TensorFlow Datasets

pip install -q tfds-nightly tensorflow matplotlib

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf

import tensorflow_datasets as tfds

2023-10-03 09:31:42.312675: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-10-03 09:31:42.312725: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-10-03 09:31:42.312761: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

tfds.list_builders()

['abstract_reasoning',
 'accentdb',
 'aeslc',
 'aflw2k3d',
 'ag_news_subset',
 'ai2_arc',
 'ai2_arc_with_ir',
 'amazon_us_reviews',
 'anli',
 'answer_equivalence',
 'arc',
 'asqa',
 'asset',
 'assin2',
 'asu_table_top_converted_externally_to_rlds',
 'austin_buds_dataset_converted_externally_to_rlds',
 'austin_sailor_dataset_converted_externally_to_rlds',
 'austin_sirius_dataset_converted_externally_to_rlds',
 'bair_robot_pushing_small',
 'bc_z',
 'bccd',
 'beans',
 'bee_dataset',
 'beir',
 'berkeley_autolab_ur5',
 'berkeley_cable_routing',
 'berkeley_fanuc_manipulation',
 'berkeley_gnm_cory_hall',
 'berkeley_gnm_recon',
 'berkeley_gnm_sac_son',
 'berkeley_mvp_converted_externally_to_rlds',
 'berkeley_rpt_converted_externally_to_rlds',
 'big_patent',
 'bigearthnet',
 'billsum',
 'binarized_mnist',
 'binary_alpha_digits',
 'ble_wind_field',
 'blimp',
 'booksum',
 'bool_q',
 'bot_adversarial_dialogue',
 'bridge',
 'bucc',
 'c4',
 'c4_wsrs',
 'caltech101',
 'caltech_birds2010',
 'caltech_birds2011',
 'cardiotox',
 'cars196',
 'cassava',
 'cats_vs_dogs',
 'celeb_a',
 'celeb_a_hq',
 'cfq',
 'cherry_blossoms',
 'chexpert',
 'cifar10',
 'cifar100',
 'cifar100_n',
 'cifar10_1',
 'cifar10_corrupted',
 'cifar10_h',
 'cifar10_n',
 'citrus_leaves',
 'cityscapes',
 'civil_comments',
 'clevr',
 'clic',
 'clinc_oos',
 'cmaterdb',
 'cmu_franka_exploration_dataset_converted_externally_to_rlds',
 'cmu_play_fusion',
 'cmu_stretch',
 'cnn_dailymail',
 'coco',
 'coco_captions',
 'coil100',
 'colorectal_histology',
 'colorectal_histology_large',
 'columbia_cairlab_pusht_real',
 'common_voice',
 'conll2002',
 'conll2003',
 'controlled_noisy_web_labels',
 'coqa',
 'corr2cause',
 'cos_e',
 'cosmos_qa',
 'covid19',
 'covid19sum',
 'crema_d',
 'criteo',
 'cs_restaurants',
 'curated_breast_imaging_ddsm',
 'cycle_gan',
 'd4rl_adroit_door',
 'd4rl_adroit_hammer',
 'd4rl_adroit_pen',
 'd4rl_adroit_relocate',
 'd4rl_antmaze',
 'd4rl_mujoco_ant',
 'd4rl_mujoco_halfcheetah',
 'd4rl_mujoco_hopper',
 'd4rl_mujoco_walker2d',
 'dart',
 'databricks_dolly',
 'davis',
 'deep1b',
 'deep_weeds',
 'definite_pronoun_resolution',
 'dementiabank',
 'diabetic_retinopathy_detection',
 'diamonds',
 'div2k',
 'dlr_edan_shared_control_converted_externally_to_rlds',
 'dlr_sara_grid_clamp_converted_externally_to_rlds',
 'dlr_sara_pour_converted_externally_to_rlds',
 'dmlab',
 'doc_nli',
 'dolphin_number_word',
 'domainnet',
 'downsampled_imagenet',
 'drop',
 'dsprites',
 'dtd',
 'duke_ultrasound',
 'e2e_cleaned',
 'efron_morris75',
 'emnist',
 'eraser_multi_rc',
 'esnli',
 'eth_agent_affordances',
 'eurosat',
 'fashion_mnist',
 'flic',
 'flores',
 'food101',
 'forest_fires',
 'fuss',
 'gap',
 'geirhos_conflict_stimuli',
 'gem',
 'genomics_ood',
 'german_credit_numeric',
 'gigaword',
 'glove100_angular',
 'glue',
 'goemotions',
 'gov_report',
 'gpt3',
 'gref',
 'groove',
 'grounded_scan',
 'gsm8k',
 'gtzan',
 'gtzan_music_speech',
 'hellaswag',
 'higgs',
 'hillstrom',
 'horses_or_humans',
 'howell',
 'i_naturalist2017',
 'i_naturalist2018',
 'i_naturalist2021',
 'iamlab_cmu_pickup_insert_converted_externally_to_rlds',
