dataset_infos.json

{"ade_corpus_v2": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Sentence": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "ADE-related", "not ADE-related"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "ade_corpus_v2", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7700, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 728095, "num_examples": 5000, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 735795, "size_in_bytes": 10487979}, "banking_77": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Query": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 78, "names": ["Unlabeled", "Refund_not_showing_up", "activate_my_card", "age_limit", "apple_pay_or_google_pay", "atm_support", "automatic_top_up", "balance_not_updated_after_bank_transfer", "balance_not_updated_after_cheque_or_cash_deposit", "beneficiary_not_allowed", "cancel_transfer", "card_about_to_expire", "card_acceptance", "card_arrival", "card_delivery_estimate", "card_linking", "card_not_working", "card_payment_fee_charged", "card_payment_not_recognised", "card_payment_wrong_exchange_rate", "card_swallowed", "cash_withdrawal_charge", "cash_withdrawal_not_recognised", "change_pin", "compromised_card", "contactless_not_working", "country_support", "declined_card_payment", "declined_cash_withdrawal", "declined_transfer", "direct_debit_payment_not_recognised", "disposable_card_limits", "edit_personal_details", "exchange_charge", "exchange_rate", "exchange_via_app", "extra_charge_on_statement", "failed_transfer", "fiat_currency_support", "get_disposable_virtual_card", "get_physical_card", "getting_spare_card", "getting_virtual_card", "lost_or_stolen_card", "lost_or_stolen_phone", "order_physical_card", "passcode_forgotten", "pending_card_payment", "pending_cash_withdrawal", "pending_top_up", "pending_transfer", "pin_blocked", "receiving_money", "request_refund", "reverted_card_payment?", "supported_cards_and_currencies", "terminate_account", "top_up_by_bank_transfer_charge", "top_up_by_card_charge", "top_up_by_cash_or_cheque", "top_up_failed", "top_up_limits", "top_up_reverted", "topping_up_by_card", "transaction_charged_twice", "transfer_fee_charged", "transfer_into_account", "transfer_not_received_by_recipient", "transfer_timing", "unable_to_verify_identity", "verify_my_identity", "verify_source_of_funds", "verify_top_up", "virtual_card_not_working", "visa_or_mastercard", "why_verify_identity", "wrong_amount_of_cash_received", "wrong_exchange_rate_for_cash_withdrawal"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "banking_77", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 3471, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 395773, "num_examples": 5000, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 399244, "size_in_bytes": 10151428}, "terms_of_service": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Sentence": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "not potentially unfair", "potentially unfair"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "terms_of_service", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11046, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 980828, "num_examples": 5000, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 991874, "size_in_bytes": 10744058}, "tai_safety_research": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Title": {"dtype": "string", "id": null, "_type": "Value"}, "Abstract Note": {"dtype": "string", "id": null, "_type": "Value"}, "Url": {"dtype": "string", "id": null, "_type": "Value"}, "Publication Year": {"dtype": "string", "id": null, "_type": "Value"}, "Item Type": {"dtype": "string", "id": null, "_type": "Value"}, "Author": {"dtype": "string", "id": null, "_type": "Value"}, "Publication Title": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "TAI safety research", "not TAI safety research"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "tai_safety_research", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 55032, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 1640464, "num_examples": 1639, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 1695496, "size_in_bytes": 11447680}, "neurips_impact_statement_risks": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Paper title": {"dtype": "string", "id": null, "_type": "Value"}, "Paper link": {"dtype": "string", "id": null, "_type": "Value"}, "Impact statement": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "doesn't mention a harmful application", "mentions a harmful application"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "neurips_impact_statement_risks", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 69143, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 199115, "num_examples": 150, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 268258, "size_in_bytes": 10020442}, "overruling": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Sentence": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "not overruling", "overruling"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "overruling", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7522, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 440198, "num_examples": 2350, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 447720, "size_in_bytes": 10199904}, "systematic_review_inclusion": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Title": {"dtype": "string", "id": null, "_type": "Value"}, "Abstract": {"dtype": "string", "id": null, "_type": "Value"}, "Authors": {"dtype": "string", "id": null, "_type": "Value"}, "Journal": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "included", "not included"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "systematic_review_inclusion", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 52787, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 2352268, "num_examples": 2244, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 2405055, "size_in_bytes": 