Remove trailing whitespace from all Python files.
Fixes flake8 warning W291 (x224).
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@@ -216,7 +216,7 @@ XXX_START_DOCSTRING = r""" The XXX model was proposed in
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`model({'input_ids': input_ids, 'token_type_ids': token_type_ids})`
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Parameters:
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config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
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config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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@@ -230,13 +230,13 @@ XXX_INPUTS_DOCSTRING = r"""
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(a) For sequence pairs:
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``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
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(b) For single sequences:
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``tokens: [CLS] the dog is hairy . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0``
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Xxx is a model with absolute position embeddings so it's usually advised to pad the inputs on
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@@ -198,7 +198,7 @@ XXX_START_DOCSTRING = r""" The XXX model was proposed in
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https://pytorch.org/docs/stable/nn.html#module
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Parameters:
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config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
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config (:class:`~transformers.XxxConfig`): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the configuration.
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Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
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"""
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@@ -212,13 +212,13 @@ XXX_INPUTS_DOCSTRING = r"""
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(a) For sequence pairs:
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``tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1``
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(b) For single sequences:
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``tokens: [CLS] the dog is hairy . [SEP]``
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``token_type_ids: 0 0 0 0 0 0 0``
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Xxx is a model with absolute position embeddings so it's usually advised to pad the inputs on
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@@ -670,9 +670,9 @@ class XxxForQuestionAnswering(XxxPreTrainedModel):
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question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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input_text = "[CLS] " + question + " [SEP] " + text + " [SEP]"
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input_ids = tokenizer.encode(input_text)
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token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
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token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]
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start_scores, end_scores = model(torch.tensor([input_ids]), token_type_ids=torch.tensor([token_type_ids]))
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all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
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all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
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print(' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]))
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# a nice puppet
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