 'imagenet2012',
 'imagenet2012_corrupted',
 'imagenet2012_fewshot',
 'imagenet2012_multilabel',
 'imagenet2012_real',
 'imagenet2012_subset',
 'imagenet_a',
 'imagenet_lt',
 'imagenet_pi',
 'imagenet_r',
 'imagenet_resized',
 'imagenet_sketch',
 'imagenet_v2',
 'imagenette',
 'imagewang',
 'imdb_reviews',
 'imperialcollege_sawyer_wrist_cam',
 'irc_disentanglement',
 'iris',
 'istella',
 'jaco_play',
 'kaist_nonprehensile_converted_externally_to_rlds',
 'kddcup99',
 'kitti',
 'kmnist',
 'kuka',
 'laion400m',
 'lambada',
 'lfw',
 'librispeech',
 'librispeech_lm',
 'libritts',
 'ljspeech',
 'lm1b',
 'locomotion',
 'lost_and_found',
 'lsun',
 'lvis',
 'malaria',
 'maniskill_dataset_converted_externally_to_rlds',
 'math_dataset',
 'math_qa',
 'mctaco',
 'media_sum',
 'mlqa',
 'mnist',
 'mnist_corrupted',
 'movie_lens',
 'movie_rationales',
 'movielens',
 'moving_mnist',
 'mrqa',
 'mslr_web',
 'mt_opt',
 'mtnt',
 'multi_news',
 'multi_nli',
 'multi_nli_mismatch',
 'natural_instructions',
 'natural_questions',
 'natural_questions_open',
 'newsroom',
 'nsynth',
 'nyu_depth_v2',
 'nyu_door_opening_surprising_effectiveness',
 'nyu_franka_play_dataset_converted_externally_to_rlds',
 'nyu_rot_dataset_converted_externally_to_rlds',
 'ogbg_molpcba',
 'omniglot',
 'open_images_challenge2019_detection',
 'open_images_v4',
 'openbookqa',
 'opinion_abstracts',
 'opinosis',
 'opus',
 'oxford_flowers102',
 'oxford_iiit_pet',
 'para_crawl',
 'pass',
 'patch_camelyon',
 'paws_wiki',
 'paws_x_wiki',
 'penguins',
 'pet_finder',
 'pg19',
 'piqa',
 'places365_small',
 'placesfull',
 'plant_leaves',
 'plant_village',
 'plantae_k',
 'protein_net',
 'q_re_cc',
 'qa4mre',
 'qasc',
 'quac',
 'quality',
 'quickdraw_bitmap',
 'race',
 'radon',
 'real_toxicity_prompts',
 'reddit',
 'reddit_disentanglement',
 'reddit_tifu',
 'ref_coco',
 'resisc45',
 'rlu_atari',
 'rlu_atari_checkpoints',
 'rlu_atari_checkpoints_ordered',
 'rlu_control_suite',
 'rlu_dmlab_explore_object_rewards_few',
 'rlu_dmlab_explore_object_rewards_many',
 'rlu_dmlab_rooms_select_nonmatching_object',
 'rlu_dmlab_rooms_watermaze',
 'rlu_dmlab_seekavoid_arena01',
 'rlu_locomotion',
 'rlu_rwrl',
 'robomimic_mg',
 'robomimic_mh',
 'robomimic_ph',
 'robonet',
 'robosuite_panda_pick_place_can',
 'roboturk',
 'rock_paper_scissors',
 'rock_you',
 's3o4d',
 'salient_span_wikipedia',
 'samsum',
 'savee',
 'scan',
 'scene_parse150',
 'schema_guided_dialogue',
 'sci_tail',
 'scicite',
 'scientific_papers',
 'scrolls',
 'segment_anything',
 'sentiment140',
 'shapes3d',
 'sift1m',
 'simpte',
 'siscore',
 'smallnorb',
 'smartwatch_gestures',
 'snli',
 'so2sat',
 'speech_commands',
 'spoken_digit',
 'squad',
 'squad_question_generation',
 'stanford_dogs',
 'stanford_hydra_dataset_converted_externally_to_rlds',
 'stanford_kuka_multimodal_dataset_converted_externally_to_rlds',
 'stanford_mask_vit_converted_externally_to_rlds',
 'stanford_online_products',
 'stanford_robocook_converted_externally_to_rlds',
 'star_cfq',
 'starcraft_video',
 'stl10',
 'story_cloze',
 'summscreen',
 'sun397',
 'super_glue',
 'svhn_cropped',
 'symmetric_solids',
 'taco_play',
 'tao',
 'tatoeba',
 'ted_hrlr_translate',
 'ted_multi_translate',
 'tedlium',
 'tf_flowers',
 'the300w_lp',
 'tiny_shakespeare',
 'titanic',
 'tokyo_u_lsmo_converted_externally_to_rlds',
 'toto',
 'trec',
 'trivia_qa',
 'tydi_qa',
 'uc_merced',
 'ucf101',
 'ucsd_kitchen_dataset_converted_externally_to_rlds',
 'ucsd_pick_and_place_dataset_converted_externally_to_rlds',
 'uiuc_d3field',
 'unified_qa',
