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Output type of [`TFBertForPreTraining`]. Args: prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). seq_relationship_logits (`tf.Tensor` of shape `(batch_size, 2)`): Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation before SoftMax). hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nrlossr.prediction_logitsseq_relationship_logitsz,Optional[Union[Tuple[tf.Tensor], tf.Tensor]]rr) r>r?r@rAr__annotations__rrrrr;r;r;r<rs     rav This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `input_ids` only and nothing else: `model(input_ids)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"input_ids": input_ids, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! Args: config ([`BertConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. a Args: input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and [`PreTrainedTokenizer.encode`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) token_type_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *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 (`np.ndarray` or `tf.Tensor` of shape `({0})`, *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) head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`np.ndarray` or `tf.Tensor` of shape `({0}, 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. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False``): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). z^The bare Bert Model transformer outputting raw hidden-states without any specific head on top.cs|eZdZddddfdd Zeeedee e e ddd d d d d d d d d dddddddddZ dddZ ZS) TFBertModelTr*rgr cs(tj|f||t||dd|_dS)Nrr)rJrKrr)r:rDr rwrVrWr;r<rKszTFBertModel.__init__batch_size, sequence_length checkpoint output_typerNFrrrrrrcCs*|j||||||||| | | | | |d}|S)a encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*, defaults to `True`): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). Set to `False` during training, `True` during generation rhrrjrirrkrrrrrrrrl)r)r:rhrrjrirrkrrrrrrrrlrr;r;r<r}s",zTFBertModel.callc CsJ|jr dSd|_t|dddk rFt|jj|jdW5QRXdS)NTr)rbrcr6r_rrHrdrer;r;r<rds zTFBertModel.build)T)NNNNNNNNNNNNNF)N)r>r?r@rKrr&BERT_INPUTS_DOCSTRINGformatr$_CHECKPOINT_FOR_DOCr _CONFIG_FOR_DOCr}rdr~r;r;rWr<rs0 27rz Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. cseZdZdddgZddfdd Zdd d d Zd d d dZeee de e e dddddddddddddddd ddZdddZZS)TFBertForPreTrainingricls.predictions.decoder.weightzcls.predictions.decoder.biasr*rCcsHtj|f||t|dd|_t|dd|_t||jjdd|_dS)Nrr nsp___cls mlm___clsrrH) rJrKrrrnsprr]mlmr:rDrwrVrWr;r<rKszTFBertForPreTraining.__init__rrcCs|jjSrr/rrr;r;r< get_lm_headsz TFBertForPreTraining.get_lm_headrcCs,tdt|jd|jjd|jjjSNzMThe method get_prefix_bias_name is deprecated. Please use `get_bias` instead./warningswarn FutureWarningrHr/rrr;r;r<get_prefix_bias_names z)TFBertForPreTraining.get_prefix_bias_namer r#rNFrrrz3Union[TFBertForPreTrainingOutput, Tuple[tf.Tensor]]) rhrrjrirrkrrrr/r4rlr1c  Cs|j||||||||| | d } | dd\}}|j|| d}|j|d}d}| dk r~| dk r~d| i}| |d<|j|||fd}| s||f| dd}|dk r|f|S|St|||| j| jd S) a labels (`tf.Tensor` 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]` next_sentence_label (`tf.Tensor` 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. kwargs (`Dict[str, any]`, *optional*, defaults to `{}`): Used to hide legacy arguments that have been deprecated. Return: Examples: ```python >>> import tensorflow as tf >>> from transformers import AutoTokenizer, TFBertForPreTraining >>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased") >>> model = TFBertForPreTraining.from_pretrained("google-bert/bert-base-uncased") >>> input_ids = tokenizer("Hello, my dog is cute", add_special_tokens=True, return_tensors="tf") >>> # Batch size 1 >>> outputs = model(input_ids) >>> prediction_logits, seq_relationship_logits = outputs[:2] ``` rhrrjrirrkrrrrlNrrrlrr/r4r/r0)rrrrr)rr/r.r=rrr)r:rhrrjrirrkrrrr/r4rlrrrrrZ total_lossZd_labelsrr;r;r<r}s<0  zTFBertForPreTraining.callc Cs|jr dSd|_t|dddk rFt|jj|jdW5QRXt|dddk r|t|jj|jdW5QRXt|dddk rt|jj|jdW5QRXdS)NTrr.r/) rbrcr6r_rrHrdr.r/rer;r;r<rdCszTFBertForPreTraining.build) NNNNNNNNNNNF)N)r>r?r@"_keys_to_ignore_on_load_unexpectedrKr2r9rr&r%r&r(rr(r}rdr~r;r;rWr<r)s0   .Or)z2Bert Model with a `language modeling` head on top.cseZdZddddgZddfdd Zd d d d Zd d ddZeee de e e eddddddddddddddddd ddZd ddZZS)!TFBertForMaskedLMr cls.seq_relationshipr*r+r*rCcsLtj|f|||jr$tdt|ddd|_t||jjdd|_ dS)NzmIf you want to use `TFBertForMaskedLM` make sure `config.is_decoder=False` for bi-directional self-attention.Frr rHr,r- rJrKrloggerwarningrrrr]r/r0rWr;r<rK\szTFBertForMaskedLM.__init__rrcCs|jjSrr1rr;r;r<r2hszTFBertForMaskedLM.get_lm_headrcCs,tdt|jd|jjd|jjjSr3r5rr;r;r<r9ks z&TFBertForMaskedLM.get_prefix_bias_namer z'paris'g)\(?r"r#rexpected_output expected_lossNFrrrz)Union[TFMaskedLMOutput, Tuple[tf.Tensor]] rhrrjrirrkrrrr/rlr1c  Cs|j||||||||| | d } | d} |j| | d}| dkr@dn |j| |d}| sz|f| dd}|dk rv|f|S|St||| j| jdS)a labels (`tf.Tensor` or `np.ndarray` 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]` r;rr<Nr>rrr0rr)rr/r=r rr)r:rhrrjrirrkrrrr/rlrrrrrr;r;r<r}os0 zTFBertForMaskedLM.callc Cs|jr dSd|_t|dddk rFt|jj|jdW5QRXt|dddk r|t|jj|jdW5QRXdSNTrr/rbrcr6r_rrHrdr/rer;r;r<rdszTFBertForMaskedLM.build) NNNNNNNNNNF)N)r>r?r@r?rKr2r9rr&r%r&r$r'r r(r}rdr~r;r;rWr<r@Rs<   ,/r@cseZdZddddgZddfdd Zd d d d Zd d ddZdddZee e e e dd dddddddddddddddddddZ d!ddZZS)"TFBertLMHeadModelr rAr*r+r*rCcsLtj|f|||js$tdt|ddd|_t||jjdd|_ dS)NzNIf you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`FrrBr,r-rCr0rWr;r<rKs  zTFBertLMHeadModel.__init__rrcCs|jjSrr1rr;r;r<r2szTFBertLMHeadModel.get_lm_headrcCs,tdt|jd|jjd|jjjSr3r5rr;r;r<r9s z&TFBertLMHeadModel.get_prefix_bias_nameNcKs@|j}|dkrt|}|dk r4|ddddf}|||dS)Nro)rhrr)rZr6Zones)r:rhrrZ model_kwargsrfr;r;r<prepare_inputs_for_generations  z/TFBertLMHeadModel.prepare_inputs_for_generationr!Frrrrz