 'AutoModelForImageClassification',
 'AutoModelForImageSegmentation',
 'AutoModelForInstanceSegmentation',
 'AutoModelForMaskedImageModeling',
 'AutoModelForMaskedLM',
 'AutoModelForMultipleChoice',
 'AutoModelForNextSentencePrediction',
 'AutoModelForObjectDetection',
 'AutoModelForPreTraining',
 'AutoModelForQuestionAnswering',
 'AutoModelForSemanticSegmentation',
 'AutoModelForSeq2SeqLM',
 'AutoModelForSequenceClassification',
 'AutoModelForSpeechSeq2Seq',
 'AutoModelForTableQuestionAnswering',
 'AutoModelForTokenClassification',
 'AutoModelForVision2Seq',
 'AutoModelWithLMHead']

DatasetDict({
    train: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 50
    })
    validation: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 10
    })
    test: Dataset({
        features: ['text', 'label', 'input_ids', 'attention_mask'],
        num_rows: 10
    })
})

TrainingArguments(
_n_gpu=1,
adafactor=False,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-08,
bf16=False,
bf16_full_eval=False,
data_seed=None,
dataloader_drop_last=False,
dataloader_num_workers=0,
dataloader_pin_memory=True,
ddp_bucket_cap_mb=None,
ddp_find_unused_parameters=None,
debug=[],
deepspeed=None,
disable_tqdm=False,
do_eval=True,
do_predict=False,
do_train=False,
eval_accumulation_steps=None,
eval_delay=0,
eval_steps=None,
evaluation_strategy=IntervalStrategy.EPOCH,
fp16=False,
fp16_backend=auto,
fp16_full_eval=False,
fp16_opt_level=O1,
gradient_accumulation_steps=1,
gradient_checkpointing=False,
greater_is_better=None,
group_by_length=False,
half_precision_backend=auto,
hub_model_id=None,
hub_strategy=HubStrategy.EVERY_SAVE,
hub_token=<HUB_TOKEN>,
ignore_data_skip=False,
label_names=None,
label_smoothing_factor=0.0,
learning_rate=2e-05,
length_column_name=length,
load_best_model_at_end=False,
local_rank=-1,
log_level=40,
log_level_replica=-1,
log_on_each_node=True,
logging_dir=distilbert-base-cased-finetuned-tiny-imdb/runs/Apr11_11-10-15_d484f04cfea1,
logging_first_step=False,
logging_nan_inf_filter=True,
logging_steps=6,
logging_strategy=IntervalStrategy.STEPS,
lr_scheduler_type=SchedulerType.LINEAR,
max_grad_norm=1.0,
max_steps=-1,
metric_for_best_model=None,
mp_parameters=,
no_cuda=False,
num_train_epochs=2,
optim=OptimizerNames.ADAMW_TORCH,
output_dir=distilbert-base-cased-finetuned-tiny-imdb,
overwrite_output_dir=False,
past_index=-1,
per_device_eval_batch_size=8,
per_device_train_batch_size=8,
prediction_loss_only=False,
push_to_hub=False,
push_to_hub_model_id=None,
push_to_hub_organization=None,
push_to_hub_token=<PUSH_TO_HUB_TOKEN>,
remove_unused_columns=True,
report_to=['tensorboard'],
resume_from_checkpoint=None,
run_name=distilbert-base-cased-finetuned-tiny-imdb,
save_on_each_node=False,
save_steps=500,
save_strategy=IntervalStrategy.STEPS,
save_total_limit=None,
seed=42,
sharded_ddp=[],
skip_memory_metrics=True,
tf32=None,
tpu_metrics_debug=False,
tpu_num_cores=None,
use_legacy_prediction_loop=False,
warmup_ratio=0.0,
warmup_steps=0,
weight_decay=0.01,
xpu_backend=None,
)

PredictionOutput(predictions=array([[-0.06958597,  0.08243475],
       [-0.07667492,  0.11690364],
       [-0.05978067,  0.05852588],
       [-0.05062508,  0.09085844],
       [-0.07219092,  0.10617454],
       [-0.08734367,  0.11028455],
       [-0.06684104,  0.08281732],
       [-0.07786269,  0.10676245],
       [-0.06891385,  0.09251334],
       [-0.08195043,  0.10885128]], dtype=float32), label_ids=array([0, 0, 1, 1, 0, 0, 0, 0, 1, 0]), metrics={'test_loss': 0.7379708886146545, 'test_runtime': 0.375, 'test_samples_per_second': 26.664, 'test_steps_per_second': 5.333})

(10, 2)

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

array([0, 0, 1, 1, 0, 0, 0, 0, 1, 0])

0.3

{'epoch': 2.0,
 'eval_accuracy': 0.895,
 'eval_loss': 0.3591071367263794,
 'eval_runtime': 13.6299,
 'eval_samples_per_second': 29.347,
 'eval_steps_per_second': 3.668}

'bert-base-cased-finetuned-imdb'

[{'label': 'LABEL_0', 'score': 0.9525713324546814}]

[{'label': 'LABEL_1', 'score': 0.8722493052482605}]