parsinlu_sentiment.py

# coding=utf-8
# 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.
"""ParsiNLU Persian reading comprehension task"""

from __future__ import absolute_import, division, print_function

import csv
import json

import datasets
from datasets import NamedSplit

logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@article{huggingface:dataset,
    title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
    authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
    year={2020}
    journal = {arXiv e-prints},
    eprint = {2012.06154},    
}
"""

# You can copy an official description
_DESCRIPTION = """\
A Persian sentiment analysis task (deciding whether a given sentence contains a particular sentiment).     
"""

_HOMEPAGE = "https://github.com/persiannlp/parsinlu/"

_LICENSE = "CC BY-NC-SA 4.0"

_URL = "https://raw.githubusercontent.com/persiannlp/parsinlu/master/data/sentiment-analysis/"
_URLs = {
    "train": _URL + "ABSA_Dataset_train.jsonl",
    "dev_food": _URL + "food_dev.jsonl",
    "dev_movies": _URL + "movie_dev.jsonl",
    "test_food": _URL + "food_test.jsonl",
    "test_movies": _URL + "movie_test.jsonl",
}

TRAIN_ALL = NamedSplit("train")
TEST_FOOD = NamedSplit("test_food")
TEST_MOVIES = NamedSplit("test_movies")
VALIDATION_FOOD = NamedSplit("validation_food")
VALIDATION_MOVIES = NamedSplit("validation_movies")


class ParsinluReadingComprehension(datasets.GeneratorBasedBuilder):
    """ParsiNLU Persian reading comprehension task."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="parsinlu-repo", version=VERSION, description="ParsiNLU repository: sentiment-analysis"
        ),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "review": datasets.Value("string"),
                "review_id": datasets.Value("string"),
                "example_id": datasets.Value("string"),
                "excel_id": datasets.Value("string"),
                "question": datasets.Value("string"),
                "category": datasets.Value("string"),
                "aspect": datasets.Value("string"),
                "label": datasets.Value("string"),
                "guid": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )



    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLs)
        return [
            datasets.SplitGenerator(
                name=TRAIN_ALL,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=TEST_FOOD,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["test_food"], "split": "test_food"},
            ),
            datasets.SplitGenerator(
                name=TEST_MOVIES,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": data_dir["test_movies"], "split": "test_movies"},
            ),
            datasets.SplitGenerator(
                name=VALIDATION_FOOD,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["dev_food"],
                    "split": "dev_food",
                },
            ),
            datasets.SplitGenerator(
                name=VALIDATION_MOVIES,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["dev_movies"],
                    "split": "dev_movies",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        logger.info("generating examples from = %s", filepath)

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f.readlines()):
                row = json.loads(row)
                yield id_, row