 'universal_dependencies',
 'unnatural_instructions',
 'usc_cloth_sim_converted_externally_to_rlds',
 'user_libri_audio',
 'user_libri_text',
 'utaustin_mutex',
 'utokyo_pr2_opening_fridge_converted_externally_to_rlds',
 'utokyo_pr2_tabletop_manipulation_converted_externally_to_rlds',
 'utokyo_saytap_converted_externally_to_rlds',
 'utokyo_xarm_bimanual_converted_externally_to_rlds',
 'utokyo_xarm_pick_and_place_converted_externally_to_rlds',
 'vctk',
 'viola',
 'visual_domain_decathlon',
 'voc',
 'voxceleb',
 'voxforge',
 'waymo_open_dataset',
 'web_graph',
 'web_nlg',
 'web_questions',
 'webvid',
 'wider_face',
 'wiki40b',
 'wiki_auto',
 'wiki_bio',
 'wiki_dialog',
 'wiki_table_questions',
 'wiki_table_text',
 'wikiann',
 'wikihow',
 'wikipedia',
 'wikipedia_toxicity_subtypes',
 'wine_quality',
 'winogrande',
 'wit',
 'wit_kaggle',
 'wmt13_translate',
 'wmt14_translate',
 'wmt15_translate',
 'wmt16_translate',
 'wmt17_translate',
 'wmt18_translate',
 'wmt19_translate',
 'wmt_t2t_translate',
 'wmt_translate',
 'wordnet',
 'wsc273',
 'xnli',
 'xquad',
 'xsum',
 'xtreme_pawsx',
 'xtreme_pos',
 'xtreme_s',
 'xtreme_xnli',
 'yahoo_ltrc',
 'yelp_polarity_reviews',
 'yes_no',
 'youtube_vis',
 'huggingface:acronym_identification',
 'huggingface:ade_corpus_v2',
 'huggingface:adv_glue',
 'huggingface:adversarial_qa',
 'huggingface:aeslc',
 'huggingface:afrikaans_ner_corpus',
 'huggingface:ag_news',
 'huggingface:ai2_arc',
 'huggingface:air_dialogue',
 'huggingface:ajgt_twitter_ar',
 'huggingface:allegro_reviews',
 'huggingface:allocine',
 'huggingface:alt',
 'huggingface:amazon_polarity',
 'huggingface:amazon_reviews_multi',
 'huggingface:amazon_us_reviews',
 'huggingface:ambig_qa',
 'huggingface:americas_nli',
 'huggingface:ami',
 'huggingface:amttl',
 'huggingface:anli',
 'huggingface:app_reviews',
 'huggingface:aqua_rat',
 'huggingface:aquamuse',
 'huggingface:ar_cov19',
 'huggingface:ar_res_reviews',
 'huggingface:ar_sarcasm',
 'huggingface:arabic_billion_words',
 'huggingface:arabic_pos_dialect',
 'huggingface:arabic_speech_corpus',
 'huggingface:arcd',
 'huggingface:arsentd_lev',
 'huggingface:art',
 'huggingface:arxiv_dataset',
 'huggingface:ascent_kb',
 'huggingface:aslg_pc12',
 'huggingface:asnq',
 'huggingface:asset',
 'huggingface:assin',
 'huggingface:assin2',
 'huggingface:atomic',
 'huggingface:autshumato',
 'huggingface:babi_qa',
 'huggingface:banking77',
 'huggingface:bbaw_egyptian',
 'huggingface:bbc_hindi_nli',
 'huggingface:bc2gm_corpus',
 'huggingface:beans',
 'huggingface:best2009',
 'huggingface:bianet',
 'huggingface:bible_para',
 'huggingface:big_patent',
 'huggingface:bigbench',
 'huggingface:billsum',
 'huggingface:bing_coronavirus_query_set',
 'huggingface:biomrc',
 'huggingface:biosses',
 'huggingface:biwi_kinect_head_pose',
 'huggingface:blbooks',
 'huggingface:blbooksgenre',
 'huggingface:blended_skill_talk',
 'huggingface:blimp',
 'huggingface:blog_authorship_corpus',
 'huggingface:bn_hate_speech',
 'huggingface:bnl_newspapers',
 'huggingface:bookcorpus',
 'huggingface:bookcorpusopen',
 'huggingface:boolq',
 'huggingface:bprec',
 'huggingface:break_data',
 'huggingface:brwac',
 'huggingface:bsd_ja_en',
 'huggingface:bswac',
 'huggingface:c3',
 'huggingface:c4',
 'huggingface:cail2018',
 'huggingface:caner',
 'huggingface:capes',
 'huggingface:casino',
 'huggingface:catalonia_independence',
 'huggingface:cats_vs_dogs',
 'huggingface:cawac',
 'huggingface:cbt',
 'huggingface:cc100',
 'huggingface:cc_news',
 'huggingface:ccaligned_multilingual',
