# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import csv import json import os from pathlib import Path import datasets # TODO: Add BibTeX citation _CITATION = """\ @InProceedings{huggingface:dataset, title = {A great new dataset}, author={huggingface, Inc. }, year={2020} } """ _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? [RAFT](https://raft.elicit.org) is a few-shot classification benchmark that tests language models: - across multiple domains (lit review, tweets, customer interaction, etc.) - on economically valuable classification tasks (someone inherently cares about the task) - in a setting that mirrors deployment (50 examples per task, info retrieval allowed, hidden test set) """ _HOMEPAGE = "https://raft.elicit.org" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" DATA_DIR = "data/" TASKS = { "ade_corpus_v2": { "name": "ade_corpus_v2", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["ADE-related", "not ADE-related"]}, }, "banking_77": { "name": "banking_77", "description": "", "data_columns": ["Query", "ID"], "label_columns": { "Label": [ "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", ] }, }, "terms_of_service": { "name": "terms_of_service", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["not potentially unfair", "potentially unfair"]}, }, "tai_safety_research": { "name": "tai_safety_research", "description": "", "data_columns": [ "Title", "Abstract Note", "Url", "Publication Year", "Item Type", "Author", "Publication Title", "ID", ], "label_columns": {"Label": ["TAI safety research", "not TAI safety research"]}, }, "neurips_impact_statement_risks": { "name": "neurips_impact_statement_risks", "description": "", "data_columns": ["Paper title", "Paper link", "Impact statement", "ID"], "label_columns": {"Label": ["doesn't mention a harmful application", "mentions a harmful application"]}, }, "overruling": { "name": "overruling", "description": "", "data_columns": ["Sentence", "ID"], "label_columns": {"Label": ["not overruling", "overruling"]}, }, "systematic_review_inclusion": { "name": "systematic_review_inclusion", "description": "", "data_columns": ["Title", "Abstract", "Authors", "Journal", "ID"], "label_columns": {"Label": ["included", "not included"]}, }, "one_stop_english": { "name": "one_stop_english", "description": "", "data_columns": ["Article", "ID"], "label_columns": {"Label": ["advanced", "elementary", "intermediate"]}, }, "tweet_eval_hate": { "name": "tweet_eval_hate", "description": "", "data_columns": ["Tweet", "ID"], "label_columns": {"Label": ["hate speech", "not hate speech"]}, }, "twitter_complaints": { "name": "twitter_complaints", "description": "", "data_columns": ["Tweet text", "ID"], "label_columns": {"Label": ["complaint", "no complaint"]}, }, "semiconductor_org_types": { "name": "semiconductor_org_types", "description": "", "data_columns": ["Paper title", "Organization name", "ID"], "label_columns": {"Label": ["company", "research institute", "university"]}, }, } _URLs = {s: {"train": f"{DATA_DIR}{s}/train.csv", "test": f"{DATA_DIR}{s}/test_unlabeled.csv"} for s in TASKS} class Raft(datasets.GeneratorBasedBuilder): """RAFT Dataset""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [] for key in TASKS: td = TASKS[key] name = td["name"] description = td["description"] BUILDER_CONFIGS.append(datasets.BuilderConfig(name=name, version=VERSION, description=description)) DEFAULT_CONFIG_NAME = ( "tai_safety_research" # It's not mandatory to have a default configuration. Just use one if it make sense. ) def _info(self): DEFAULT_LABEL_NAME = "Unlabeled" task = TASKS[self.config.name] data_columns = {col_name: (datasets.Value("string") if col_name != "ID" else datasets.Value("int32")) for col_name in task["data_columns"]} label_columns = {} for label_name in task["label_columns"]: labels = [DEFAULT_LABEL_NAME] + task["label_columns"][label_name] label_columns[label_name] = datasets.ClassLabel(len(labels), labels) # Merge dicts features = datasets.Features(**data_columns, **label_columns) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset. supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive data_dir = dl_manager.download_and_extract(_URLs) dataset = self.config.name return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir[dataset]["train"], "split": "train"} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir[dataset]["test"], "split": "test"} ), ] def _generate_examples( self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` ): """Yields examples as (key, example) tuples.""" # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is here for legacy reason (tfds) and is not important in itself. task = TASKS[self.config.name] labels = list(task["label_columns"]) with open(filepath, encoding="utf-8") as f: csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True) column_names = next(csv_reader) # Test csvs don't have any label columns. if split == "test": column_names += labels for id_, row in enumerate(csv_reader): if split == "test": row += ["Unlabeled"] * len(labels) # dicts don't have inherent ordering in python, right?? yield id_, {name: value for name, value in zip(column_names, row)}