DiscoEval / DiscoEval.py
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# 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 os
import io
import datasets
import constants
import pickle
import logging
_CITATION = """\
@InProceedings{mchen-discoeval-19,
title = {Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations},
author = {Mingda Chen and Zewei Chu and Kevin Gimpel},
booktitle = {Proc. of {EMNLP}},
year={2019}
}
"""
_DESCRIPTION = """\
This dataset contains all tasks of the DiscoEval benchmark for sentence representation learning.
"""
_HOMEPAGE = "https://github.com/ZeweiChu/DiscoEval"
class DiscoEvalSentence(datasets.GeneratorBasedBuilder):
"""DiscoEval Benchmark"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=constants.SPARXIV,
version=VERSION,
description="Sentence positioning dataset from arXiv",
),
datasets.BuilderConfig(
name=constants.SPROCSTORY,
version=VERSION,
description="Sentence positioning dataset from ROCStory",
),
datasets.BuilderConfig(
name=constants.SPWIKI,
version=VERSION,
description="Sentence positioning dataset from Wikipedia",
),
datasets.BuilderConfig(
name=constants.DCCHAT,
version=VERSION,
description="Discourse Coherence dataset from chat",
),
datasets.BuilderConfig(
name=constants.DCWIKI,
version=VERSION,
description="Discourse Coherence dataset from Wikipedia",
),
datasets.BuilderConfig(
name=constants.RST,
version=VERSION,
description="The RST Discourse Treebank dataset ",
),
datasets.BuilderConfig(
name=constants.PDTB_E,
version=VERSION,
description="The Penn Discourse Treebank - Explicit dataset.",
),
datasets.BuilderConfig(
name=constants.PDTB_I,
version=VERSION,
description="The Penn Discourse Treebank - Implicit dataset.",
),
datasets.BuilderConfig(
name=constants.SSPABS,
version=VERSION,
description="The SSP dataset.",
),
datasets.BuilderConfig(
name=constants.BSOARXIV,
version=VERSION,
description="The BSO Task with the arxiv dataset.",
),
datasets.BuilderConfig(
name=constants.BSOWIKI,
version=VERSION,
description="The BSO Task with the wiki dataset.",
),
datasets.BuilderConfig(
name=constants.BSOROCSTORY,
version=VERSION,
description="The BSO Task with the rocstory dataset.",
),
]
def _info(self):
if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.SP_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SP_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.BSOARXIV, constants.BSOWIKI, constants.BSOROCSTORY]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.BSO_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.BSO_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.DCCHAT, constants.DCWIKI]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.DC_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.DC_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.RST]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: [datasets.Value('string')]
for i in range(constants.RST_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.RST_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.PDTB_E]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.PDTB_E_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.PDTB_E_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.PDTB_I]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.PDTB_I_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.PDTB_I_LABELS)
features = datasets.Features(features_dict)
elif self.config.name in [constants.SSPABS]:
features_dict = {
constants.TEXT_COLUMN_NAME[i]: datasets.Value('string')
for i in range(constants.SSPABS_TEXT_COLUMNS)
}
features_dict[constants.LABEL_NAME] = datasets.ClassLabel(names=constants.SSPABS_LABELS)
features = datasets.Features(features_dict)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
if self.config.name in [constants.SPARXIV, constants.SPROCSTORY, constants.SPWIKI]:
data_dir = constants.SP_DATA_DIR + "/" + constants.SP_DIRS[self.config.name]
train_name = constants.SP_TRAIN_NAME
valid_name = constants.SP_VALID_NAME
test_name = constants.SP_TEST_NAME
elif self.config.name in [constants.BSOARXIV, constants.BSOWIKI, constants.BSOROCSTORY]:
data_dir = constants.BSO_DATA_DIR + "/" + constants.BSO_DIRS[self.config.name]
train_name = constants.BSO_TRAIN_NAME
valid_name = constants.BSO_VALID_NAME
test_name = constants.BSO_TEST_NAME
elif self.config.name in [constants.DCCHAT, constants.DCWIKI]:
data_dir = constants.DC_DATA_DIR + "/" + constants.DC_DIRS[self.config.name]
train_name = constants.DC_TRAIN_NAME
valid_name = constants.DC_VALID_NAME
test_name = constants.DC_TEST_NAME
elif self.config.name in [constants.RST]:
data_dir = constants.RST_DATA_DIR
train_name = constants.RST_TRAIN_NAME
valid_name = constants.RST_VALID_NAME
test_name = constants.RST_TEST_NAME
elif self.config.name in [constants.PDTB_E, constants.PDTB_I]:
data_dir = os.path.join(constants.PDTB_DATA_DIR, constants.PDTB_DIRS[self.config.name])
train_name = constants.PDTB_TRAIN_NAME
valid_name = constants.PDTB_VALID_NAME
test_name = constants.PDTB_TEST_NAME
elif self.config.name in [constants.SSPABS]:
data_dir = constants.SSPABS_DATA_DIR
train_name = constants.SSPABS_TRAIN_NAME
valid_name = constants.SSPABS_VALID_NAME
test_name = constants.SSPABS_TEST_NAME
urls_to_download = {
"train": data_dir + "/" + train_name,
"valid": data_dir + "/" + valid_name,
"test": data_dir + "/" + test_name,
}
logger = logging.getLogger(__name__)
data_dirs = dl_manager.download_and_extract(urls_to_download)
logger.info(f"Data directories: {data_dirs}")
downloaded_files = dl_manager.download_and_extract(data_dirs)
logger.info(f"Downloading Completed")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": downloaded_files['train'],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": downloaded_files['valid'],
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": downloaded_files['test'],
"split": "test"
},
),
]
def _generate_examples(self, filepath, split):
logger = logging.getLogger(__name__)
logger.info(f"Current working dir: {os.getcwd()}")
logger.info("generating examples from = %s", filepath)
if self.config.name == constants.RST:
data = pickle.load(open(filepath, "rb"))
for key, line in enumerate(data):
example = {constants.TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
example[constants.LABEL_NAME] = line[0]
yield key, example
else:
with io.open(filepath, mode='r', encoding='utf-8') as f:
for key, line in enumerate(f):
line = line.strip().split("\t")
example = {constants.TEXT_COLUMN_NAME[i]: sent for i, sent in enumerate(line[1:])}
example[constants.LABEL_NAME] = line[0]
yield key, example