MBart

The MBart model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.

According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.

Note

This class is nearly identical to the PyTorch implementation of MBart in Huggingface Transformers. For more information, visit the corresponding section in their documentation.

MBartConfig

class transformers.MBartConfig(vocab_size=50265, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function='gelu', d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, gradient_checkpointing=False, pad_token_id=1, bos_token_id=0, eos_token_id=2, forced_eos_token_id=2, **kwargs)

This is the configuration class to store the configuration of a MBartModel. It is used to instantiate an MBART 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 MBART facebook/mbart-large-cc25 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Parameters
  • vocab_size (int, optional, defaults to 50265) – Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling MBartModel or TFMBartModel.

  • d_model (int, optional, defaults to 1024) – Dimensionality of the layers and the pooler layer.

  • encoder_layers (int, optional, defaults to 12) – Number of encoder layers.

  • decoder_layers (int, optional, defaults to 12) – Number of decoder layers.

  • encoder_attention_heads (int, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer encoder.

  • decoder_attention_heads (int, optional, defaults to 16) – Number of attention heads for each attention layer in the Transformer decoder.

  • decoder_ffn_dim (int, optional, defaults to 4096) – Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.

  • encoder_ffn_dim (int, optional, defaults to 4096) – Dimensionality of the “intermediate” (often named feed-forward) layer in decoder.

  • activation_function (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.

  • dropout (float, optional, defaults to 0.1) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

  • activation_dropout (float, optional, defaults to 0.0) – The dropout ratio for activations inside the fully connected layer.

  • classifier_dropout (float, optional, defaults to 0.0) – The dropout ratio for classifier.

  • max_position_embeddings (int, optional, defaults to 1024) – 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).

  • init_std (float, optional, defaults to 0.02) – The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

  • encoder_layerdrop – (float, optional, defaults to 0.0): The LayerDrop probability for the encoder. See the LayerDrop paper for more details.

  • decoder_layerdrop – (float, optional, defaults to 0.0): The LayerDrop probability for the decoder. See the LayerDrop paper for more details.

  • gradient_checkpointing (bool, optional, defaults to False) – If True, use gradient checkpointing to save memory at the expense of slower backward pass.

  • scale_embedding (bool, optional, defaults to False) – Scale embeddings by diving by sqrt(d_model).

  • use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models)

  • forced_eos_token_id (int, optional, defaults to 2) – The id of the token to force as the last generated token when max_length is reached. Usually set to eos_token_id.

Example:

>>> from transformers import MBartModel, MBartConfig

>>> # Initializing a MBART facebook/mbart-large-cc25 style configuration
>>> configuration = MBartConfig()

>>> # Initializing a model from the facebook/mbart-large-cc25 style configuration
>>> model = MBartModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

MBartTokenizer

class transformers.MBartTokenizer(*args, **kwargs)

MBartTokenizerFast

class transformers.MBartTokenizerFast(*args, tokenizer_file=None, src_lang=None, tgt_lang=None, **kwargs)

Construct a “fast” MBART tokenizer (backed by HuggingFace’s tokenizers library). Based on BPE.

MBartTokenizerFast is a subclass of XLMRobertaTokenizerFast. Refer to superclass XLMRobertaTokenizerFast for usage examples and documentation concerning the initialization parameters and other methods.

The tokenization method is <tokens> <eos> <language code> for source language documents, and <language code> <tokens> <eos>` for target language documents.

Examples:

>>> from transformers import MBartTokenizerFast
>>> tokenizer = MBartTokenizerFast.from_pretrained('facebook/mbart-large-en-ro', src_lang="en_XX", tgt_lang="ro_RO")
>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
>>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> inputs = tokenizer(example_english_phrase, return_tensors="pt)
>>> with tokenizer.as_target_tokenizer():
...     labels = tokenizer(expected_translation_romanian, return_tensors="pt")
>>> inputs["labels"] = labels["input_ids"]
as_target_tokenizer()

Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels.

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int]

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. The special tokens depend on calling set_lang.