 'huggingface:cdsc',
 'huggingface:cdt',
 'huggingface:cedr',
 'huggingface:cfq',
 'huggingface:chr_en',
 'huggingface:cifar10',
 'huggingface:cifar100',
 'huggingface:circa',
 'huggingface:civil_comments',
 'huggingface:clickbait_news_bg',
 'huggingface:climate_fever',
 'huggingface:clinc_oos',
 'huggingface:clue',
 'huggingface:cmrc2018',
 'huggingface:cmu_hinglish_dog',
 'huggingface:cnn_dailymail',
 'huggingface:coached_conv_pref',
 'huggingface:coarse_discourse',
 'huggingface:codah',
 'huggingface:code_search_net',
 'huggingface:code_x_glue_cc_clone_detection_big_clone_bench',
 'huggingface:code_x_glue_cc_clone_detection_poj104',
 'huggingface:code_x_glue_cc_cloze_testing_all',
 'huggingface:code_x_glue_cc_cloze_testing_maxmin',
 'huggingface:code_x_glue_cc_code_completion_line',
 'huggingface:code_x_glue_cc_code_completion_token',
 'huggingface:code_x_glue_cc_code_refinement',
 'huggingface:code_x_glue_cc_code_to_code_trans',
 'huggingface:code_x_glue_cc_defect_detection',
 'huggingface:code_x_glue_ct_code_to_text',
 'huggingface:code_x_glue_tc_nl_code_search_adv',
 'huggingface:code_x_glue_tc_text_to_code',
 'huggingface:code_x_glue_tt_text_to_text',
 'huggingface:com_qa',
 'huggingface:common_gen',
 'huggingface:common_language',
 'huggingface:common_voice',
 'huggingface:commonsense_qa',
 'huggingface:competition_math',
 'huggingface:compguesswhat',
 'huggingface:conceptnet5',
 'huggingface:conceptual_12m',
 'huggingface:conceptual_captions',
 'huggingface:conll2000',
 'huggingface:conll2002',
 'huggingface:conll2003',
 'huggingface:conll2012_ontonotesv5',
 'huggingface:conllpp',
 'huggingface:consumer-finance-complaints',
 'huggingface:conv_ai',
 'huggingface:conv_ai_2',
 'huggingface:conv_ai_3',
 'huggingface:conv_questions',
 'huggingface:coqa',
 'huggingface:cord19',
 'huggingface:cornell_movie_dialog',
 'huggingface:cos_e',
 'huggingface:cosmos_qa',
 'huggingface:counter',
 'huggingface:covid_qa_castorini',
 'huggingface:covid_qa_deepset',
 'huggingface:covid_qa_ucsd',
 'huggingface:covid_tweets_japanese',
 'huggingface:covost2',
 'huggingface:cppe-5',
 'huggingface:craigslist_bargains',
 'huggingface:crawl_domain',
 'huggingface:crd3',
 'huggingface:crime_and_punish',
 'huggingface:crows_pairs',
 'huggingface:cryptonite',
 'huggingface:cs_restaurants',
 'huggingface:cuad',
 'huggingface:curiosity_dialogs',
 'huggingface:daily_dialog',
 'huggingface:dane',
 'huggingface:danish_political_comments',
 'huggingface:dart',
 'huggingface:datacommons_factcheck',
 'huggingface:dbpedia_14',
 'huggingface:dbrd',
 'huggingface:deal_or_no_dialog',
 'huggingface:definite_pronoun_resolution',
 'huggingface:dengue_filipino',
 'huggingface:dialog_re',
 'huggingface:diplomacy_detection',
 'huggingface:disaster_response_messages',
 'huggingface:discofuse',
 'huggingface:discovery',
 'huggingface:disfl_qa',
 'huggingface:doc2dial',
 'huggingface:docred',
 'huggingface:doqa',
 'huggingface:dream',
 'huggingface:drop',
 'huggingface:duorc',
 'huggingface:dutch_social',
 'huggingface:dyk',
 'huggingface:e2e_nlg',
 'huggingface:e2e_nlg_cleaned',
 'huggingface:ecb',
 'huggingface:ecthr_cases',
 'huggingface:eduge',
 'huggingface:ehealth_kd',
 'huggingface:eitb_parcc',
 'huggingface:electricity_load_diagrams',
 'huggingface:eli5',
 'huggingface:eli5_category',
 'huggingface:elkarhizketak',
 'huggingface:emea',
 'huggingface:emo',
 'huggingface:emotion',
 'huggingface:emotone_ar',
 'huggingface:empathetic_dialogues',
 'huggingface:enriched_web_nlg',
 'huggingface:enwik8',
 'huggingface:eraser_multi_rc',
