Datasets:

clarin-pl
/

polemo2-official

	# coding=utf-8
	# Copyright 2021 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
	"""PolEmo2 dataset."""
	from dataclasses import dataclass
	from typing import List, Dict, Generator, Union, Optional, Tuple

	import datasets

	_DESCRIPTION = """PolEmo 2.0: Corpus of Multi-Domain Consumer Reviews, evaluation data for article presented at CoNLL."""
	_CITATION = """
	@inproceedings{kocon-etal-2019-multi,
	title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
	author = "Koco{\'n}, Jan and
	Mi{\l}kowski, Piotr and
	Za{\'s}ko-Zieli{\'n}ska, Monika",
	booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
	month = nov,
	year = "2019",
	address = "Hong Kong, China",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/K19-1092",
	doi = "10.18653/v1/K19-1092",
	pages = "980--991",}
	"""
	_HOMEPAGE = "https://clarin-pl.eu/dspace/handle/11321/710"
	_LICENSE = "CC-BY-4.0"

	_DOMAINS = [
	"all",
	"hotels",
	"medicine",
	"products",
	"reviews",
	]
	_OUT_DOMAINS = ["Nhotels", "Nmedicine", "Nproducts", "Nreviews"]
	_CONFIGS_TEXT = ["text", "sentence"]

	_LABELS = ["zero", "minus", "plus", "amb"]

	URL_PATH = (
	"https://huggingface.co/datasets/clarin-pl/polemo2-official/resolve/main/data"
	)
	_URLS = {
	cfg: {
	**{
	domain: {
	split_type: f"{URL_PATH}/{domain}.{cfg}.{split_type}.txt"
	for split_type in ["train", "dev", "test"]
	}
	for domain in _DOMAINS
	},
	**{
	domain: {
	split_type: f"{URL_PATH}/{domain}.{cfg}.{split_type}.txt"
	for split_type in ["train", "dev"]
	}
	for domain in _OUT_DOMAINS
	},
	}
	for cfg in _CONFIGS_TEXT
	}


	@dataclass
	class PolEmo2Config(datasets.BuilderConfig):
	text_cfg: Optional[str] = None
	domain: Optional[str] = None
	train_domains: Optional[List[str]] = None
	dev_domains: Optional[List[str]] = None
	test_domains: Optional[List[str]] = None


	class PolEmo2(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIG_CLASS = PolEmo2Config
	BUILDER_CONFIGS = [
	*[
	PolEmo2Config(
	name=f"{domain}_{text_type}",
	domain=domain,
	text_cfg=text_type,
	train_domains=[domain],
	dev_domains=[domain],
	test_domains=[domain],
	)
	for domain in _DOMAINS
	for text_type in _CONFIGS_TEXT
	]
	]
	DEFAULT_CONFIG_NAME = "all_text"

	def _info(self) -> datasets.DatasetInfo:
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"target": datasets.features.ClassLabel(
	names=_LABELS, num_classes=len(_LABELS)
	),
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE
	)

	def _get_files_by_domains(self, domains: List[str], split: str) -> List[str]:
	return [_URLS[self.config.text_cfg][domain][split] for domain in domains]

	def _split_generators(
	self, dl_manager: datasets.DownloadManager
	) -> List[datasets.SplitGenerator]:
	files = {
	"train": dl_manager.download_and_extract(
	self._get_files_by_domains(
	domains=self.config.train_domains, split="train"
	)
	),
	"dev": dl_manager.download_and_extract(
	self._get_files_by_domains(domains=self.config.dev_domains, split="dev")
	),
	"test": dl_manager.download_and_extract(
	self._get_files_by_domains(
	domains=self.config.test_domains, split="test"
	)
	),
	}
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"filepath": files["dev"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"filepath": files["test"]},
	),
	]

	def _generate_examples(
	self, filepath: Union[str, List[str]]
	) -> Generator[Tuple[int, Dict[str, str]], None, None]:

	gid = 0
	for path in filepath:
	with open(path, "r", encoding="utf-8") as f:
	for line in f:
	splitted_line = line.split(" ")
	yield gid, {
	"text": " ".join(splitted_line[:-1]),
	"target": (
	splitted_line[-1]
	.strip()
	.replace("minus_m", "minus")
	.replace("plus_m", "plus")
	.split("_")[-1]
	),
	}
	gid += 1