raft.py

# 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_URL = "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_URL}{s}/train.csv", "test": f"{DATA_DIR_URL}{s}/test_unlabeled.csv"} for s in TASKS}


class Raft(datasets.GeneratorBasedBuilder):
    """RAFT Dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    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):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        DEFAULT_LABEL_NAME = "Unlabeled"

        task = TASKS[self.config.name]
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        data_columns = {col_name: datasets.Value("string") 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)}