Datasets:
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License:
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
| # | |
| # 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. | |
| # Lint as: python3 | |
| """FeedbackQA: An Retrieval-based Question Answering Dataset with User Feedback""" | |
| import json | |
| import datasets | |
| import os | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """ | |
| """ | |
| _DESCRIPTION = """\ | |
| FeedbackQA is a retrieval-based QA dataset \ | |
| that contains interactive feedback from users. \ | |
| It has two parts: the first part contains a conventional RQA dataset, \ | |
| whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs. | |
| """ | |
| #_URLS = { | |
| # "train": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/46bd763229fc603d73f634a312367acb83c3b713a5dfd9fcf8a9b3e310c39a67", | |
| # "dev": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/40a93282e5fdee4706c20e32ddd4734151139d67f6844dbcffb9e7be22ae6b8f", | |
| # "test": "https://cdn-lfs.huggingface.co/datasets/McGill-NLP/FeedbackQA/50c4a21dc778cf064f731161e2213f21d2951cabd9331a1c524f791055040d02" | |
| #} | |
| _URL = 'https://drive.google.com/uc?export=download&id=14KV6yKgdjzb6fbFzshGuNvEp9zGv_gol' | |
| class FeedbackConfig(datasets.BuilderConfig): | |
| """BuilderConfig for FeedbackQA.""" | |
| def __init__(self, **kwargs): | |
| """BuilderConfig for FeedbackQA. | |
| Args: | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| super(FeedbackConfig, self).__init__(**kwargs) | |
| class FeedbackQA(datasets.GeneratorBasedBuilder): | |
| """FeedbackQA: retrieval-based QA dataset that contains interactive feedback from users.""" | |
| BUILDER_CONFIGS = [ | |
| FeedbackConfig( | |
| name="plain_text", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Plain text", | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| #"id": datasets.Value("string"), | |
| #"title": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "answer": datasets.Value("string"), | |
| "feedback": datasets.features.Sequence( | |
| { | |
| "rating": datasets.Value("string"), | |
| "explanation": datasets.Value("string"), | |
| } | |
| ), | |
| } | |
| ), | |
| # No default supervised_keys (as we have to pass both question | |
| # and context as input). | |
| supervised_keys=None, | |
| homepage="https://mcgill-nlp.github.io/feedbackQA_data/", | |
| citation=_CITATION | |
| ) | |
| def _split_generators(self, dl_manager): | |
| downloaded_files_path = dl_manager.download_and_extract(_URL) | |
| train_file = os.path.join(downloaded_files_path, 'feedback_train.json') | |
| val_file = os.path.join(downloaded_files_path, 'feedback_valid.json') | |
| test_file = os.path.join(downloaded_files_path, 'feedback_test.json') | |
| print(test_file) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}), | |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_file}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", filepath) | |
| key = 0 | |
| with open(filepath, encoding="utf-8") as f: | |
| fbqa = json.load(f) | |
| for dict_item in fbqa: | |
| question = dict_item['question'] | |
| passage_text = '' | |
| if dict_item['passage']['reference']['page_title']: | |
| passage_text += dict_item['passage']['reference']['page_title'] + '\n' | |
| if dict_item['passage']['reference']['section_headers']: | |
| passage_text += '\n'.join(dict_item['passage']['reference']['section_headers']) + '\n' | |
| if dict_item['passage']['reference']['section_content']: | |
| passage_text += dict_item['passage']['reference']['section_content'] | |
| yield key, { | |
| "question": question, | |
| "answer": passage_text, | |
| "feedback": { | |
| "rating": dict_item['rating'], | |
| "explanation": dict_item['feedback'], | |
| }, | |
| } | |
| key += 1 |