Datasets:
Sub-tasks:
language-modeling
Languages:
Persian
Multilinguality:
monolingual
Size Categories:
10B<n<100B
Tags:
License:
# coding=utf-8 | |
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace NLP 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 | |
"""Jomleh The Farsi sentence dataset.""" | |
import collections | |
import io | |
import zstandard | |
import json | |
from dataclasses import dataclass | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
class Identification: | |
label: str | |
prob: float | |
_DESCRIPTION = """\ | |
Jomleh is a Farsi (Persian) monolingual dataset composed of one sentence per \ | |
sample. It's focused on quality over quantity and it's curated mostly based \ | |
on the OSCAR project (https://oscar-project.com) among other data sources.\ | |
""" | |
_URL = "https://mlengineer.ai" | |
_LICENSE = """ | |
These data are released under this licensing scheme | |
We do not own any of the text from which these data has been extracted. | |
We license the actual packaging of these data under the Creative Commons CC0 license \ | |
(\"no rights reserved\") http://creativecommons.org/publicdomain/zero/1.0/ | |
To the extent possible under law, Inria has waived all copyright \ | |
and related or neighboring rights to OSCAR | |
This work is published from: France. | |
Should you consider that our data contains material that is owned by you \ | |
and should therefore not be reproduced here, please: | |
* Clearly identify yourself, with detailed contact data such as an address, \ | |
telephone number or email address at which you can be contacted. | |
* Clearly identify the copyrighted work claimed to be infringed. | |
* Clearly identify the material that is claimed to be infringing and \ | |
information reasonably sufficient to allow us to locate the material. | |
We will comply to legitimate requests by removing the affected sources \ | |
from the next release of the corpus. \ | |
""" | |
_CITATION = """\ | |
""" | |
_BASE_DATA_PAT_FORMAT_STR = "files/" | |
_BASE_CHECKSUM_FILE_NAME = "checksum.sha256" | |
class JomlehConfig(datasets.BuilderConfig): | |
"""OSCAR corpus.""" | |
def __init__(self, **kwargs): | |
"""BuilderConfig for Jomleh. | |
Args: | |
**kwargs: Keyword arguments forwarded to super. | |
""" | |
description = ( | |
f"Jomleh dataset from April 2023" | |
) | |
super(JomlehConfig, self).__init__( | |
name="Jomleh", description=description, **kwargs | |
) | |
# Additional attributes | |
self.base_data_path = _BASE_DATA_PAT_FORMAT_STR | |
class Jomleh(datasets.GeneratorBasedBuilder): | |
"""Jomleh The Farsi text dataset based on OSCAR project.""" | |
BUILDER_CONFIGS = [ | |
JomlehConfig( # pylint: disable=g-complex-comprehension | |
version=datasets.Version("2023.4.0"), | |
) | |
] | |
BUILDER_CONFIG_CLASS = JomlehConfig | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"id": datasets.Value("int64"), | |
"text": datasets.Value("string"), | |
"source": datasets.Value("string"), | |
} | |
), | |
supervised_keys=None, | |
homepage=_URL, | |
citation=_CITATION, | |
license=_LICENSE, | |
) | |
def _split_generators(self, dl_manager): | |
checksum_path = self.config.base_data_path + _BASE_CHECKSUM_FILE_NAME | |
checksum_file = dl_manager.download(checksum_path) | |
with open(checksum_file, encoding="utf-8") as f: | |
data_filenames = [line.split()[1] for line in f if line] | |
data_urls = [ | |
self.config.base_data_path + data_filename | |
for data_filename in data_filenames | |
] | |
doc_files = dl_manager.download( | |
[url for url in data_urls if url.endswith(".jsonl.zst")] | |
) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, gen_kwargs={"doc_files": doc_files} | |
), | |
] | |
def _generate_examples(self, doc_files): | |
"""This function returns the examples in the raw (text) form by iterating on all the files.""" | |
for doc_path in doc_files: | |
logger.info("generating examples from = %s", doc_path) | |
with open(doc_path, "rb") as fh: | |
dctx = zstandard.ZstdDecompressor() | |
stream_reader = dctx.stream_reader(fh) | |
buffered_reader = io.BufferedReader(stream_reader) | |
text_stream = io.TextIOWrapper(buffered_reader, encoding="utf-8") | |
for line in text_stream: | |
doc = json.loads(line) | |
yield doc["id"], {"id": doc["id"], "text": doc["text"], "source": doc["source"]} | |