import datasets # coding=utf-8 # Copyright 2024 HuggingFace Datasets Authors. # Lint as: python3 """The Shipping label Dataset. it converts conll to ner input format""" 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, }