An MBART sequence has the following format, where X represents the sequence:

  • input_ids (for encoder) X [eos, src_lang_code]

  • decoder_input_ids: (for decoder) X [eos, tgt_lang_code]

BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator.

Parameters
  • token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

list of input IDs with the appropriate special tokens.

Return type

List[int]

get_special_tokens_mask(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int]

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

Parameters
  • token_ids_0 (List[int]) – List of ids.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

  • already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[int]

prepare_seq2seq_batch(src_texts: List[str], src_lang: str = 'en_XX', tgt_texts: Optional[List[str]] = None, tgt_lang: str = 'ro_RO', **kwargs) → transformers.tokenization_utils_base.BatchEncoding

Prepare model inputs for translation. For best performance, translate one sentence at a time.

Parameters
  • src_texts (List[str]) – List of documents to summarize or source language texts.

  • tgt_texts (list, optional) – List of summaries or target language texts.

  • max_length (int, optional) – Controls the maximum length for encoder inputs (documents to summarize or source language texts) If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • max_target_length (int, optional) – Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set to None, this will use the max_length value.

  • padding (bool, str or PaddingStrategy, optional, defaults to False) –

    Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).

    • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.

    • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).

  • return_tensors (str or TensorType, optional) –

    If set, will return tensors instead of list of python integers. Acceptable values are:

    • 'tf': Return TensorFlow tf.constant objects.

    • 'pt': Return PyTorch torch.Tensor objects.

    • 'np': Return Numpy np.ndarray objects.

  • truncation (bool, str or TruncationStrategy, optional, defaults to True) –

    Activates and controls truncation. Accepts the following values:

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • **kwargs – Additional keyword arguments passed along to self.__call__.

Returns

A BatchEncoding with the following fields:

  • input_ids – List of token ids to be fed to the encoder.

  • attention_mask – List of indices specifying which tokens should be attended to by the model.

  • labels – List of token ids for tgt_texts.

The full set of keys [input_ids, attention_mask, labels], will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.

Return type

BatchEncoding

set_src_lang_special_tokens(src_lang) → None

Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].

set_tgt_lang_special_tokens(lang: str) → None

Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].

MBart50Tokenizer

class transformers.MBart50Tokenizer(*args, **kwargs)

MBart50TokenizerFast

class transformers.MBart50TokenizerFast(vocab_file, src_lang=None, tgt_lang=None, tokenizer_file=None, eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', **kwargs)

Construct a “fast” MBART tokenizer for mBART-50 (backed by HuggingFace’s tokenizers library). Based on BPE.

This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

Parameters
  • vocab_file (str) – Path to the vocabulary file.

  • src_lang (str, optional) – A string representing the source language.

  • tgt_lang (str, optional) – A string representing the target language.

  • eos_token (str, optional, defaults to "</s>") – The end of sequence token.

  • sep_token (str, optional, defaults to "</s>") – 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.

  • cls_token (str, optional, defaults to "<s>") – The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens.

  • unk_token (str, optional, defaults to "<unk>") – 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 "<pad>") – The token used for padding, for example when batching sequences of different lengths.

  • mask_token (str, optional, defaults to "<mask>") – The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict.

Examples:

>>> from transformers import MBart50TokenizerFast
>>> tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50", src_lang="en_XX", tgt_lang="ro_RO")
>>> src_text = " UN Chief Says There Is No Military Solution in Syria"
>>> tgt_text =  "Şeful ONU declară că nu există o soluţie militară în Siria"
>>> model_inputs = tokenizer(src_text, return_tensors="pt")
>>> with tokenizer.as_target_tokenizer():
...    labels = tokenizer(tgt_text, return_tensors="pt").input_ids
>>> # model(**model_inputs, labels=labels) should work
as_target_tokenizer()

Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels.

build_inputs_with_special_tokens(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int]

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. The special tokens depend on calling set_lang.

An MBART-50 sequence has the following format, where X represents the sequence:

  • input_ids (for encoder) [src_lang_code] X [eos]

  • labels: (for decoder) [tgt_lang_code] X [eos]

BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a separator.

