Datasets:

ought
/

raft

	# 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)}