norec_agg.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
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian"""
import csv

import datasets


_CITATION = """
@InProceedings{OvrMaeBar20,
  author = {Lilja {\O}vrelid and Petter M{\ae}hlum and Jeremy Barnes and Erik Velldal},
  title = {A Fine-grained Sentiment Dataset for {N}orwegian},
  booktitle = {{Proceedings of the 12th Edition of the Language Resources and Evaluation Conference}},
  year = 2020,
  address = "Marseille, France, 2020"
}
"""

_DESCRIPTION = """\
Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian
This dataset was created by the Nordic Language Processing Laboratory by
aggregating the fine-grained annotations in NoReC_fine and removing sentences
with conflicting or no sentiment.
"""


_HOMEPAGE = "https://github.com/ltgoslo/NorBERT/"

_BASE_URL = "https://raw.githubusercontent.com/ltgoslo/NorBERT/main/benchmarking/data/sentiment/no"
_URLS = {
    "train": f"{_BASE_URL}/train.csv",
    "dev": f"{_BASE_URL}/dev.csv",
    "test": f"{_BASE_URL}/test.csv",
}


class NorecAgg(datasets.GeneratorBasedBuilder):
    """Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian"""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["negative", "positive"]),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(csv_file, delimiter=",")
            for idx, row in enumerate(csv_reader):
                label, text = row
                label = int(label)
                yield int(idx), {"text": text, "label": label}