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Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, BertForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = BertForPreTraining.from_pretrained("google-bert/bert-base-uncased") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ``` Nrr\rYrrxrrrr/rZ)r"r#r$rr) rKr1rr6r rrbr!rr)rtrwrr\rYrrxrArBrrrrrrrr total_lossloss_fctmasked_lm_lossnext_sentence_lossrr%r%r(r-s<+ zBertForPreTraining.forward NNNNNNNNNNN)rrr_tied_weights_keysr`r:r?rrrHrrrrr!rrr%r%rur(r2sT       r2zP Bert Model with a `language modeling` head on top for CLM fine-tuning. c"seZdZddgZfddZddZddZe dd ee j d ee j d ee j d ee j dee j dee j dee j dee j dee j dee e j dee dee dee dee de ee j effddZddZZS)BertLMHeadModelzcls.predictions.decoder.biasr4cs@t||jstdt|dd|_t||_| dS)NzLIf you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`Fr+ r_r`rr3warningr&rrr6r*rsrur%r(r`s    zBertLMHeadModel.__init__cCr8rr9r r%r%r(r:r;z%BertLMHeadModel.get_output_embeddingscCr<rr=r>r%r%r(r?r@z%BertLMHeadModel.set_output_embeddingsNrwrr\rYrrxrrrArrrrrrzcKs|dur|n|jj}| durd} |j||||||||| | | | |d }|d}||}d}| dur>|j|| |jjfi|}|sT|f|dd}|durR|f|S|St|||j|j|j |j dS)a labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` NF) rr\rYrrxrrrrrrrrr/)r"logitsrrrr) rKr1rr6Z loss_functionrbrrrrr)rtrwrr\rYrrxrrrArrrrrZ loss_kwargsrrrZlm_lossrr%r%r(rsD zBertLMHeadModel.forwardcs.d}|D]}|tfdd|Df7}q|S)Nr%c3s$|] }|d|jVqdS)rN)Z index_selectrr|)r&Z past_statebeam_idxr%r(r)s"z1BertLMHeadModel._reorder_cache..)r)rtrrPZreordered_pastZ layer_pastr%rOr(_reorder_caches zBertLMHeadModel._reorder_cache)NNNNNNNNNNNNNN)rrrrIr`r:r?rrrHrrrrrrrrQrr%r%rur(rJ}sh        @rJcseZdZddgZfddZddZddZe dd ee j d ee j d ee j d ee j dee j dee j dee j dee j dee j dee dee dee de e e j effddZdddZede fddZZS)BertForMaskedLMr3r4cs@t||jrtdt|dd|_t||_| dS)NzkIf you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.FrKrLrsrur%r(r`s   zBertForMaskedLM.__init__cCr8rr9r r%r%r(r:r;z%BertForMaskedLM.get_output_embeddingscCr<rr=r>r%r%r(r?r@z%BertForMaskedLM.set_output_embeddingsNrwrr\rYrrxrrrArrrrzc  Cs| dur| n|jj} |j||||||||| | | d } | d}||}d}| dur:t}||d|jj| d}| sP|f| dd}|durN|f|S|St||| j| j dS)a labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` N) rr\rYrrxrrrrrrrZr/r"rNrr) rKr1rr6r rrbrrr)rtrwrr\rYrrxrrrArrrrrrrFrErr%r%r(rs: zBertForMaskedLM.forwardcKs~|j}|d}|jjdurtdtj|||jddfgdd}tj|df|jjtj|j d}tj||gdd}||dS)Nrz.The PAD token should be defined for generationr"rZrr{)rwr) rErKrdrFrHrZ new_zerosfullrrr|)rtrwrZ model_kwargsr~Zeffective_batch_sizeZ dummy_tokenr%r%r(prepare_inputs_for_generation6s " z-BertForMaskedLM.prepare_inputs_for_generationcCsdS)z Legacy correction: BertForMaskedLM can't