Upload file for convert conll file to NER input text

convert_text_for_ner.py

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+
+import datasets
+# coding=utf-8
+# Copyright 2024 HuggingFace Datasets Authors.
+# Lint as: python3
+"""The Shipping label Dataset."""
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """
+The goal of this task is to provide a dataset for name entity recognition."""
+
+_URL = "https://raw.githubusercontent.com/SanghaviHarshPankajkumar/shipping_label_project/main/NER/data/"
+_TRAINING_FILE = "train.txt"
+_VAL_FILE = "val.txt"
+_TEST_FILE = "test.txt"
+
+
+class shipping_labels_Config(datasets.BuilderConfig):
+    """Shipping Label Dataset for ner"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Shipping Label data.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(shipping_labels_Config, self).__init__(**kwargs)
+
+
+class shiping_label_ner(datasets.GeneratorBasedBuilder):
+    """Shipping Label Dataset for ner"""
+
+    BUILDER_CONFIGS = [
+        shipping_labels_Config(
+            name="shipping_label_ner", version=datasets.Version("1.0.0"), description="Shipping Label Dataset for ner"
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-GCNUM",
+                                "I-GCNUM",
+                                "B-BGNUM",
+                                "I-BGNUM",
+                                "B-DATE",
+                                "I-DATE",
+                                "B-ORG",
+                                "I-ORG",
+                                "B-LOCATION",
+                                "I-LOCATION",
+                                "B-NAME",
+                                "I-NAME",
+                                "B-BARCODE",
+                                "I-BARCODE",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+            "val": f"{_URL}{_VAL_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.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
+        ]
+
+    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:
+                    token, label = row.split(" ")
+                    current_tokens.append(token)
+                    current_labels.append(label)
+                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 last sentence in dataset 🧐
+            if current_tokens:
+                yield sentence_counter, {
+                    "id": str(sentence_counter),
+                    "tokens": current_tokens,
+                    "ner_tags": current_labels,
+                }