Parameters
  • token_ids_0 (List[int]) – List of IDs to which the special tokens will be added.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

Returns

list of input IDs with the appropriate special tokens.

Return type

List[int]

get_special_tokens_mask(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int]

Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

Parameters
  • token_ids_0 (List[int]) – List of ids.

  • token_ids_1 (List[int], optional) – Optional second list of IDs for sequence pairs.

  • already_has_special_tokens (bool, optional, defaults to False) – Whether or not the token list is already formatted with special tokens for the model.

Returns

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Return type

List[int]

prepare_seq2seq_batch(src_texts: List[str], src_lang: str = 'en_XX', tgt_texts: Optional[List[str]] = None, tgt_lang: str = 'ro_RO', **kwargs) → transformers.tokenization_utils_base.BatchEncoding

Prepare model inputs for translation. For best performance, translate one sentence at a time.

Parameters
  • src_texts (List[str]) – List of documents to summarize or source language texts.

  • tgt_texts (list, optional) – List of summaries or target language texts.

  • max_length (int, optional) – Controls the maximum length for encoder inputs (documents to summarize or source language texts) If left unset or set to None, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated.

  • max_target_length (int, optional) – Controls the maximum length of decoder inputs (target language texts or summaries) If left unset or set to None, this will use the max_length value.

  • padding (bool, str or PaddingStrategy, optional, defaults to False) –

    Activates and controls padding. Accepts the following values:

    • True or 'longest': Pad to the longest sequence in the batch (or no padding if only a single sequence if provided).

    • 'max_length': Pad to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided.

    • False or 'do_not_pad' (default): No padding (i.e., can output a batch with sequences of different lengths).

  • return_tensors (str or TensorType, optional) –

    If set, will return tensors instead of list of python integers. Acceptable values are:

    • 'tf': Return TensorFlow tf.constant objects.

    • 'pt': Return PyTorch torch.Tensor objects.

    • 'np': Return Numpy np.ndarray objects.

  • truncation (bool, str or TruncationStrategy, optional, defaults to True) –

    Activates and controls truncation. Accepts the following values:

    • True or 'longest_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_first': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • 'only_second': Truncate to a maximum length specified with the argument max_length or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.

    • False or 'do_not_truncate' (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size).

  • **kwargs – Additional keyword arguments passed along to self.__call__.

Returns

A BatchEncoding with the following fields:

  • input_ids – List of token ids to be fed to the encoder.

  • attention_mask – List of indices specifying which tokens should be attended to by the model.

  • labels – List of token ids for tgt_texts.

The full set of keys [input_ids, attention_mask, labels], will only be returned if tgt_texts is passed. Otherwise, input_ids, attention_mask will be the only keys.

Return type

BatchEncoding

save_vocabulary(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str]

Save only the vocabulary of the tokenizer (vocabulary + added tokens).

This method won’t save the configuration and special token mappings of the tokenizer. Use _save_pretrained() to save the whole state of the tokenizer.

Parameters
  • save_directory (str) – The directory in which to save the vocabulary.

  • filename_prefix (str, optional) – An optional prefix to add to the named of the saved files.

Returns

Paths to the files saved.

Return type

Tuple(str)

set_src_lang_special_tokens(src_lang: str) → None

Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos].

set_tgt_lang_special_tokens(tgt_lang: str) → None

Reset the special tokens to the target language setting. prefix=[src_lang_code] and suffix=[eos].

MBartModel

class transformers.MBartModel(config: transformers.models.mbart.configuration_mbart.MBartConfig)

The bare MBART Model outputting raw hidden-states without any specific head on top. This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (MBartConfig) – 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() method to load the model weights.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The MBartModel forward method, overrides the __call__() special method.

Note

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.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

    Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are 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?

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) – Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids to the right, following the paper.

  • decoder_input_ids

    Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are input IDs?

