| |
| import datasets |
|
|
| logger = datasets.logging.get_logger(__name__) |
|
|
| _CITATION = """ """ |
|
|
| _DESCRIPTION = """Telugu English POS Codeswitch dataset. |
| """ |
|
|
| _HOMEPAGE = "" |
|
|
| _URL = "https://huggingface.co/datasets/anishka/CodeSwitching-TE-EN/blob/main/" |
| _TRAINING_FILE = "TWT-train.conllu" |
| _DEV_FILE = "TWT-dev.conllu" |
| _TEST_FILE = "TWT-test.conllu" |
|
|
|
|
| class TeEnCodeSwitchConfig(datasets.BuilderConfig): |
| """ Builder config for the Ancora Ca NER dataset """ |
|
|
| def __init__(self, **kwargs): |
| """BuilderConfig for TeEnCodeSwitch. |
| Args: |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(TeEnCodeSwitchConfig, self).__init__(**kwargs) |
|
|
|
|
| class TeEnCodeSwitch(datasets.GeneratorBasedBuilder): |
| """ Te-En-CodeSwitch dataset.""" |
|
|
| BUILDER_CONFIGS = [ |
| TeEnCodeSwitchConfig( |
| name="Te-En-CodeSwitch", |
| version=datasets.Version("0.0.1"), |
| description="Te-En-CodeSwitch dataset" |
| ), |
| ] |
|
|
| 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=[ |
| "NOUN", |
| "PUNCT", |
| "ADP", |
| "NUM", |
| "SYM", |
| "SCONJ", |
| "ADJ", |
| "PART", |
| "DET", |
| "CCONJ", |
| "PROPN", |
| "PRON", |
| "X", |
| "_", |
| "ADV", |
| "INTJ", |
| "VERB", |
| "AUX", |
| ] |
| ) |
| ), |
| "xpos": datasets.Sequence(datasets.Value("string")), |
| "feats": datasets.Sequence(datasets.Value("string")), |
| "head": datasets.Sequence(datasets.Value("string")), |
| "deprel": datasets.Sequence(datasets.Value("string")), |
| "deps": datasets.Sequence(datasets.Value("string")), |
| "misc": datasets.Sequence(datasets.Value("string")), |
| } |
| ), |
| supervised_keys=None, |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| urls_to_download = { |
| "train": f"{_URL}{_TRAINING_FILE}", |
| "dev": f"{_URL}{_DEV_FILE}", |
| "test": f"{_URL}{_TEST_FILE}", |
| } |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
| print ("Downloading files: ") |
| print (urls_to_download) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
| ] |
|
|
| def _generate_examples(self, filepath): |
| id = 0 |
| for path in filepath: |
| with open(path, "r", encoding="utf-8") as data_file: |
| tokenlist = list(conllu.parse_incr(data_file)) |
| for sent in tokenlist: |
| if "sent_id" in sent.metadata: |
| idx = sent.metadata["sent_id"] |
| else: |
| idx = id |
|
|
| tokens = [token["form"] for token in sent] |
|
|
| if "text" in sent.metadata: |
| txt = sent.metadata["text"] |
| else: |
| txt = " ".join(tokens) |
|
|
| yield id, { |
| "idx": str(idx), |
| "text": txt, |
| "tokens": [token["form"] for token in sent], |
| "lemmas": [token["lemma"] for token in sent], |
| "upos": [token["upos"] for token in sent], |
| "xpos": [token["xpos"] for token in sent], |
| "feats": [str(token["feats"]) for token in sent], |
| "head": [str(token["head"]) for token in sent], |
| "deprel": [str(token["deprel"]) for token in sent], |
| "deps": [str(token["deps"]) for token in sent], |
| "misc": [str(token["misc"]) for token in sent], |
| } |
| id += 1 |
|
|
