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# Loading script for the Telugu-English Codeswitch Transliterate dataset
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
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