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Update files from the datasets library (from 1.0.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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cos_e.py ADDED
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+ # coding=utf-8
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+ # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+
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+ # Lint as: python3
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+ """Commonsense Explanations (CoS-E) Dataset."""
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+
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+ from __future__ import absolute_import, division, print_function
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+
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+ import json
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+ import os
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+
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+ import datasets
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+
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+
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+ _CITATION = """
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+ @inproceedings{rajani2019explain,
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+ title = {Explain Yourself! Leveraging Language models for Commonsense Reasoning},
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+ author = {Rajani, Nazneen Fatema and
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+ McCann, Bryan and
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+ Xiong, Caiming and
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+ Socher, Richard}
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+ year={2019}
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+ booktitle = {Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)}
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+ url ={https://arxiv.org/abs/1906.02361}
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+ }
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+ """
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+
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+ _DESCRIPTION = """
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+ Common Sense Explanations (CoS-E) allows for training language models to
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+ automatically generate explanations that can be used during training and
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+ inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.
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+ """
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+
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+ _COS_E_URL = "https://raw.githubusercontent.com/salesforce/cos-e/master/data/"
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+
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+ # COS E has explanations for the CQA dataset, which is joined by ID.
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+ _CQA_V1_11_URL_TRAIN = "https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl"
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+ _CQA_V1_11_URL_DEV = "https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl"
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+ _CQA_V1_11_URL_TEST = "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl"
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+
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+ _CQA_V1_0_URL_TRAIN = os.path.join(_COS_E_URL, "v1.0/train_rand_split.jsonl")
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+ _CQA_V1_0_URL_DEV = os.path.join(_COS_E_URL, "v1.0/dev_rand_split.jsonl")
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+ _CQA_V1_0_URL_TEST = os.path.join(_COS_E_URL, "v1.0/test_rand_split_no_answers.jsonl")
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+
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+
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+ def _download_and_index_cqa(dl_manager, name):
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+ """Downloads CQA and returns it, indexed by id, for joining with Cos-E."""
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+
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+ downloaded_files = dl_manager.download_and_extract(
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+ {
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+ "cqa_train": _CQA_V1_11_URL_TRAIN if name == "v1.11" else _CQA_V1_0_URL_TRAIN,
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+ "cqa_dev": _CQA_V1_11_URL_DEV if name == "v1.11" else _CQA_V1_0_URL_DEV,
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+ "cqa_test": _CQA_V1_11_URL_TEST if name == "v1.11" else _CQA_V1_0_URL_TEST,
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+ }
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+ )
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+
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+ # NB: "cqa_test" is included in the files, but not in any of the CoS-E splits.
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+ cqa_splits = ["cqa_train", "cqa_dev"]
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+ cqa_complete = []
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+ for split in cqa_splits:
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+ with open(downloaded_files[split], encoding="utf-8") as f:
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+ for _, line in enumerate(f):
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+ d = json.loads(line)
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+ cqa_complete.append(d)
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+
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+ # Index the CQA dataset by id for joining with Cos-E.
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+ cqa_indexed = {}
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+ for d in cqa_complete:
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+ cqa_indexed[d["id"]] = d
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+ return cqa_indexed
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+
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+
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+ def _get_choices_and_answer(cqa):
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+ """Returns choices and the answer from a cqa example."""
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+ choices = []
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+ answer_key = cqa["answerKey"]
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+ answer = None
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+ for choice in cqa["question"]["choices"]:
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+ choices.append(choice["text"])
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+ if answer_key == choice["label"]:
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+ answer = choice["text"]
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+ return choices, answer
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+
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+
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+ class CosEConfig(datasets.BuilderConfig):
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+
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+ """ BuilderConfig for CosE"""
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+
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+ def __init__(self, **kwargs):
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+ """
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+
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+ Args:
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+ **kwargs: keyword arguments forwarded to super.
