utils_nlp/models/transformers/datasets.py [42:62]:
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        )
        if self.label_col is None:
            return tuple(
                [
                    torch.tensor(input_ids, dtype=torch.long),
                    torch.tensor(attention_mask, dtype=torch.long),
                    torch.tensor(token_type_ids, dtype=torch.long),
                ]
            )
        labels = self.df.iloc[idx, self.label_col]
        return tuple(
            [
                torch.tensor(input_ids, dtype=torch.long),  # input_ids
                torch.tensor(attention_mask, dtype=torch.long),  # attention_mask
                torch.tensor(token_type_ids, dtype=torch.long),  # segment ids
                torch.tensor(labels, dtype=torch.long),  # labels
            ]
        )

    def __len__(self):
        return self.df.shape[0]
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utils_nlp/models/transformers/datasets.py [104:126]:
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        )

        if self.label_col is None:
            return tuple(
                [
                    torch.tensor(input_ids, dtype=torch.long),
                    torch.tensor(attention_mask, dtype=torch.long),
                    torch.tensor(token_type_ids, dtype=torch.long),
                ]
            )

        labels = self.df.iloc[idx, self.label_col]
        return tuple(
            [
                torch.tensor(input_ids, dtype=torch.long),
                torch.tensor(attention_mask, dtype=torch.long),
                torch.tensor(token_type_ids, dtype=torch.long),
                torch.tensor(labels, dtype=torch.long),
            ]
        )

    def __len__(self):
        return self.df.shape[0]
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