Upload ner.py

ner.py

@@ -0,0 +1,107 @@
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = "https://raw.githubusercontent.com/Kriyansparsana/demorepo/main/"
+_TRAINING_FILE = "wnut17train%20(1).conll"
+_DEV_FILE = "indian_ner_dev.conll"
+_TEST_FILE = "indian_ner_test.conll"
+
+class indian_namesConfig(datasets.BuilderConfig):
+    """The WNUT 17 Emerging Entities Dataset."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for WNUT 17.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(indian_namesConfig, self).__init__(**kwargs)
+
+class indian_names(datasets.GeneratorBasedBuilder):
+    """The WNUT 17 Emerging Entities Dataset."""
+
+    BUILDER_CONFIGS = [
+        indian_namesConfig(
+            name="indian_names", version=datasets.Version("1.0.0"), description="The WNUT 17 Emerging Entities Dataset"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-corporation",
+                                "I-corporation",
+                                "B-person",
+                                "I-person",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+            
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            current_tokens = []
+            current_labels = []
+            sentence_counter = 0
+            for row in f:
+                row = row.rstrip()
+                if row:
+                    if "\t" in row:
+                        token, label = row.split("\t")
+                        current_tokens.append(token)
+                        current_labels.append(label)
+                    else:
+                        # Handle cases where the delimiter is missing
+                        # You can choose to skip these rows or handle them differently
+                        logger.warning(f"Delimiter missing in row: {row}")
+                else:
+                    # New sentence
+                    if not current_tokens:
+                        # Consecutive empty lines will cause empty sentences
+                        continue
+                    assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels"
+                    sentence = (
+                        sentence_counter,
+                        {
+                            "id": str(sentence_counter),
+                            "tokens": current_tokens,
+                            "ner_tags": current_labels,
+                        },
+                    )
+                    sentence_counter += 1
+                    current_tokens = []
+                    current_labels = []
+                    yield sentence
+            # Don't forget the last sentence in the dataset 🧐
+            if current_tokens:
+                yield sentence_counter, {
+                    "id": str(sentence_counter),
+                    "tokens": current_tokens,
+                    "ner_tags": current_labels,
+                }