Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 3,980 Bytes
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# 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
"""Movie reviews with human annotated rationales."""
from __future__ import absolute_import, division, print_function
import json
import os
import datasets
_CITATION = """
@unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
author = {Omar F. Zaidan and Jason Eisner and Christine Piatko},
title = {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
booktitle = {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
month = {December},
year = {2008}
}
"""
_DESCRIPTION = """
The movie rationale dataset contains human annotated rationales for movie
reviews.
"""
_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/movies.tar.gz"
class MovieRationales(datasets.GeneratorBasedBuilder):
"""Movie reviews with human annotated rationales."""
VERSION = datasets.Version("0.1.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"review": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=["NEG", "POS"]),
"evidences": datasets.features.Sequence(datasets.Value("string")),
}
),
supervised_keys=None,
homepage="http://www.cs.jhu.edu/~ozaidan/rationales/",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
data_dir = os.path.join(dl_dir, "movies")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
),
]
def _generate_examples(self, data_dir, filepath):
"""Yields examples."""
reviews_dir = os.path.join(data_dir, "docs")
with open(filepath, encoding="utf-8") as f:
for line in f:
row = json.loads(line)
doc_id = row["annotation_id"]
review_file = os.path.join(reviews_dir, doc_id)
with open(review_file, encoding="utf-8") as f1:
review_text = f1.read()
evidences = []
for evidence in row["evidences"]:
for e in evidence:
evidences.append(e["text"])
yield doc_id, {
"review": review_text,
"label": row["classification"],
"evidences": evidences,
}
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