Delete loading script

raft.py

@@ -1,295 +0,0 @@
-# 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)}