# 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 """Commonsense Explanations (CoS-E) Dataset.""" import json import datasets _CITATION = """ @inproceedings{rajani2019explain, title = {Explain Yourself! Leveraging Language models for Commonsense Reasoning}, author = {Rajani, Nazneen Fatema and McCann, Bryan and Xiong, Caiming and Socher, Richard} year={2019} booktitle = {Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)} url ={https://arxiv.org/abs/1906.02361} } """ _DESCRIPTION = """ Common Sense Explanations (CoS-E) allows for training language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. """ _COS_E_URL = "https://raw.githubusercontent.com/salesforce/cos-e/master/data/" # COS E has explanations for the CQA dataset, which is joined by ID. _CQA_V1_11_URL_TRAIN = "https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl" _CQA_V1_11_URL_DEV = "https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl" _CQA_V1_11_URL_TEST = "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl" _CQA_V1_0_URL_TRAIN = _COS_E_URL + "v1.0/train_rand_split.jsonl" _CQA_V1_0_URL_DEV = _COS_E_URL + "v1.0/dev_rand_split.jsonl" _CQA_V1_0_URL_TEST = _COS_E_URL + "v1.0/test_rand_split_no_answers.jsonl" def _download_and_index_cqa(dl_manager, name): """Downloads CQA and returns it, indexed by id, for joining with Cos-E.""" downloaded_files = dl_manager.download_and_extract( { "cqa_train": _CQA_V1_11_URL_TRAIN if name == "v1.11" else _CQA_V1_0_URL_TRAIN, "cqa_dev": _CQA_V1_11_URL_DEV if name == "v1.11" else _CQA_V1_0_URL_DEV, "cqa_test": _CQA_V1_11_URL_TEST if name == "v1.11" else _CQA_V1_0_URL_TEST, } ) # NB: "cqa_test" is included in the files, but not in any of the CoS-E splits. cqa_splits = ["cqa_train", "cqa_dev"] cqa_complete = [] for split in cqa_splits: with open(downloaded_files[split], encoding="utf-8") as f: for _, line in enumerate(f): d = json.loads(line) cqa_complete.append(d) # Index the CQA dataset by id for joining with Cos-E. cqa_indexed = {} for d in cqa_complete: cqa_indexed[d["id"]] = d return cqa_indexed def _get_choices_and_answer(cqa): """Returns choices and the answer from a cqa example.""" choices = [] answer_key = cqa["answerKey"] answer = None for choice in cqa["question"]["choices"]: choices.append(choice["text"]) if answer_key == choice["label"]: answer = choice["text"] return choices, answer class CosEConfig(datasets.BuilderConfig): """BuilderConfig for CosE""" def __init__(self, **kwargs): """ Args: **kwargs: keyword arguments forwarded to super. """ super(CosEConfig, self).__init__(**kwargs) class CosE(datasets.GeneratorBasedBuilder): """CoS-E: Common Sense Explanations corpus.""" BUILDER_CONFIGS = [ CosEConfig( name="v1.0", description="cos-e version 1.0", version=datasets.Version("1.0.0", ""), ), CosEConfig( name="v1.11", description="cos-e version 1.11", version=datasets.Version("1.11.0", ""), ), ] def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "question": datasets.Value("string"), "choices": datasets.features.Sequence(datasets.Value("string")), "answer": datasets.Value("string"), "abstractive_explanation": datasets.Value("string"), "extractive_explanation": datasets.Value("string"), } ), supervised_keys=None, homepage="https://github.com/salesforce/cos-e", citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # NB: The CQA Dataset should be read only once, and only by callers who # want to _create_ the Cos-E dataset from scratch. cqa_indexed = _download_and_index_cqa(dl_manager, self.config.name) if self.config.name == "v1.11": files = dl_manager.download_and_extract( { "dev": [_COS_E_URL + "v1.11/cose_dev_v1.11_processed.jsonl"], "train": [_COS_E_URL + "v1.11/cose_train_v1.11_processed.jsonl"], } ) elif self.config.name == "v1.0": files = dl_manager.download_and_extract( { "dev": [_COS_E_URL + "v1.0/cose_dev_v1.0_processed.jsonl"], "train": [_COS_E_URL + "v1.0/cose_train_v1.0_processed.jsonl"], } ) else: raise ValueError("Unknown config name") # We use the CoS-E/CQA dev set as our validation set. return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"files": files["train"], "cqa_indexed": cqa_indexed}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"files": files["dev"], "cqa_indexed": cqa_indexed}, ), ] def _generate_examples(self, files, **kwargs): """Yields examples.""" cqa_indexed = kwargs["cqa_indexed"] for filepath in files: with open(filepath, encoding="utf-8") as f: for line in f: cos = json.loads(line) cqa = cqa_indexed[cos["id"]] choices, answer = _get_choices_and_answer(cqa) yield cos["id"], { "id": cos["id"], "question": cqa["question"]["stem"], "choices": choices, "answer": answer, "abstractive_explanation": cos["explanation"]["open-ended"], "extractive_explanation": cos["explanation"]["selected"], }