    MBart uses a specific language id token as the starting token for decoder_input_ids generation that varies according to source and target language, e.g. 25004 for en_XX, and 25003 for de_DE. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

    For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

    Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

    If you want to change padding behavior, you should read modeling_mbart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  • head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the heas is masked.

  • decoder_head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers with each tuple having 2 tuples each of which has 2 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

    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).

  • inputs_embeds (torch.FloatTensor 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.

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) –

    Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

    If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.

  • 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 instead of a plain tuple.

Returns

A Seq2SeqModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (MBartConfig) 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 decoder 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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • decoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

  • decoder_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 of the decoder, 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).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

  • encoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

  • encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

Seq2SeqModelOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import MBartTokenizer, MBartModel
>>> import torch

>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
>>> model = MBartModel.from_pretrained('facebook/mbart-large-cc25')

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
get_input_embeddings()

Returns the model’s input embeddings.

Returns

A torch module mapping vocabulary to hidden states.

Return type

nn.Module

set_input_embeddings(value)

Set model’s input embeddings.

Parameters

value (nn.Module) – A module mapping vocabulary to hidden states.

MBartModelWithHeads

class transformers.MBartModelWithHeads(config: transformers.models.mbart.configuration_mbart.MBartConfig, **kwargs)

MBART Model with the option to add multiple flexible prediction heads on top. This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (MBartConfig) – 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() method to load the model weights.

property active_head

The active prediction head configuration of this model. Can be either the name of a single available head (string) or a list of multiple available heads. In case of a list of heads, the same base model is forwarded through all specified heads.

Returns

A string or a list of strings describing the active head configuration.

Return type

Union[str, List[str]]

add_adapter(adapter_name: str, config=None)

Adds a new adapter module of the specified type to the model.

Parameters
  • adapter_name (str) – The name of the adapter module to be added.

  • config (str or dict, optional) –

    The adapter configuration, can be either:

    • the string identifier of a pre-defined configuration dictionary

    • a configuration dictionary specifying the full config

    • if not given, the default configuration for this adapter type will be used

add_classification_head(head_name, num_labels=2, layers=2, activation_function='tanh', overwrite_ok=False, multilabel=False, id2label=None)

Adds a sequence classification head on top of the model.

Parameters
  • head_name (str) – The name of the head.

  • num_labels (int, optional) – Number of classification labels. Defaults to 2.

  • layers (int, optional) – Number of layers. Defaults to 2.

  • activation_function (str, optional) – Activation function. Defaults to ‘tanh’.

  • overwrite_ok (bool, optional) – Force overwrite if a head with the same name exists. Defaults to False.

  • multilabel (bool, optional) – Enable multilabel classification setup. Defaults to False.

add_fusion(adapter_names: Union[transformers.adapters.composition.Fuse, list], adapter_fusion_config=None, override_kwargs=None)

Adds AdapterFusion to the model with alll the necessary configurations and weight initializations

Parameters
  • adapter_names – a list of adapter names which should be fused

  • adapter_fusion_config (str or dict) –

    adapter fusion configuration, can be either:

    • a string identifying a pre-defined adapter fusion configuration

    • a dictionary representing the adapter fusion configuration

    • the path to a file containing the adapter fusion configuration

  • override_kwargs – dictionary items for values which should be overwritten in the default AdapterFusion configuration

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, head=None, return_dict=None, **kwargs)

The MBartModelWithHeads forward method, overrides the __call__() special method.

Note

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.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

    Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are 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?

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) – Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids to the right, following the paper.

  • decoder_input_ids

    Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are input IDs?

    MBart uses a specific language id token as the starting token for decoder_input_ids generation that varies according to source and target language, e.g. 25004 for en_XX, and 25003 for de_DE. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

    For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

    Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

    If you want to change padding behavior, you should read modeling_mbart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  • head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the heas is masked.

  • decoder_head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers with each tuple having 2 tuples each of which has 2 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

    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).

  • inputs_embeds (torch.FloatTensor 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.

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) –

    Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

    If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.

  • 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 instead of a plain tuple.