12157239}, "one_stop_english": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Article": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 4, "names": ["Unlabeled", "advanced", "elementary", "intermediate"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "one_stop_english", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 201542, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 2091973, "num_examples": 518, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 2293515, "size_in_bytes": 12045699}, "tweet_eval_hate": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Tweet": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "hate speech", "not hate speech"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "tweet_eval_hate", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7586, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 450920, "num_examples": 2966, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 458506, "size_in_bytes": 10210690}, "twitter_complaints": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Tweet text": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 3, "names": ["Unlabeled", "complaint", "no complaint"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "twitter_complaints", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5446, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 382168, "num_examples": 3399, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 387614, "size_in_bytes": 10139798}, "semiconductor_org_types": {"description": "Large pre-trained language models have shown promise for few-shot learning, completing text-based tasks given only a few task-specific examples. Will models soon solve classification tasks that have so far been reserved for human research assistants? \n\n[RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models:\n\n- across multiple domains (lit review, tweets, customer interaction, etc.)\n- on economically valuable classification tasks (someone inherently cares about the task)\n- in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set)\n", "citation": "@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n", "homepage": "https://raft.elicit.org", "license": "", "features": {"Paper title": {"dtype": "string", "id": null, "_type": "Value"}, "Organization name": {"dtype": "string", "id": null, "_type": "Value"}, "ID": {"dtype": "string", "id": null, "_type": "Value"}, "Label": {"num_classes": 4, "names": ["Unlabeled", "company", "research institute", "university"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "raft", "config_name": "semiconductor_org_types", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8345, "num_examples": 50, "dataset_name": "raft"}, "test": {"name": "test", "num_bytes": 75173, "num_examples": 449, "dataset_name": "raft"}}, "download_checksums": {"data/ade_corpus_v2/train.csv": {"num_bytes": 7788, "checksum": "b6c1268b8ce0fbf4fcaa0ee88562354b65d7ebd2527c3c3cc875e3a47757ae36"}, "data/ade_corpus_v2/test_unlabeled.csv": {"num_bytes": 661890, "checksum": "28981e898281755e1c2d460ab75cceb8f9d8ce0a88ff4636153cf8c393c6f99e"}, "data/banking_77/train.csv": {"num_bytes": 3914, "checksum": "8c417cea66d12569cb3057b125a98358aea0642dda67cf6deea52d4ec1c28ae1"}, "data/banking_77/test_unlabeled.csv": {"num_bytes": 327152, "checksum": "aebd62e56a3b8ebf618afa519cc261fedc7700d96053357ebff83fab44bbaeac"}, "data/terms_of_service/train.csv": {"num_bytes": 11528, "checksum": "8b46784b1e601d753ad44ce33fced828ec3bea95b6b575a2195322ead807f6c7"}, "data/terms_of_service/test_unlabeled.csv": {"num_bytes": 917272, "checksum": "491679e8c9303f825d341dcdfb0a5779a9d2f925744cc44c0039b219abf7d83f"}, "data/tai_safety_research/train.csv": {"num_bytes": 54758, "checksum": "fac15dde8a9f5536424289aa707dcfe1fa3128a7ee2d813cf3251c53092f7dbc"}, "data/tai_safety_research/test_unlabeled.csv": {"num_bytes": 1594375, "checksum": "e606d7858813490507d90d3d17830c6f1ebd8c777fc4a9f865699e375be355d0"}, "data/neurips_impact_statement_risks/train.csv": {"num_bytes": 70008, "checksum": "76cd6aa2707cd99445ba69f8c392a89faa4bf718e967b07dc457f88529b3ff2e"}, "data/neurips_impact_statement_risks/test_unlabeled.csv": {"num_bytes": 196429, "checksum": "5e4d1f313f92dad3e06e035eaff044f818b20ce1ed4d22fcd17e7756e54089a7"}, "data/overruling/train.csv": {"num_bytes": 7585, "checksum": "617061ba0dbd501d74896ccf2f9ebed00c35c4c4057f74afdd94c96cb514130e"}, "data/overruling/test_unlabeled.csv": {"num_bytes": 412483, "checksum": "c0a61fc8980b544611f8d662c752d88e2ed635146f2f3c72f16eec60e3ddb7fe"}, "data/systematic_review_inclusion/train.csv": {"num_bytes": 52491, "checksum": "2d2acd76f6acf1fd1359cf07323586fa134bbe76689e112f5c213b63271e2318"}, "data/systematic_review_inclusion/test_unlabeled.csv": {"num_bytes": 2309274, "checksum": "cd7b9192d4fb46e432d97a0640b6dd12bc4ae7a8908ccbfc4cf401abb662aedd"}, "data/one_stop_english/train.csv": {"num_bytes": 201489, "checksum": "8c0102ea703677c23c3525e3bc7504c4b734d9298c5d3a25403d274034cb9d89"}, "data/one_stop_english/test_unlabeled.csv": {"num_bytes": 2085757, "checksum": "9046d777e1774f4000108ed39aa37f8aca4c8fe96f7f2f4bd8c0b2d004b0d035"}, "data/tweet_eval_hate/train.csv": {"num_bytes": 7642, "checksum": "ab091c90e8afbb0dabe17cc6be93b1b80fd914e00a12d66b2dd7b194f355ba65"}, "data/tweet_eval_hate/test_unlabeled.csv": {"num_bytes": 412052, "checksum": "707e720c60f8beb359301994c691a609367cf3bf27d0c573d22f9e47ecc63204"}, "data/twitter_complaints/train.csv": {"num_bytes": 5376, "checksum": "365b067c2d2a7f30128d766baf4901936e047295bb2a693878164dbb84d828fa"}, "data/twitter_complaints/test_unlabeled.csv": {"num_bytes": 336272, "checksum": "7f6f975ec98a5b467b39c487bbc711b29e177119d12c49c79b31faba83609082"}, "data/semiconductor_org_types/train.csv": {"num_bytes": 8120, "checksum": "b7e238cbfd0ed518c222b64e8656599ed13872de135c6e38d6f6123c355d2f64"}, "data/semiconductor_org_types/test_unlabeled.csv": {"num_bytes": 68529, "checksum": "141d8b36685475310c98cdb16099acbf04498d80349f4434c2201deffd271f24"}}, "download_size": 9752184, "post_processing_size": null, "dataset_size": 83518, "size_in_bytes": 9835702}}