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import os
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import datasets
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import json
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from pathlib import Path
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_ROOT = Path(__file__).resolve().parent
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_DATA_POS = {
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"train": "./data/g_train.json",
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"test": "./data/g_test.json",
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"val": "./data/g_dev.json",
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"extra": "./data/augmented_train.json"
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}
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_DESCIPTION = "contains original ontonotes train/dev/test dataset from https://github.com/shimaokasonse/NFGEC, as well as newly augmented training dataset. "
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class ontonotes(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCIPTION,
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features=datasets.Features({
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"mention_span": datasets.Value("string"),
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"right_context_token": datasets.Value("string"),
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"left_context_token": datasets.Value("string"),
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"y_str": datasets.Sequence(datasets.Value("string")),
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"y_type_str": datasets.Sequence(datasets.Value("string")),
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"y": datasets.Sequence(datasets.Value("int32")),
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"y_type": datasets.Sequence(datasets.Value("int32")),
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"annot_id": datasets.Value("string"),
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})
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)
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def _split_generators(self, dl_manager):
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"path": _DATA_POS["train"], "split": "train"
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"path": _DATA_POS["test"], "split": "test"
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"path": _DATA_POS["val"], "split": "val"
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}
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),
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datasets.SplitGenerator(
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name="extra",
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gen_kwargs={
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"path": _DATA_POS["extra"], "split": "extra"
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}
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)
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]
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def _generate_examples(self, path, split):
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with open(path, "r") as f:
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data = [json.loads(line) for line in f]
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for i, example in enumerate(data):
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yield i, {
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"mention_span": example["mention_span"],
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"right_context_token": example["right_context_token"],
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"left_context_token": example["left_context_token"],
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"y_str": example["y_str"],
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"y_type_str": example["y_type_str"],
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"y": example["y"],
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"y_type": example["y_type"],
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"annot_id": example["annot_id"]
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}
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