  • labels (torch.LongTensor of shape (batch_size,), optional) – Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Returns

A ModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (MBartConfig) and inputs.

Return type

ModelOutput or tuple(torch.FloatTensor)

Base class for all model outputs as dataclass. Has a __getitem__ that allows indexing by integer or slice (like a tuple) or strings (like a dictionary) that will ignore the None attributes. Otherwise behaves like a regular python dictionary.

Warning

You can’t unpack a ModelOutput directly. Use the to_tuple() method to convert it to a tuple before.

Example:

>>> from transformers import MBartTokenizer, MBartModelWithHeads
>>> import torch

>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
>>> model = MBartModelWithHeads.from_pretrained('facebook/mbart-large-cc25')

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
freeze_model(freeze=True)

Freezes all weights of the model.

get_labels(head_name=None)

Returns the labels the given head is assigning/predictin

Parameters
  • head_name – (str, optional) the name of the head which labels should be returned. Default is None.

  • the name is None the labels of the active head are returned (If) –

Returns: labels

get_labels_dict(head_name=None)

Returns the id2label dict for the given hea

Parameters
  • head_name – (str, optional) the name of the head which labels should be returned. Default is None.

  • the name is None the labels of the active head are returned (If) –

Returns: id2label

load_adapter(adapter_name_or_path: str, config: Union[dict, str] = None, version: str = None, model_name: str = None, load_as: str = None, with_head: bool = True, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None, **kwargs) → str

Loads a pre-trained pytorch adapter module from the local file system or a remote location.

Parameters
  • adapter_name_or_path (str) –

    can be either:

    • the identifier of a pre-trained task adapter to be loaded from Adapter Hub

    • a path to a directory containing adapter weights saved using model.saved_adapter()

    • a URL pointing to a zip folder containing a saved adapter module

  • config (dict or str, optional) – The requested configuration of the adapter. If not specified, will be either: - the default adapter config for the requested adapter if specified - the global default adapter config

  • version (str, optional) – The version of the adapter to be loaded.

  • model_name (str, optional) – The string identifier of the pre-trained model.

  • load_as (str, optional) – Load the adapter using this name. By default, the name with which the adapter was saved will be used.

Returns

The name with which the adapter was added to the model.

Return type

str

load_adapter_fusion(adapter_fusion_name_or_path: str, load_as: str = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None, **kwargs) → str

Loads a pre-trained pytorch adapter module from the local file system or a remote location.

Parameters
  • adapter_fusion_name_or_path (str) –

    can be either:

    • the identifier of a pre-trained task adapter fusion module to be loaded from Adapter Hub

    • a path to a directory containing adapter weights saved using model.saved_adapter()

    • a URL pointing to a zip folder containing a saved adapter module

  • config (dict or str, optional) – The requested configuration of the adapter fusion. If not specified, will be either: - the default adapter config for the requested adapter fusion if specified - the global default adapter fusion config

  • model_name (str, optional) – The string identifier of the pre-trained model.

  • load_as (str, optional) – Load the adapter using this name. By default, the name with which the adapter was saved will be used.

Returns

The name with which the adapter was added to the model.

Return type

str

pre_transformer_forward()

This method should be called by every adapter-implementing model at the very beginning of the forward() method.

save_adapter(save_directory: str, adapter_name: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using load_adapter().

Parameters
  • save_directory (str) – Path to a directory where the adapter should be saved.

  • adapter_name (str) – Name of the adapter to be saved.

Raises

ValueError – If the given adapter name is invalid.

save_adapter_fusion(save_directory: str, adapter_names: list, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves an adapter and its configuration file to a directory so that it can be shared or reloaded using load_adapter().

Parameters
  • save_directory (str) – Path to a directory where the adapter should be saved.

  • adapter_name (str) – Name of the adapter to be saved.

Raises

ValueError – If the given adapter name is invalid.

save_all_adapter_fusions(save_directory: str, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves all adapters of this model together with their configuration to subfolders of the given location.

