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# 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
"""Ottoman 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 Ottoman 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
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