call `generate()` from `GenerationMixin`, even though it has a `prepare_inputs_for_generation` method. Fr%)r6r%r%r( can_generateFszBertForMaskedLM.can_generate) NNNNNNNNNNNNr)rrrrIr`r:r?rrrHrrrrrrrU classmethodrVrr%r%rur(rRs`       9rRzT Bert Model with a `next sentence prediction (classification)` head on top. ceZdZfddZe          ddeejdeejdeejdeejdeejd eejd eejd eed eed eede e eje ffddZ Z S)BertForNextSentencePredictioncr5r)r_r`r&rrr6r*rsrur%r(r`Ur7z&BertForNextSentencePrediction.__init__Nrwrr\rYrrxrArrrrzc  Ksd| vrtdt| d}| dur| n|jj} |j|||||||| | d } | d} || }d}|durEt}|| dd| d}| s[|f| dd}|durY|f|S|St ||| j | j dS) a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, BertForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = BertForNextSentencePrediction.from_pretrained("google-bert/bert-base-uncased") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ``` rBzoThe `next_sentence_label` argument is deprecated and will be removed in a future version, use `labels` instead.NrCr"rZr/rS) warningswarn FutureWarningpoprKr1rr6r rrrr)rtrwrr\rYrrxrArrrkwargsrrZseq_relationship_scoresrGrErr%r%r(r^sB*  z%BertForNextSentencePrediction.forward NNNNNNNNNN)rrrr`rrrHrrrrrrrr%r%rur(rYOsH      rYz Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks. crX)BertForSequenceClassificationcsdt||j|_||_t||_|jdur|jn|j}t ||_ t |j |j|_ |dSr)r_r` num_labelsrKr&rclassifier_dropoutrlrrkrmrrcr.r*rtrKrbrur%r(r`s    z&BertForSequenceClassification.__init__Nrwrr\rYrrxrArrrrzc  Csr| dur| n|jj} |j|||||||| | d } | d} || } || } d}|dur|jjdurV|jdkr 1` a classification loss is computed (Cross-Entropy). NrCr"Z regressionZsingle_label_classificationZmulti_label_classificationrZr/rS)rKr1rrmr.Z problem_typerar^rHrrrCr squeezer rr rrr)rtrwrr\rYrrxrArrrrrrNr"rErr%r%r(rsV     "       z%BertForSequenceClassification.forwardr_)rrrr`rrrHrrrrrrrr%r%rur(r`sH     r`crX)BertForMultipleChoicecsTt|t||_|jdur|jn|j}t||_t |j d|_ | dS)Nr") r_r`r&rrbrlrrkrmrrcr.r*rcrur%r(r`s    zBertForMultipleChoice.__init__Nrwrr\rYrrxrArrrrzc  Csn| dur| n|jj} |dur|jdn|jd} |dur%|d|dnd}|dur4|d|dnd}|durC|d|dnd}|durR|d|dnd}|dure|d|d|dnd}|j|||||||| | d } | d} || } || }|d| }d}|durt}|||}| s|f| dd}|dur|f|S|St ||| j | j dS)a[ input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) Nr"rZrrCr/rS) rKr1rErrqrrmr.r rrr)rtrwrr\rYrrxrArrrZ num_choicesrrrNZreshaped_logitsr"rErr%r%r(r!sL,    zBertForMultipleChoice.forwardr_)rrrr`rrrHrrrrrrrr%r%rur(resH      recrX)BertForTokenClassificationcsbt||j|_t|dd|_|jdur|jn|j}t||_ t |j |j|_ | dSNFrK)r_r`rar&rrbrlrrkrmrrcr.r*rcrur%r(r`s   z#BertForTokenClassification.__init__Nrwrr\rYrrxrArrrrzc  Cs| dur| n|jj} |j|||||||| | d } | d} || } || } d}|durs     ,   I@e3V]   ! 5c`l_YjE M