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import datasets |
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"""The Shipping label Dataset.""" |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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""" |
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_DESCRIPTION = """ |
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The goal of this task is to provide a dataset for name entity recognition.""" |
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_URL = "https://raw.githubusercontent.com/SanghaviHarshPankajkumar/shipping_label_project/main/NER/data/" |
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_TRAINING_FILE = "train.txt" |
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_VAL_FILE = "val.txt" |
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_TEST_FILE = "test.txt" |
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class shipping_labels_Config(datasets.BuilderConfig): |
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"""Shipping Label Dataset for ner""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Shipping Label data. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(shipping_labels_Config, self).__init__(**kwargs) |
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class shiping_label_ner(datasets.GeneratorBasedBuilder): |
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"""Shipping Label Dataset for ner""" |
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BUILDER_CONFIGS = [ |
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shipping_labels_Config( |
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name="shipping_label_ner", version=datasets.Version("1.0.0"), description="Shipping Label Dataset for ner" |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"O", |
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"B-ORG", |
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"I-ORG", |
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"B-GCNUMBER", |
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"I-GCNUMBER", |
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"B-BGNUMBER", |
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"I-BGNUMBER", |
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"B-NAME", |
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"I-NAME", |
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"B-LOCATION", |
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"I-LOCATION", |
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"B-COUNTRY", |
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"I-COUNTRY", |
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] |
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) |
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), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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"test": f"{_URL}{_TEST_FILE}", |
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"val": f"{_URL}{_VAL_FILE}", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}), |
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] |
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def _generate_examples(self, filepath): |
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logger.info("⏳ Generating examples from = %s", filepath) |
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with open(filepath, encoding="utf-8") as f: |
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current_tokens = [] |
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current_labels = [] |
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sentence_counter = 0 |
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for row in f: |
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row = row.rstrip() |
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if row: |
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token, label = row.split(" ") |
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current_tokens.append(token) |
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current_labels.append(label) |
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else: |
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if not current_tokens: |
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continue |
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assert len(current_tokens) == len(current_labels), "💔 between len of tokens & labels" |
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sentence = ( |
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sentence_counter, |
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{ |
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"id": str(sentence_counter), |
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"tokens": current_tokens, |
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"ner_tags": current_labels, |
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}, |
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) |
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sentence_counter += 1 |
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current_tokens = [] |
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current_labels = [] |
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yield sentence |
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if current_tokens: |
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yield sentence_counter, { |
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"id": str(sentence_counter), |
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"tokens": current_tokens, |
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"ner_tags": current_labels, |
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} |
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