 'huggingface:esnli',
 'huggingface:eth_py150_open',
 'huggingface:ethos',
 'huggingface:ett',
 'huggingface:eu_regulatory_ir',
 'huggingface:eurlex',
 'huggingface:euronews',
 'huggingface:europa_eac_tm',
 'huggingface:europa_ecdc_tm',
 'huggingface:europarl_bilingual',
 'huggingface:event2Mind',
 'huggingface:evidence_infer_treatment',
 'huggingface:exams',
 'huggingface:factckbr',
 'huggingface:fake_news_english',
 'huggingface:fake_news_filipino',
 'huggingface:farsi_news',
 'huggingface:fashion_mnist',
 'huggingface:fever',
 'huggingface:few_rel',
 'huggingface:financial_phrasebank',
 'huggingface:finer',
 'huggingface:flores',
 'huggingface:flue',
 'huggingface:food101',
 'huggingface:fquad',
 'huggingface:freebase_qa',
 'huggingface:gap',
 'huggingface:gem',
 'huggingface:generated_reviews_enth',
 'huggingface:generics_kb',
 'huggingface:german_legal_entity_recognition',
 'huggingface:germaner',
 'huggingface:germeval_14',
 'huggingface:giga_fren',
 'huggingface:gigaword',
 'huggingface:glucose',
 'huggingface:glue',
 'huggingface:gnad10',
 'huggingface:go_emotions',
 'huggingface:gooaq',
 'huggingface:google_wellformed_query',
 'huggingface:grail_qa',
 'huggingface:great_code',
 'huggingface:greek_legal_code',
 'huggingface:gsm8k',
 'huggingface:guardian_authorship',
 'huggingface:gutenberg_time',
 'huggingface:hans',
 'huggingface:hansards',
 'huggingface:hard',
 'huggingface:harem',
 'huggingface:has_part',
 'huggingface:hate_offensive',
 'huggingface:hate_speech18',
 'huggingface:hate_speech_filipino',
 'huggingface:hate_speech_offensive',
 'huggingface:hate_speech_pl',
 'huggingface:hate_speech_portuguese',
 'huggingface:hatexplain',
 'huggingface:hausa_voa_ner',
 'huggingface:hausa_voa_topics',
 'huggingface:hda_nli_hindi',
 'huggingface:head_qa',
 'huggingface:health_fact',
 'huggingface:hebrew_projectbenyehuda',
 'huggingface:hebrew_sentiment',
 'huggingface:hebrew_this_world',
 'huggingface:hellaswag',
 'huggingface:hendrycks_test',
 'huggingface:hind_encorp',
 'huggingface:hindi_discourse',
 'huggingface:hippocorpus',
 'huggingface:hkcancor',
 'huggingface:hlgd',
 'huggingface:hope_edi',
 'huggingface:hotpot_qa',
 'huggingface:hover',
 'huggingface:hrenwac_para',
 'huggingface:hrwac',
 'huggingface:humicroedit',
 'huggingface:hybrid_qa',
 'huggingface:hyperpartisan_news_detection',
 'huggingface:iapp_wiki_qa_squad',
 'huggingface:id_clickbait',
 'huggingface:id_liputan6',
 'huggingface:id_nergrit_corpus',
 'huggingface:id_newspapers_2018',
 'huggingface:id_panl_bppt',
 'huggingface:id_puisi',
 'huggingface:igbo_english_machine_translation',
 'huggingface:igbo_monolingual',
 'huggingface:igbo_ner',
 'huggingface:ilist',
 'huggingface:imagenet-1k',
 'huggingface:imagenet_sketch',
 'huggingface:imdb',
 'huggingface:imdb_urdu_reviews',
 'huggingface:imppres',
 'huggingface:indic_glue',
 'huggingface:indonli',
 'huggingface:indonlu',
 'huggingface:inquisitive_qg',
 'huggingface:interpress_news_category_tr',
 'huggingface:interpress_news_category_tr_lite',
 'huggingface:irc_disentangle',
 'huggingface:isixhosa_ner_corpus',
 'huggingface:isizulu_ner_corpus',
 'huggingface:iwslt2017',
 'huggingface:jeopardy',
 'huggingface:jfleg',
 'huggingface:jigsaw_toxicity_pred',
 'huggingface:jigsaw_unintended_bias',
 'huggingface:jnlpba',
 'huggingface:journalists_questions',
 'huggingface:kan_hope',
 'huggingface:kannada_news',
 'huggingface:kd_conv',
 'huggingface:kde4',
 'huggingface:kelm',
 'huggingface:kilt_tasks',
 'huggingface:kilt_wikipedia',
