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@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text

ottoman_first_level.py

@@ -0,0 +1,161 @@
+# 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

train.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3dea32a569695632827b777b8d182b0e135ac3a0d8be09f6af3609fc3fb4739
+size 11715427