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import os |
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import datasets |
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import pandas as pd |
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class TWEETS(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"ID": datasets.Value("string"), |
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"tweet": datasets.Value("string"), |
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"unnecessary": datasets.Value("bool"), |
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"mandatory": datasets.Value("bool"), |
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"pharma": datasets.Value("bool"), |
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"conspiracy": datasets.Value("bool"), |
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"political": datasets.Value("bool"), |
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"country": datasets.Value("bool"), |
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"rushed": datasets.Value("bool"), |
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"ingredients": datasets.Value("bool"), |
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"side-effect": datasets.Value("bool"), |
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"ineffective": datasets.Value("bool"), |
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"religious": datasets.Value("bool"), |
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"none": datasets.Value("bool"), |
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} |
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) |
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return datasets.DatasetInfo( |
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features=features, |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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data_dir = os.path.join(dl_manager.download_and_extract("https://drive.google.com/u/0/uc?id=1e_QaxcG0zSv4UWqncXjDA0ZqBjzg3oVP&export=download")) |
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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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"filepath": os.path.join( data_dir, "data/train_data.csv"), "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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"filepath": os.path.join( data_dir, "data/val_data.csv"), "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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"filepath": os.path.join( data_dir, "data/val_data.csv"), "split": "validation", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""Yields examples as (key, example) tuples.""" |
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with open(filepath, encoding="utf-8") as f: |
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next(f) |
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for id_, row in enumerate(f): |
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data = row.split(",") |
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yield id_, { |
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"ID": data[0], |
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"tweet": data[1], |
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"unnecessary": int(data[2]), |
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"mandatory": int(data[3]), |
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"pharma": int(data[4]), |
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"conspiracy": int(data[5]), |
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"political": int(data[6]), |
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"country": int(data[7]), |
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"rushed": int(data[8]), |
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"ingredients": int(data[9]), |
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"side-effect": int(data[10]), |
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"ineffective": int(data[11]), |
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"religious": int(data[12]), |
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"none": int(data[13]), |
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} |
