# BertGeneration ## Overview BertGeneration モデルは、次を使用してシーケンス間のタスクに利用できる BERT モデルです。 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) で提案されている `EncoderDecoderModel` タスク、Sascha Rothe、Sishi Nagayan、Aliaksei Severyn 著。 論文の要約は次のとおりです。 *大規模なニューラル モデルの教師なし事前トレーニングは、最近、自然言語処理に革命をもたらしました。による NLP 実践者は、公開されたチェックポイントからウォームスタートして、複数の項目で最先端の技術を推進してきました。 コンピューティング時間を大幅に節約しながらベンチマークを実行します。これまでのところ、主に自然言語に焦点を当ててきました。 タスクを理解する。この論文では、シーケンス生成のための事前トレーニングされたチェックポイントの有効性を実証します。私たちは 公開されている事前トレーニング済み BERT と互換性のある Transformer ベースのシーケンス間モデルを開発しました。 GPT-2 および RoBERTa チェックポイントを使用し、モデルの初期化の有用性について広範な実証研究を実施しました。 エンコーダとデコーダ、これらのチェックポイント。私たちのモデルは、機械翻訳に関する新しい最先端の結果をもたらします。 テキストの要約、文の分割、および文の融合。* ## Usage examples and tips - モデルを `EncoderDecoderModel` と組み合わせて使用​​して、2 つの事前トレーニングされたモデルを活用できます。 後続の微調整のための BERT チェックポイント。 ```python >>> # leverage checkpoints for Bert2Bert model... >>> # use BERT's cls token as BOS token and sep token as EOS token >>> encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102) >>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token >>> decoder = BertGenerationDecoder.from_pretrained( ... "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102 ... ) >>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder) >>> # create tokenizer... >>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased") >>> input_ids = tokenizer( ... "This is a long article to summarize", add_special_tokens=False, return_tensors="pt" ... ).input_ids >>> labels = tokenizer("This is a short summary", return_tensors="pt").input_ids >>> # train... >>> loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss >>> loss.backward() ``` - 事前トレーニングされた `EncoderDecoderModel` もモデル ハブで直接利用できます。 ```python >>> # instantiate sentence fusion model >>> sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse") >>> tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse") >>> input_ids = tokenizer( ... "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt" ... ).input_ids >>> outputs = sentence_fuser.generate(input_ids) >>> print(tokenizer.decode(outputs[0])) ``` チップ： - [BertGenerationEncoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationEncoder) と [BertGenerationDecoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationDecoder) は、 `EncoderDecoder` と組み合わせます。 - 要約、文の分割、文の融合、および翻訳の場合、入力に特別なトークンは必要ありません。 したがって、入力の末尾に EOS トークンを追加しないでください。 このモデルは、[patrickvonplaten](https://huggingface.co/patrickvonplaten) によって提供されました。元のコードは次のとおりです [ここ](https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder) があります。 ## BertGenerationConfig[[transformers.BertGenerationConfig]]
class transformers.BertGenerationConfigtransformers.BertGenerationConfighttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/configuration_bert_generation.py#L20[{"name": "vocab_size", "val": " = 50358"}, {"name": "hidden_size", "val": " = 1024"}, {"name": "num_hidden_layers", "val": " = 24"}, {"name": "num_attention_heads", "val": " = 16"}, {"name": "intermediate_size", "val": " = 4096"}, {"name": "hidden_act", "val": " = 'gelu'"}, {"name": "hidden_dropout_prob", "val": " = 0.1"}, {"name": "attention_probs_dropout_prob", "val": " = 0.1"}, {"name": "max_position_embeddings", "val": " = 512"}, {"name": "initializer_range", "val": " = 0.02"}, {"name": "layer_norm_eps", "val": " = 1e-12"}, {"name": "pad_token_id", "val": " = 0"}, {"name": "bos_token_id", "val": " = 2"}, {"name": "eos_token_id", "val": " = 1"}, {"name": "position_embedding_type", "val": " = 'absolute'"}, {"name": "use_cache", "val": " = True"}, {"name": "**kwargs", "val": ""}]- **vocab_size** (`int`, *optional*, defaults to 50358) -- Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `BertGeneration`. - **hidden_size** (`int`, *optional*, defaults to 1024) -- Dimensionality of the encoder layers and the pooler layer. - **num_hidden_layers** (`int`, *optional*, defaults to 24) -- Number of hidden layers in the Transformer encoder. - **num_attention_heads** (`int`, *optional*, defaults to 16) -- Number of attention heads for each attention layer in the Transformer encoder. - **intermediate_size** (`int`, *optional*, defaults to 4096) -- Dimensionality of the "intermediate" (often called feed-forward) layer in the Transformer encoder. - **hidden_act** (`str` or `function`, *optional*, defaults to `"gelu"`) -- The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. - **hidden_dropout_prob** (`float`, *optional*, defaults to 0.1) -- The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. - **attention_probs_dropout_prob** (`float`, *optional*, defaults to 0.1) -- The dropout ratio for the attention probabilities. - **max_position_embeddings** (`int`, *optional*, defaults to 512) -- The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). - **initializer_range** (`float`, *optional*, defaults to 0.02) -- The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - **layer_norm_eps** (`float`, *optional*, defaults to 1e-12) -- The epsilon used by the layer normalization layers. - **pad_token_id** (`int`, *optional*, defaults to 0) -- Padding token id. - **bos_token_id** (`int`, *optional*, defaults to 2) -- Beginning of stream token id. - **eos_token_id** (`int`, *optional*, defaults to 1) -- End of stream token id. - **position_embedding_type** (`str`, *optional*, defaults to `"absolute"`) -- Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://huggingface.co/papers/2009.13658). - **use_cache** (`bool`, *optional*, defaults to `True`) -- Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`.0 This is the configuration class to store the configuration of a `BertGenerationPreTrainedModel`. It is used to instantiate a BertGeneration model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the BertGeneration [google/bert_for_seq_generation_L-24_bbc_encoder](https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder) architecture. Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.57.0/ja/main_classes/configuration#transformers.PretrainedConfig) for more information. Examples: ```python >>> from transformers import BertGenerationConfig, BertGenerationEncoder >>> # Initializing a BertGeneration config >>> configuration = BertGenerationConfig() >>> # Initializing a model (with random weights) from the config >>> model = BertGenerationEncoder(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```
## BertGenerationTokenizer[[transformers.BertGenerationTokenizer]]
class transformers.BertGenerationTokenizertransformers.BertGenerationTokenizerhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/tokenization_bert_generation.py#L34[{"name": "vocab_file", "val": ""}, {"name": "bos_token", "val": " = ''"}, {"name": "eos_token", "val": " = ''"}, {"name": "unk_token", "val": " = ''"}, {"name": "pad_token", "val": " = ''"}, {"name": "sep_token", "val": " = '<::::>'"}, {"name": "sp_model_kwargs", "val": ": typing.Optional[dict[str, typing.Any]] = None"}, {"name": "**kwargs", "val": ""}]- **vocab_file** (`str`) -- [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that contains the vocabulary necessary to instantiate a tokenizer. - **bos_token** (`str`, *optional*, defaults to `""`) -- The begin of sequence token. - **eos_token** (`str`, *optional*, defaults to `""`) -- The end of sequence token. - **unk_token** (`str`, *optional*, defaults to `""`) -- The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. - **pad_token** (`str`, *optional*, defaults to `""`) -- The token used for padding, for example when batching sequences of different lengths. - **sep_token** (`str`, *optional*, defaults to `"< --:::>"`): The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. - **sp_model_kwargs** (`dict`, *optional*) -- Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, to set: - `enable_sampling`: Enable subword regularization. - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. - `nbest_size = {0,1}`: No sampling is performed. - `nbest_size > 1`: samples from the nbest_size results. - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.0 Construct a BertGeneration tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.57.0/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
save_vocabularytransformers.BertGenerationTokenizer.save_vocabularyhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/tokenization_bert_generation.py#L159[{"name": "save_directory", "val": ": str"}, {"name": "filename_prefix", "val": ": typing.Optional[str] = None"}]
## BertGenerationEncoder[[transformers.BertGenerationEncoder]]
class transformers.BertGenerationEncodertransformers.BertGenerationEncoderhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/modeling_bert_generation.py#L592[{"name": "config", "val": ""}]- **config** ([BertGenerationEncoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationEncoder)) -- 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 [from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0 The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forwardtransformers.BertGenerationEncoder.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/modeling_bert_generation.py#L633[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "head_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) - **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **head_mask** (`torch.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** (`torch.Tensor` of shape `(batch_size, 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. - **encoder_hidden_states** (`torch.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** (`torch.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** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, `DynamicCache` will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **use_cache** (`bool`, *optional*) -- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - **output_attentions** (`bool`, *optional*) -- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. - **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. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([BertGenerationConfig](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationConfig)) and inputs. - **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is output. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + 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 optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (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. - **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads. The [BertGenerationEncoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationEncoder) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
