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from transformers import PreTrainedTokenizerFast |
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import numpy |
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import torch |
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class ModernDecoderBERTTokenizer(PreTrainedTokenizerFast): |
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def _batch_encode_plus(self, *args, **kwargs): |
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outputs = super()._batch_encode_plus(*args, **kwargs) |
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del outputs["token_type_ids"] |
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for key in ['input_ids', 'attention_mask']: |
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if isinstance(outputs[key], torch.Tensor): |
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outputs[key] = outputs[key][..., :-1] |
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elif isinstance(outputs[key], numpy.ndarray): |
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outputs[key] = outputs[key][..., :-1] |
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elif isinstance(outputs[key], list): |
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outputs[key] = [sequence[:-1] for sequence in outputs[key]] |
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return outputs |
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from transformers import AutoTokenizer |
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AutoTokenizer.register(ModernDecoderBERTTokenizer, fast_tokenizer_class=ModernDecoderBERTTokenizer) |
