Update tajik-text-segmentation.py

tajik-text-segmentation.py

@@ -1,73 +1,87 @@
-"""Custom Annotations Dataset."""
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
 
-import os
 from .annotations_parser import load_yedda_annotations
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
 
+import datasets
 
 
-_CITATION = """
-@misc{tajik-text-segmentation-2023,
-    title={Tajik sentence-wise text segmentation dataset},
-    author={Sobir Bobiev},
-    year={2023},
-    howpublished={\\url{https://huggingface.co/datasets/sobir-hf/tajik-text-segmentation}},
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@misc{tajik-text-segmentation,
+title = {Tajik text segmentation dataset},
+author={Sobir Bobiev},
+year={2023}
 }
 """
 
-_DESCRIPTION = """
-Tajik sentence-wise text segmentation dataset consisting of annotated text files.
+_DESCRIPTION = """\
+This dataset contains tajik texts with sentences annotated. Can be useful for sentence boundary detection, segmenting text and many NLP tasks.
 """
 
-class CustomAnnotationsConfig(datasets.BuilderConfig):
-    """BuilderConfig for Custom Annotations."""
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
 
-    def __init__(self, **kwargs):
-        """BuilderConfig for Custom Annotations.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(CustomAnnotationsConfig, self).__init__(**kwargs)
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
 
 
-class CustomAnnotations(datasets.GeneratorBasedBuilder):
-    """Custom Annotations: Dataset with annotated text files."""
+class TajikTextSegmentation(datasets.GeneratorBasedBuilder):
+    """A dataset of sentence-wise text segmentation in Tajik language."""
 
-    BUILDER_CONFIGS = [
-        CustomAnnotationsConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
+    VERSION = datasets.Version("1.1.0")
 
     def _info(self):
+        features = datasets.Features(
+            {
+                "file": datasets.Value("string"),
+                "text": datasets.Value("string"),
+                "annotated_text": datasets.Value("string"),
+                "number_of_labels": datasets.Value("int32"),
+                "positions":  [[datasets.Value("int32")]],
+                "labels":  [datasets.Value("string")]
+            }
+        )
         return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "file": datasets.Value("string"),
-                    "text": datasets.Value("string"),
-                    "annotated_text": datasets.Value("string"),
-                    "number_of_labels": datasets.Value("int32"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="your dataset homepage",
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
             citation=_CITATION,
         )
 
     def _split_generators(self, dl_manager):
-        directory_path = os.path.abspath('annotations')
         return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"directory_path": directory_path}),
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "directory_path": './annotations',
+                },
+            ),
         ]
 
     def _generate_examples(self, directory_path):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", directory_path)
+        """This function returns the examples."""
         
         annotations = load_yedda_annotations(directory_path)
         
@@ -81,12 +95,7 @@ class CustomAnnotations(datasets.GeneratorBasedBuilder):
                 "file": file,
                 "text": text,
                 "annotated_text": annotated_text,
+                "positions": file_annotation['positions'],
+                "labels": file_annotation['labels'],
                 "number_of_labels": number_of_labels,
             }
-
-if __name__ == '__main__':
-    # You can test the data generation by running this script.
-    # It will create a dataset in a subdirectory `./datasets/`.
-    from datasets import load_dataset
-    dataset = load_dataset('./tajik-text-segmentation.py')
-    print(dataset)