 'huggingface:kinnews_kirnews',
 'huggingface:klue',
 'huggingface:kor_3i4k',
 'huggingface:kor_hate',
 'huggingface:kor_ner',
 'huggingface:kor_nli',
 'huggingface:kor_nlu',
 'huggingface:kor_qpair',
 'huggingface:kor_sae',
 'huggingface:kor_sarcasm',
 'huggingface:labr',
 'huggingface:lama',
 'huggingface:lambada',
 'huggingface:large_spanish_corpus',
 'huggingface:laroseda',
 'huggingface:lc_quad',
 'huggingface:lccc',
 'huggingface:lener_br',
 'huggingface:lex_glue',
 'huggingface:liar',
 'huggingface:librispeech_asr',
 'huggingface:librispeech_lm',
 'huggingface:limit',
 'huggingface:lince',
 'huggingface:linnaeus',
 'huggingface:liveqa',
 'huggingface:lj_speech',
 'huggingface:lm1b',
 'huggingface:lst20',
 'huggingface:m_lama',
 'huggingface:mac_morpho',
 'huggingface:makhzan',
 'huggingface:masakhaner',
 'huggingface:math_dataset',
 'huggingface:math_qa',
 'huggingface:matinf',
 'huggingface:mbpp',
 'huggingface:mc4',
 'huggingface:mc_taco',
 'huggingface:md_gender_bias',
 'huggingface:mdd',
 'huggingface:med_hop',
 'huggingface:medal',
 'huggingface:medical_dialog',
 'huggingface:medical_questions_pairs',
 'huggingface:medmcqa',
 'huggingface:menyo20k_mt',
 'huggingface:meta_woz',
 'huggingface:metashift',
 'huggingface:metooma',
 'huggingface:metrec',
 'huggingface:miam',
 'huggingface:mkb',
 'huggingface:mkqa',
 'huggingface:mlqa',
 'huggingface:mlsum',
 'huggingface:mnist',
 'huggingface:mocha',
 'huggingface:monash_tsf',
 'huggingface:moroco',
 'huggingface:movie_rationales',
 'huggingface:mrqa',
 'huggingface:ms_marco',
 'huggingface:ms_terms',
 'huggingface:msr_genomics_kbcomp',
 'huggingface:msr_sqa',
 'huggingface:msr_text_compression',
 'huggingface:msr_zhen_translation_parity',
 'huggingface:msra_ner',
 'huggingface:mt_eng_vietnamese',
 'huggingface:muchocine',
 'huggingface:multi_booked',
 'huggingface:multi_eurlex',
 'huggingface:multi_news',
 'huggingface:multi_nli',
 'huggingface:multi_nli_mismatch',
 'huggingface:multi_para_crawl',
 'huggingface:multi_re_qa',
 'huggingface:multi_woz_v22',
 'huggingface:multi_x_science_sum',
 'huggingface:multidoc2dial',
 'huggingface:multilingual_librispeech',
 'huggingface:mutual_friends',
 'huggingface:mwsc',
 'huggingface:myanmar_news',
 'huggingface:narrativeqa',
 'huggingface:narrativeqa_manual',
 'huggingface:natural_questions',
 'huggingface:ncbi_disease',
 'huggingface:nchlt',
 'huggingface:ncslgr',
 'huggingface:nell',
 'huggingface:neural_code_search',
 'huggingface:news_commentary',
 'huggingface:newsgroup',
 'huggingface:newsph',
 'huggingface:newsph_nli',
 'huggingface:newspop',
 'huggingface:newsqa',
 'huggingface:newsroom',
 'huggingface:nkjp-ner',
 'huggingface:nli_tr',
 'huggingface:nlu_evaluation_data',
 'huggingface:norec',
 'huggingface:norne',
 'huggingface:norwegian_ner',
 'huggingface:nq_open',
 'huggingface:nsmc',
 'huggingface:numer_sense',
 'huggingface:numeric_fused_head',
 'huggingface:oclar',
 'huggingface:offcombr',
 'huggingface:offenseval2020_tr',
 'huggingface:offenseval_dravidian',
 'huggingface:ofis_publik',
 'huggingface:ohsumed',
 'huggingface:ollie',
 'huggingface:omp',
 'huggingface:onestop_english',
 'huggingface:onestop_qa',
 'huggingface:open_subtitles',
 'huggingface:openai_humaneval',
 'huggingface:openbookqa',
 'huggingface:openslr',
 'huggingface:openwebtext',
 'huggingface:opinosis',
 'huggingface:opus100',
 'huggingface:opus_books',
 'huggingface:opus_dgt',
 'huggingface:opus_dogc',
 'huggingface:opus_elhuyar',
 'huggingface:opus_euconst',
 'huggingface:opus_finlex',
