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diff --git a/jomleh.py b/jomleh.py
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+# 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__)
+
+
+@dataclass
+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"]}