russian_first_level.py

# coding=utf-8
# Copyright 2023 Nonwestlit codebase authors 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
"""Russian Literary Dataset from late 19th century up to early 20th century."""


import json
import warnings
from typing import List

import datasets
from transformers import PreTrainedTokenizerBase

logger = datasets.logging.get_logger(__name__)

_DESCRIPTION = """\
First level categorization of Russian articles.
"""

_URLS = {
    "train": "train.json",
    "val": "val.json",
    "test": "test.json",
}

_CLASS_NAMES = ["literary_text", "cultural_discourse", "other"]


class NonwestlitFirstLevelConfig(datasets.BuilderConfig):
    """BuilderConfig for Dataset."""

    def __init__(
        self, tokenizer: PreTrainedTokenizerBase = None, max_sequence_length: int = None, **kwargs
    ):
        """BuilderConfig for Dataset.

        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super(NonwestlitFirstLevelConfig, self).__init__(**kwargs)
        self.tokenizer = tokenizer
        self.max_sequence_length = max_sequence_length

    @property
    def features(self):
        return {
            "labels": datasets.ClassLabel(names=_CLASS_NAMES),
            "input_ids": datasets.Value("string"),
            "title": datasets.Value("string"),
            "iid": datasets.Value("uint32"),
            "chunk_id": datasets.Value("uint32"),
        }


class NonwestlitFirstLevelDataset(datasets.GeneratorBasedBuilder):
    """Nonwestlit Ottoman Classification Dataset"""

    BUILDER_CONFIGS = [
        NonwestlitFirstLevelConfig(
            name="seq_cls",
            version=datasets.Version("1.0.0", ""),
            description=_DESCRIPTION,
        )
    ]
    BUILDER_CONFIG_CLASS = NonwestlitFirstLevelConfig
    __current_id = 1
    __current_chunk_id = 1

    @property
    def __next_id(self):
        cid = self.__current_id
        self.__current_id += 1
        return cid

    @property
    def __next_chunk_id(self):
        cid = self.__current_chunk_id
        self.__current_chunk_id += 1
        return cid

    def __reset_chunk_id(self):
        self.__current_chunk_id = 1

    def _info(self):
        if self.config.tokenizer is None:
            raise RuntimeError(
                "For HF Datasets and for chunking to be carried out, 'tokenizer' must be given."
            )
        if "llama" in self.config.tokenizer.name_or_path:
            warnings.warn(
                "It is suggested to pass 'max_sequence_length' argument for Llama-2 model family. There "
                "might be errors for the data processing parts as `model_max_len` attributes are set to"
                "MAX_INT64 (?)."
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(self.config.features),
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir["train"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir["val"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir["test"]}
            ),
        ]

    def prepare_articles(self, article: str) -> List[str]:
        tokenizer = self.config.tokenizer
        model_inputs = tokenizer(
            article,
            truncation=True,
            padding=True,
            max_length=self.config.max_sequence_length,
            return_overflowing_tokens=True,
        )
        return tokenizer.batch_decode(model_inputs["input_ids"], skip_special_tokens=True)

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            dataset = json.load(f)

        chunk_id = 0
        for instance in dataset:
            iid = instance.get("id", self.__next_id)
            label = instance.get("label")
            article = self.prepare_articles(instance["article"])
            self.__reset_chunk_id()
            for chunk in article:
                chunk_inputs = {
                    "iid": iid,
                    "chunk_id": self.__next_chunk_id,
                    "title": instance["title"],
                    "input_ids": chunk,
                    "labels": int(label) - 1,
                }
                yield chunk_id, chunk_inputs
                chunk_id += 1