Commit
•
6170b43
1
Parent(s):
326b1c4
support multiple task ids
Browse files- tokenizer.py +31 -13
tokenizer.py
CHANGED
@@ -11,13 +11,14 @@ class JinaTokenizer(RobertaTokenizer):
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def __call__(self, *args, task_type=None, **kwargs):
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batch_encoding = super().__call__(*args, **kwargs)
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return batch_encoding
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def _batch_encode_plus(self, *args, task_type=None, **kwargs):
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@@ -45,18 +46,35 @@ class JinaTokenizer(RobertaTokenizer):
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return batch_encoding
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@staticmethod
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def _get_task_type_ids(batch_encoding: BatchEncoding, task_type
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if isinstance(batch_encoding['input_ids'], torch.Tensor):
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shape = batch_encoding['input_ids'].shape
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return torch.ones(shape, dtype=torch.long)
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else:
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-
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if isinstance(batch_encoding['input_ids'], list):
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return (
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elif isinstance(batch_encoding['input_ids'], np.array):
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return (
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else:
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warnings.warn(
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'input_ids is not a torch tensor, numpy array, or list. Returning torch tensor'
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)
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return torch.ones(shape, dtype=torch.long)
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def __call__(self, *args, task_type=None, **kwargs):
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batch_encoding = super().__call__(*args, **kwargs)
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if task_type is not None:
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batch_encoding = BatchEncoding(
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{
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'task_type_ids': self._get_task_type_ids(batch_encoding, task_type),
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**batch_encoding,
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},
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tensor_type=kwargs.get('return_tensors'),
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)
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return batch_encoding
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def _batch_encode_plus(self, *args, task_type=None, **kwargs):
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return batch_encoding
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@staticmethod
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def _get_task_type_ids(batch_encoding: BatchEncoding, task_type):
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def apply_task_type(m, x):
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x = torch.tensor(x)
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return m * x if len(x.shape) == 0 else m * x[:, None]
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if isinstance(batch_encoding['input_ids'], torch.Tensor):
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shape = batch_encoding['input_ids'].shape
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return apply_task_type(torch.ones(shape, dtype=torch.long), task_type)
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else:
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try:
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shape = torch.tensor(batch_encoding['input_ids']).shape
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except:
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raise ValueError(
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"Unable to create tensor, you should probably "
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"activate truncation and/or padding with "
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"'padding=True' 'truncation=True' to have batched "
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"tensors with the same length."
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)
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if isinstance(batch_encoding['input_ids'], list):
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return (
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apply_task_type(torch.ones(shape, dtype=torch.long), task_type)
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).tolist()
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elif isinstance(batch_encoding['input_ids'], np.array):
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return (
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apply_task_type(torch.ones(shape, dtype=torch.long), task_type)
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).numpy()
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else:
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warnings.warn(
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'input_ids is not a torch tensor, numpy array, or list. Returning torch tensor'
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)
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return apply_task_type(torch.ones(shape, dtype=torch.long), task_type)
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