distinguish was is not trained
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@ -214,7 +214,8 @@ def bertForSequenceClassification(*args, **kwargs):
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"""
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BertForSequenceClassification is a fine-tuning model that includes
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BertModel and a sequence-level (sequence or pair of sequences) classifier
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on top of the BertModel.
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on top of the BertModel. Note that the classification head is only initialized
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and has to be trained.
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The sequence-level classifier is a linear layer that takes as input the
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last hidden state of the first character in the input sequence
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@ -252,7 +253,8 @@ def bertForSequenceClassification(*args, **kwargs):
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def bertForMultipleChoice(*args, **kwargs):
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"""
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BertForMultipleChoice is a fine-tuning model that includes BertModel and a
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linear layer on top of the BertModel.
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linear layer on top of the BertModel. Note that the multiple choice head is
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only initialized and has to be trained.
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Args:
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num_choices: the number (>=2) of classes for the classifier.
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@ -287,7 +289,8 @@ def bertForQuestionAnswering(*args, **kwargs):
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"""
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BertForQuestionAnswering is a fine-tuning model that includes BertModel
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with a token-level classifiers on top of the full sequence of last hidden
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states.
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states. Note that the classification head is only initialized
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and has to be trained.
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Example:
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# Load the tokenizer
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@ -318,7 +321,8 @@ def bertForQuestionAnswering(*args, **kwargs):
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def bertForTokenClassification(*args, **kwargs):
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"""
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BertForTokenClassification is a fine-tuning model that includes BertModel
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and a token-level classifier on top of the BertModel.
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and a token-level classifier on top of the BertModel. Note that the classification
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head is only initialized and has to be trained.
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The token-level classifier is a linear layer that takes as input the last
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hidden state of the sequence.
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