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import json |
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import datasets |
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from pathlib import Path |
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import pandas as pd |
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BASE_DATA_PATH = Path("./data") |
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class CSCOMMConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CSCOMM.""" |
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def __init__(self, key, pretraining=False, data_path="./data", **kwargs): |
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"""BuilderConfig for CSCOMM. |
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Args: |
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key: `string` |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CSCOMMConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs) |
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self.key = key |
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self.pretraining = pretraining |
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class CSCOMM(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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CSCOMMConfig( |
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name="AP", |
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key="ap" |
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), |
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CSCOMMConfig( |
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name="AP+P", |
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key="ap_p" |
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), |
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CSCOMMConfig( |
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name="AP+J", |
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key="ap_j" |
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), |
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CSCOMMConfig( |
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name="AP+PJ", |
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key="ap_pj" |
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), |
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CSCOMMConfig( |
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name="BA", |
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key="ba" |
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), |
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CSCOMMConfig( |
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name="BA+P", |
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key="ba_p" |
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), |
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CSCOMMConfig( |
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name="BA+J", |
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key="ba_j" |
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), |
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CSCOMMConfig( |
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name="BA+PJ", |
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key="ba_pj" |
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), |
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CSCOMMConfig( |
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name="pretrain-unlabeled", |
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key="pt_un", |
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pretraining=True |
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), |
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CSCOMMConfig( |
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name="pretrain-labeled", |
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key="pt_la", |
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pretraining=True |
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), |
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CSCOMMConfig( |
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name="pretrain-both", |
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key="pt_unla", |
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pretraining=True |
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), |
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] |
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def _info(self): |
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features = { |
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"round_id": datasets.Value("string"), |
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"source": datasets.Value("string") |
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} |
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if not self.config.pretraining: |
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features["commentary"] = datasets.Value("string") |
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return datasets.DatasetInfo( |
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features=datasets.Features(features), |
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) |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract({ |
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"train": f"./data/{self.config.key}/train.csv", |
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"valid": f"./data/{self.config.key}/valid.csv", |
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"test": f"./data/{self.config.key}/test.csv" |
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}) |
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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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"data_file": dl_dir["train"], |
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"split": datasets.Split.TRAIN, |
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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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"data_file": dl_dir["valid"], |
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"split": datasets.Split.VALIDATION, |
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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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"data_file": dl_dir["test"], |
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"split": datasets.Split.TEST, |
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}, |
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), |
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] |
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def _generate_examples(self, data_file, split): |
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df = pd.read_csv(data_file) |
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for i, row in enumerate(df.itertuples()): |
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example = {"round_id": row.round_id, "source": row.source} |
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if not self.config.pretraining: |
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example["commentary"] = row.commentary |
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yield i, example |
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