Upload ner_dataset_script.py

ner_dataset_script.py

@@ -0,0 +1,89 @@
+import csv
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """Citation"""
+
+_DESCRIPTION = """Description"""
+
+_DOWNLOAD_URLS = {
+    "train": "Splitdataset\nerutc_train.csv",
+    "test": "Splitdataset\nerutc_test.csv",
+}
+
+
+class DatasetNameConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(DatasetNameConfig, self).__init__(**kwargs)
+
+
+class DatasetName(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        DatasetNameConfig(
+            name="nerutc",
+            version=datasets.Version("1.1.1"),
+            description=_DESCRIPTION,
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    # TODO YOU SHOULD PUT THE EXTRACTED UNIQUE TAGS IN YOUR DATASET HERE. THIS LIST IS JUST AN EXAMPLE
+                    """
+                    To extract unique tags from a pandas dataframe use this code and paste the output list below.
+
+                    ```python
+                    unique_tags = df["TAGS_COLUMN_NAME"].explode().unique()
+                    print(unique_tags)
+                    ```
+                    """
+                    "ner_tags": datasets.Sequence(  # USE `pos_tags`, `ner_tags`, `chunk_tags`, etc.
+                        datasets.features.ClassLabel(names=['O' 'B-UNI' 'I-UNI'])  # TODO
+                    ),
+                }
+            ),
+            homepage="PUT PATH TO THE ORIGINAL DATASET HOME PAGE HERE (OPTIONAL BUT RECOMMENDED)",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """
+        Return SplitGenerators.
+        """
+
+        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
+        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    # TODO
+    def _generate_examples(self, filepath):
+        """
+        Per each file_path read the csv file and iterate it.
+        For each row yield a tuple of (id, {"tokens": ..., "tags": ..., ...})
+        Each call to this method yields an output like below:
+        ```
+        (124, {"tokens": ["hello", "world"], "pos_tags": ["NOUN", "NOUN"]})
+        ```
+        """
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(csv_file, quotechar='"', skipinitialspace=True)
+
+            # Uncomment below line to skip the first row if your csv file has a header row
+            # next(csv_reader, None)
+
+            for id_, row in enumerate(csv_reader):
+                tokens, ner_tags = row
+                # Optional preprocessing here
+                yield id_, {"tokens": tokens, "ner_tags": ner_tags}