 'huggingface:opus_fiskmo',
 'huggingface:opus_gnome',
 'huggingface:opus_infopankki',
 'huggingface:opus_memat',
 'huggingface:opus_montenegrinsubs',
 'huggingface:opus_openoffice',
 'huggingface:opus_paracrawl',
 'huggingface:opus_rf',
 'huggingface:opus_tedtalks',
 'huggingface:opus_ubuntu',
 'huggingface:opus_wikipedia',
 'huggingface:opus_xhosanavy',
 'huggingface:orange_sum',
 'huggingface:oscar',
 'huggingface:para_crawl',
 'huggingface:para_pat',
 'huggingface:parsinlu_reading_comprehension',
 'huggingface:pass',
 'huggingface:paws',
 'huggingface:paws-x',
 'huggingface:pec',
 'huggingface:peer_read',
 'huggingface:peoples_daily_ner',
 'huggingface:per_sent',
 'huggingface:persian_ner',
 'huggingface:pg19',
 'huggingface:php',
 'huggingface:piaf',
 'huggingface:pib',
 'huggingface:piqa',
 'huggingface:pn_summary',
 'huggingface:poem_sentiment',
 'huggingface:polemo2',
 'huggingface:poleval2019_cyberbullying',
 'huggingface:poleval2019_mt',
 'huggingface:polsum',
 'huggingface:polyglot_ner',
 'huggingface:prachathai67k',
 'huggingface:pragmeval',
 'huggingface:proto_qa',
 'huggingface:psc',
 'huggingface:ptb_text_only',
 'huggingface:pubmed',
 'huggingface:pubmed_qa',
 'huggingface:py_ast',
 'huggingface:qa4mre',
 'huggingface:qa_srl',
 'huggingface:qa_zre',
 'huggingface:qangaroo',
 'huggingface:qanta',
 'huggingface:qasc',
 'huggingface:qasper',
 'huggingface:qed',
 'huggingface:qed_amara',
 'huggingface:quac',
 'huggingface:quail',
 'huggingface:quarel',
 'huggingface:quartz',
 'huggingface:quickdraw',
 'huggingface:quora',
 'huggingface:quoref',
 'huggingface:race',
 'huggingface:re_dial',
 'huggingface:reasoning_bg',
 'huggingface:recipe_nlg',
 'huggingface:reclor',
 'huggingface:red_caps',
 'huggingface:reddit',
 'huggingface:reddit_tifu',
 'huggingface:refresd',
 'huggingface:reuters21578',
 'huggingface:riddle_sense',
 'huggingface:ro_sent',
 'huggingface:ro_sts',
 'huggingface:ro_sts_parallel',
 'huggingface:roman_urdu',
 'huggingface:roman_urdu_hate_speech',
 'huggingface:ronec',
 'huggingface:ropes',
 'huggingface:rotten_tomatoes',
 'huggingface:russian_super_glue',
 'huggingface:rvl_cdip',
 'huggingface:s2orc',
 'huggingface:samsum',
 'huggingface:sanskrit_classic',
 'huggingface:saudinewsnet',
 'huggingface:sberquad',
 'huggingface:sbu_captions',
 'huggingface:scan',
 'huggingface:scb_mt_enth_2020',
 'huggingface:scene_parse_150',
 'huggingface:schema_guided_dstc8',
 'huggingface:scicite',
 'huggingface:scielo',
 'huggingface:scientific_papers',
 'huggingface:scifact',
 'huggingface:sciq',
 'huggingface:scitail',
 'huggingface:scitldr',
 'huggingface:search_qa',
 'huggingface:sede',
 'huggingface:selqa',
 'huggingface:sem_eval_2010_task_8',
 'huggingface:sem_eval_2014_task_1',
 'huggingface:sem_eval_2018_task_1',
 'huggingface:sem_eval_2020_task_11',
 'huggingface:sent_comp',
 'huggingface:senti_lex',
 'huggingface:senti_ws',
 'huggingface:sentiment140',
 'huggingface:sepedi_ner',
 'huggingface:sesotho_ner_corpus',
 'huggingface:setimes',
 'huggingface:setswana_ner_corpus',
 'huggingface:sharc',
 'huggingface:sharc_modified',
 'huggingface:sick',
 'huggingface:silicone',
 'huggingface:simple_questions_v2',
 'huggingface:siswati_ner_corpus',
 'huggingface:smartdata',
 'huggingface:sms_spam',
 'huggingface:snips_built_in_intents',
 'huggingface:snli',
 'huggingface:snow_simplified_japanese_corpus',
 'huggingface:so_stacksample',
 'huggingface:social_bias_frames',
 'huggingface:social_i_qa',
 'huggingface:sofc_materials_articles',
 'huggingface:sogou_news',
 ...]