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+ """
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+ super(CosEConfig, self).__init__(**kwargs)
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+
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+
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+ class CosE(datasets.GeneratorBasedBuilder):
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+ """CoS-E: Common Sense Explanations corpus."""
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+
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+ BUILDER_CONFIGS = [
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+ CosEConfig(
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+ name="v1.0",
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+ description="cos-e version 1.0",
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+ version=datasets.Version("1.0.0", ""),
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+ ),
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+ CosEConfig(
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+ name="v1.11",
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+ description="cos-e version 1.11",
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+ version=datasets.Version("1.11.0", ""),
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+ ),
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+ ]
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+
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+ def _info(self):
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+ return datasets.DatasetInfo(
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+ description=_DESCRIPTION,
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+ features=datasets.Features(
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+ {
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+ "id": datasets.Value("string"),
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+ "question": datasets.Value("string"),
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+ "choices": datasets.features.Sequence(datasets.Value("string")),
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+ "answer": datasets.Value("string"),
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+ "abstractive_explanation": datasets.Value("string"),
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+ "extractive_explanation": datasets.Value("string"),
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+ }
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+ ),
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+ supervised_keys=None,
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+ homepage="https://github.com/salesforce/cos-e",
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+
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+ # NB: The CQA Dataset should be read only once, and only by callers who
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+ # want to _create_ the Cos-E dataset from scratch.
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+ cqa_indexed = _download_and_index_cqa(dl_manager, self.config.name)
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+
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+ if self.config.name == "v1.11":
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+ files = dl_manager.download_and_extract(
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+ {
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+ "dev": [os.path.join(_COS_E_URL, "v1.11/cose_dev_v1.11_processed.jsonl")],
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+ "train": [os.path.join(_COS_E_URL, "v1.11/cose_train_v1.11_processed.jsonl")],
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+ }
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+ )
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+
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+ elif self.config.name == "v1.0":
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+ files = dl_manager.download_and_extract(
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+ {
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+ "dev": [os.path.join(_COS_E_URL, "v1.0/cose_dev_v1.0_processed.jsonl")],
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+ "train": [os.path.join(_COS_E_URL, "v1.0/cose_train_v1.0_processed.jsonl")],
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+ }
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+ )
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+ else:
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+ raise ValueError("Unknown config name")
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+ # We use the CoS-E/CQA dev set as our validation set.
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ gen_kwargs={"files": files["train"], "cqa_indexed": cqa_indexed},
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+ ),
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+ datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ gen_kwargs={"files": files["dev"], "cqa_indexed": cqa_indexed},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, files, **kwargs):
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+ """Yields examples."""
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+ cqa_indexed = kwargs["cqa_indexed"]
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+ for filepath in files:
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+ with open(filepath, encoding="utf-8") as f:
