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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_HOMEPAGE = "https://www.google.com" |
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_URL = f'https://huggingface.co/datasets/chintagunta85/pv_dataset/raw/main/' |
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_TRAINING_FILE = "pv_train.tsv" |
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_DEV_FILE = "pv_val.tsv" |
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_TEST_FILE = "pv_test.tsv" |
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class PVDatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Bc2gmCorpus""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for Bc2gmCorpus. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(PVDatasetConfig, self).__init__(**kwargs) |
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class PVDataset(datasets.GeneratorBasedBuilder): |
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"""Bc2gmCorpus dataset.""" |
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BUILDER_CONFIGS = [ |
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PVDatasetConfig(name="PVDatasetCorpus", version=datasets.Version("1.0.0"), description="PVDataset"), |
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] |
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def _info(self): |
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custom_names = ['O','B-GENE','I-GENE','B-CHEMICAL','I-CHEMICAL','B-DISEASE','I-DISEASE','B-SPECIES', 'I-SPECIES'] |
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return datasets.DatasetInfo( |
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description='abhi', |
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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=custom_names |
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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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homepage=_HOMEPAGE, |
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citation='cite me', |
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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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"dev": f"{_URL}{_DEV_FILE}", |
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"test": f"{_URL}{_TEST_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.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
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] |
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def _generate_examples(self, filepath): |
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shift = 0 |
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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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guid = 0 |
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tokens = [] |
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ner_tags = [] |
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for line in f: |
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if line == "" or line == "\n": |
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if tokens: |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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guid += 1 |
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tokens = [] |
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ner_tags = [] |
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else: |
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splits = line.split("\t") |
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tokens.append(splits[0]) |
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if(splits[1].rstrip()=="B"): |
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ner_tags.append("B-SPECIES") |
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elif(splits[1].rstrip()=="I"): |
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ner_tags.append("I-SPECIES") |
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elif(splits[1].rstrip()=="B-Chemical"): |
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ner_tags.append("B-CHEMICAL") |
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elif(splits[1].rstrip()=="I-Chemical"): |
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ner_tags.append("I-CHEMICAL") |
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elif(splits[1].rstrip()=="B-Disease"): |
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ner_tags.append("B-DISEASE") |
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elif(splits[1].rstrip()=="I-Disease"): |
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ner_tags.append("I-DISEASE") |
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else: |
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ner_tags.append(splits[1].rstrip()) |
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yield guid, { |
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"id": str(guid), |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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