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# coding=utf-8
# Copyright 2020 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
"""
Arguement Mining Dataset created by Stab , Gurevych et. al. CL 2017
"""
import datasets
import os
_CITATION = """\
@article{stab2017parsing,
title={Parsing argumentation structures in persuasive essays},
author={Stab, Christian and Gurevych, Iryna},
journal={Computational Linguistics},
volume={43},
number={3},
pages={619--659},
year={2017},
publisher={MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info~…}
}
"""
_DESCRIPTION = """\
tokens along with chunk id. IOB1 format Begining of arguement denoted by B-ARG,inside arguement
denoted by I-ARG, other chunks are O
Orginial train,test split as used by the paper is provided
"""
_URL = "https://raw.githubusercontent.com/Sam131112/Argument-Mining-Dataset/main/"
_TRAINING_FILE = "train.txt"
_TEST_FILE = "test.txt"
class ArguementMiningCL2017Config(datasets.BuilderConfig):
"""BuilderConfig for CL2017"""
def __init__(self, **kwargs):
"""BuilderConfig forCl2017.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(ArguementMiningCL2017Config, self).__init__(**kwargs)
class ArguementMiningCL2017(datasets.GeneratorBasedBuilder):
"""CL2017 dataset."""
BUILDER_CONFIGS = [
ArguementMiningCL2017Config(name="cl2017", version=datasets.Version("1.0.0"), description="Cl2017 dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"chunk_tags":datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-ARG",
"I-ARG",
]
)
),
}
),
supervised_keys=None,
homepage="https://direct.mit.edu/coli/article/43/3/619/1573/Parsing-Argumentation-Structures-in-Persuasive",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
urls_to_download = {
"train": _TRAINING_FILE,
"test": _TEST_FILE,
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
print("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
chunk_tags = []
ner_tags = []
for line in f:
if line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"chunk_tags": chunk_tags,
}
guid = guid+1
tokens = []
chunk_tags = []
else:
# cl2017 tokens are space separated
line=line.strip('\n')
splits = line.split("\t")
#print(splits)
tokens.append(splits[0])
chunk_tags.append(splits[1])
#print({"id": str(guid),"tokens": tokens,"chunk_tags": chunk_tags,})
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"chunk_tags": chunk_tags,
}
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