Delete convert_text_for_ner.py

convert_text_for_ner.py

@@ -1,128 +0,0 @@
-
-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,
-                }