ds = tfds.load('mnist', split='train', shuffle_files=True)
assert isinstance(ds, tf.data.Dataset)
print(ds)

<_PrefetchDataset element_spec={'image': TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), 'label': TensorSpec(shape=(), dtype=tf.int64, name=None)}>
2023-10-03 09:31:47.319649: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:268] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

builder = tfds.builder('mnist')
# 1. Create the tfrecord files (no-op if already exists)
builder.download_and_prepare()
# 2. Load the `tf.data.Dataset`
ds = builder.as_dataset(split='train', shuffle_files=True)
print(ds)

<_PrefetchDataset element_spec={'image': TensorSpec(shape=(28, 28, 1), dtype=tf.uint8, name=None), 'label': TensorSpec(shape=(), dtype=tf.int64, name=None)}>

tfds build mnist

ds = tfds.load('mnist', split='train')
ds = ds.take(1)  # Only take a single example

for example in ds:  # example is `{'image': tf.Tensor, 'label': tf.Tensor}`
  print(list(example.keys()))
  image = example["image"]
  label = example["label"]
  print(image.shape, label)

['image', 'label']
(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)
2023-10-03 09:31:49.152950: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

ds = tfds.load('mnist', split='train', as_supervised=True)
ds = ds.take(1)

for image, label in ds:  # example is (image, label)
  print(image.shape, label)

(28, 28, 1) tf.Tensor(4, shape=(), dtype=int64)
2023-10-03 09:31:50.061634: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

ds = tfds.load('mnist', split='train', as_supervised=True)
ds = ds.take(1)

for image, label in tfds.as_numpy(ds):
  print(type(image), type(label), label)

<class 'numpy.ndarray'> <class 'numpy.int64'> 4
2023-10-03 09:31:51.019114: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

image, label = tfds.as_numpy(tfds.load(
    'mnist',
    split='test',
    batch_size=-1,
    as_supervised=True,
))

print(type(image), image.shape)

<class 'numpy.ndarray'> (10000, 28, 28, 1)

ds = tfds.load('mnist', split='train')
ds = ds.batch(32).prefetch(1)

tfds.benchmark(ds, batch_size=32)
tfds.benchmark(ds, batch_size=32)  # Second epoch much faster due to auto-caching

************ Summary ************

Examples/sec (First included) 47739.04 ex/sec (total: 60032 ex, 1.26 sec)
Examples/sec (First only) 99.23 ex/sec (total: 32 ex, 0.32 sec)
Examples/sec (First excluded) 64169.04 ex/sec (total: 60000 ex, 0.94 sec)

************ Summary ************

Examples/sec (First included) 187387.35 ex/sec (total: 60032 ex, 0.32 sec)
Examples/sec (First only) 2385.47 ex/sec (total: 32 ex, 0.01 sec)
Examples/sec (First excluded) 195472.46 ex/sec (total: 60000 ex, 0.31 sec)

ds, info = tfds.load('mnist', split='train', with_info=True)

tfds.as_dataframe(ds.take(4), info)

2023-10-03 09:31:54.816533: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

ds, info = tfds.load('mnist', split='train', with_info=True)

fig = tfds.show_examples(ds, info)

2023-10-03 09:31:55.822856: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

ds, info = tfds.load('mnist', with_info=True)

builder = tfds.builder('mnist')
info = builder.info

print(info)

tfds.core.DatasetInfo(
    name='mnist',
    full_name='mnist/3.0.1',
    description="""
    The MNIST database of handwritten digits.
    """,
    homepage='http://yann.lecun.com/exdb/mnist/',
    data_dir='gs://tensorflow-datasets/datasets/mnist/3.0.1',
    file_format=tfrecord,
    download_size=11.06 MiB,
    dataset_size=21.00 MiB,
    features=FeaturesDict({
        'image': Image(shape=(28, 28, 1), dtype=uint8),
        'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
    }),
    supervised_keys=('image', 'label'),
    disable_shuffling=False,
    splits={
        'test': <SplitInfo num_examples=10000, num_shards=1>,
        'train': <SplitInfo num_examples=60000, num_shards=1>,
    },
    citation="""@article{lecun2010mnist,
      title={MNIST handwritten digit database},
      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
      volume={2},
      year={2010}
    }""",
)

info.features

FeaturesDict({
    'image': Image(shape=(28, 28, 1), dtype=uint8),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=10),
})

print(info.features["label"].num_classes)
print(info.features["label"].names)
print(info.features["label"].int2str(7))  # Human readable version (8 -> 'cat')
print(info.features["label"].str2int('7'))

10
['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
7
7

print(info.features.shape)
print(info.features.dtype)
print(info.features['image'].shape)
print(info.features['image'].dtype)

WARNING:absl:`FeatureConnector.dtype` is deprecated. Please change your code to use NumPy with the field `FeatureConnector.np_dtype` or use TensorFlow with the field `FeatureConnector.tf_dtype`.
WARNING:absl:`FeatureConnector.dtype` is deprecated. Please change your code to use NumPy with the field `FeatureConnector.np_dtype` or use TensorFlow with the field `FeatureConnector.tf_dtype`.
{'image': (28, 28, 1), 'label': ()}
{'image': tf.uint8, 'label': tf.int64}
(28, 28, 1)
<dtype: 'uint8'>

print(info.splits)

{'test': <SplitInfo num_examples=10000, num_shards=1>, 'train': <SplitInfo num_examples=60000, num_shards=1>}

print(list(info.splits.keys()))

['test', 'train']

print(info.splits['train'].num_examples)
print(info.splits['train'].filenames)
print(info.splits['train'].num_shards)

60000
['mnist-train.tfrecord-00000-of-00001']
1

print(info.splits['train[15%:75%]'].num_examples)
print(info.splits['train[15%:75%]'].file_instructions)

36000
[FileInstruction(filename='gs://tensorflow-datasets/datasets/mnist/3.0.1/mnist-train.tfrecord-00000-of-00001', skip=9000, take=36000, examples_in_shard=60000)]

@misc{TFDS,
  title = { {TensorFlow Datasets}, A collection of ready-to-use datasets},
  howpublished = {\url{https://www.tensorflow.org/datasets} },
}