## BertGenerationDecoder[[transformers.BertGenerationDecoder]]
class transformers.BertGenerationDecodertransformers.BertGenerationDecoderhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/modeling_bert_generation.py#L765[{"name": "config", "val": ""}]- **config** ([BertGenerationDecoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationDecoder)) -- 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 [from_pretrained()](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.0 BertGeneration Model with a `language modeling` head on top for CLM fine-tuning. This model inherits from [PreTrainedModel](/docs/transformers/v4.57.0/ja/main_classes/model#transformers.PreTrainedModel). 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 PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forwardtransformers.BertGenerationDecoder.forwardhttps://github.com/huggingface/transformers/blob/v4.57.0/src/transformers/models/bert_generation/modeling_bert_generation.py#L787[{"name": "input_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "position_ids", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "head_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "inputs_embeds", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "encoder_attention_mask", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "labels", "val": ": typing.Optional[torch.Tensor] = None"}, {"name": "past_key_values", "val": ": typing.Optional[transformers.cache_utils.Cache] = None"}, {"name": "use_cache", "val": ": typing.Optional[bool] = None"}, {"name": "output_attentions", "val": ": typing.Optional[bool] = None"}, {"name": "output_hidden_states", "val": ": typing.Optional[bool] = None"}, {"name": "return_dict", "val": ": typing.Optional[bool] = None"}, {"name": "**kwargs", "val": ""}]- **input_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of input sequence tokens in the vocabulary. Padding will be ignored by default. Indices can be obtained using [AutoTokenizer](/docs/transformers/v4.57.0/ja/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v4.57.0/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *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) - **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **head_mask** (`torch.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** (`torch.Tensor` of shape `(batch_size, 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. - **encoder_hidden_states** (`torch.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** (`torch.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**. - **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 in `[0, ..., config.vocab_size]` - **past_key_values** (`~cache_utils.Cache`, *optional*) -- Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Only `Cache` instance is allowed as input, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). If no `past_key_values` are passed, `DynamicCache` will be initialized by default. The model will output the same cache format that is fed as input. If `past_key_values` are used, the user is expected to input only unprocessed `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, unprocessed_length)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. - **use_cache** (`bool`, *optional*) -- If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - **output_attentions** (`bool`, *optional*) -- Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. - **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. - **return_dict** (`bool`, *optional*) -- Whether or not to return a [ModelOutput](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[transformers.modeling_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or `tuple(torch.FloatTensor)`A [transformers.modeling_outputs.CausalLMOutputWithCrossAttentions](/docs/transformers/v4.57.0/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([BertGenerationConfig](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationConfig)) and inputs. - **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Language modeling loss (for next-token prediction). - **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) -- Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). - **hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + 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 optional initial embedding outputs. - **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (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. - **cross_attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) -- Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads. - **past_key_values** (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- It is a `Cache` instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. The [BertGenerationDecoder](/docs/transformers/v4.57.0/ja/model_doc/bert-generation#transformers.BertGenerationDecoder) forward method, overrides the `__call__` special method. Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them. Example: ```python >>> from transformers import AutoTokenizer, BertGenerationDecoder, BertGenerationConfig >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") >>> config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder") >>> config.is_decoder = True >>> model = BertGenerationDecoder.from_pretrained( ... "google/bert_for_seq_generation_L-24_bbc_encoder", config=config ... ) >>> inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.logits ```