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+ for line in f:
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+ cos = json.loads(line)
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+ cqa = cqa_indexed[cos["id"]]
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+ choices, answer = _get_choices_and_answer(cqa)
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+ yield cos["id"], {
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+ "id": cos["id"],
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+ "question": cqa["question"]["stem"],
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+ "choices": choices,
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+ "answer": answer,
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+ "abstractive_explanation": cos["explanation"]["open-ended"],
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+ "extractive_explanation": cos["explanation"]["selected"],
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+ }
dataset_infos.json ADDED
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+ {"v1.0": {"description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n", "citation": "\n@inproceedings{rajani2019explain,\n title = \"Explain Yourself! Leveraging Language models for Commonsense Reasoning\",\n author = \"Rajani, Nazneen Fatema and\n McCann, Bryan and\n Xiong, Caiming and\n Socher, Richard\",\n year=\"2019\",\n booktitle = \"Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)\",\n url =\"https://arxiv.org/abs/1906.02361\"\n}\n", "homepage": "https://github.com/salesforce/cos-e", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "abstractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}, "extractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "cos_e", "config_name": "v1.0", "version": {"version_str": "1.0.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2077517, "num_examples": 7610, "dataset_name": "cos_e"}, "validation": {"name": "validation", "num_bytes": 261887, "num_examples": 950, "dataset_name": "cos_e"}}, "download_checksums": {"https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/train_rand_split.jsonl": {"num_bytes": 2160200, "checksum": "1989ce97e24d8572113d6a18f44e0f11ee9d206fb9bf9a1133937645583e697e"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/dev_rand_split.jsonl": {"num_bytes": 268531, "checksum": "790dd2a8492e7f3b51ded04116de603115b7acaded32ea84f6a7101f9d571ac1"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/test_rand_split_no_answers.jsonl": {"num_bytes": 250752, "checksum": "b9c3d1319667ea1569be6f7b3ed0546bd8222d2f3a759f928307343a0282e190"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/cose_dev_v1.0_processed.jsonl": {"num_bytes": 182444, "checksum": "ab7b8ac91bca1a6ba798816af6aca703a739f576c919360ddc376d9d3046be53"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.0/cose_train_v1.0_processed.jsonl": {"num_bytes": 1433393, "checksum": "df9f83ac4891f38e0771470858d5f1c4b5bb08fee5c53f38f9df9b3d3675ea74"}}, "download_size": 4295320, "dataset_size": 2339404, "size_in_bytes": 6634724}, "v1.11": {"description": "\nCommon Sense Explanations (CoS-E) allows for training language models to\nautomatically generate explanations that can be used during training and\ninference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.\n", "citation": "\n@inproceedings{rajani2019explain,\n title = \"Explain Yourself! Leveraging Language models for Commonsense Reasoning\",\n author = \"Rajani, Nazneen Fatema and\n McCann, Bryan and\n Xiong, Caiming and\n Socher, Richard\",\n year=\"2019\",\n booktitle = \"Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)\",\n url =\"https://arxiv.org/abs/1906.02361\"\n}\n", "homepage": "https://github.com/salesforce/cos-e", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "choices": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "abstractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}, "extractive_explanation": {"dtype": "string", "id": null, "_type": "Value"}}, "supervised_keys": null, "builder_name": "cos_e", "config_name": "v1.11", "version": {"version_str": "1.11.0", "description": "", "datasets_version_to_prepare": null, "major": 1, "minor": 11, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2717420, "num_examples": 9741, "dataset_name": "cos_e"}, "validation": {"name": "validation", "num_bytes": 331760, "num_examples": 1221, "dataset_name": "cos_e"}}, "download_checksums": {"https://s3.amazonaws.com/commensenseqa/train_rand_split.jsonl": {"num_bytes": 3785890, "checksum": "58ffa3c8472410e24b8c43f423d89c8a003d8284698a6ed7874355dedd09a2fb"}, "https://s3.amazonaws.com/commensenseqa/dev_rand_split.jsonl": {"num_bytes": 471653, "checksum": "3210497fdaae614ac085d9eb873dd7f4d49b6f965a93adadc803e1229fd8a02a"}, "https://s3.amazonaws.com/commensenseqa/test_rand_split_no_answers.jsonl": {"num_bytes": 423148, "checksum": "b426896d71a9cd064cf01cfaf6e920817c51701ef66028883ac1af2e73ad5f29"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.11/cose_dev_v1.11_processed.jsonl": {"num_bytes": 200867, "checksum": "a8367c94901ba249e48bcec76eaff9e7b91cec0f0e4d94879975d7d1b952bc41"}, "https://raw.githubusercontent.com/salesforce/cos-e/master/data/v1.11/cose_train_v1.11_processed.jsonl": {"num_bytes": 1653976, "checksum": "4c0ccfd34243cf7af62b441643437769663edcb980b991487f766b97a547e9bd"}}, "download_size": 6535534, "dataset_size": 3049180, "size_in_bytes": 9584714}}
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