Parameters

save_directory (str) – Path to a directory where the adapters should be saved.

save_all_adapters(save_directory: str, with_head: bool = True, meta_dict: dict = None, custom_weights_loaders: Optional[List[transformers.adapters.loading.WeightsLoader]] = None)

Saves all adapters of this model together with their configuration to subfolders of the given location.

Parameters

save_directory (str) – Path to a directory where the adapters should be saved.

set_active_adapters(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock], skip_layers: Optional[List[int]] = None)

Sets the adapter modules to be used by default in every forward pass. This setting can be overriden by passing the adapter_names parameter in the foward() pass. If no adapter with the given name is found, no module of the respective type will be activated. In case the calling model class supports named prediction heads, this method will attempt to activate a prediction head with the name of the last adapter in the list of passed adapter names.

Parameters

adapter_setup (list) – The list of adapters to be activated by default. Can be a fusion or stacking configuration.

set_adapter_fusion_config(adapter_fusion_config, override_kwargs=None)

Sets the adapter fusion configuration.

Parameters

adapter_fusion_config (str or dict) –

adapter fusion configuration, can be either:

  • a string identifying a pre-defined adapter fusion configuration

  • a dictionary representing the adapter fusion configuration

  • the path to a file containing the adapter fusion configuration

train_adapter(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock])

Sets the model into mode for training the given adapters.

train_fusion(adapter_setup: Union[list, transformers.adapters.composition.AdapterCompositionBlock], unfreeze_adapters=False)

Sets the model into mode for training of adapter fusion determined by a list of adapter names.

MBartForConditionalGeneration

class transformers.MBartForConditionalGeneration(config: transformers.models.mbart.configuration_mbart.MBartConfig)

The MBART Model with a language modeling head. Can be used for summarization. This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (MBartConfig) – 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() method to load the model weights.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs=None, past_key_values=None, inputs_embeds=None, decoder_inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The MBartForConditionalGeneration forward method, overrides the __call__() special method.

Note

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.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

    Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are 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?

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) – Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids to the right, following the paper.

  • decoder_input_ids

    Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are input IDs?

    MBart uses a specific language id token as the starting token for decoder_input_ids generation that varies according to source and target language, e.g. 25004 for en_XX, and 25003 for de_DE. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

    For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

    Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

    If you want to change padding behavior, you should read modeling_mbart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  • head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the heas is masked.

  • decoder_head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers with each tuple having 2 tuples each of which has 2 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

    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).

  • inputs_embeds (torch.FloatTensor 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.

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) –

    Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

    If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.

  • 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 instead of a plain tuple.

  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) – Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (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].

Returns

A Seq2SeqLMOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (MBartConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Language modeling loss.

  • 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).

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • decoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

  • decoder_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 of the decoder, 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).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

  • encoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

  • encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

Seq2SeqLMOutput or tuple(torch.FloatTensor)

Summarization example:

>>> from transformers import MBartTokenizer, MBartForConditionalGeneration, MBartConfig

>>> model = MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')

>>> ARTICLE_TO_SUMMARIZE = "Meine Freunde sind cool, aber sie essen zu viel Kuchen."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')

>>> # Generate Summary
>>> summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=5, early_stopping=True)
>>> print([tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids])

Mask filling example:

>>> from transformers import MBartTokenizer, MBartForConditionalGeneration
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
>>> # de_DE is the language symbol id <LID> for German
>>> TXT = "</s> Meine Freunde sind <mask> nett aber sie essen zu viel Kuchen. </s> de_DE"

>>> model = MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
>>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors='pt')['input_ids']
>>> logits = model(input_ids).logits

>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)

>>> tokenizer.decode(predictions).split()
get_output_embeddings()

Returns the model’s output embeddings.

Returns

A torch module mapping hidden states to vocabulary.

Return type

nn.Module

prepare_inputs_for_generation(decoder_input_ids, past=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs)

Implement in subclasses of PreTrainedModel for custom behavior to prepare inputs in the generate method.

resize_token_embeddings(new_num_tokens: int) → torch.nn.modules.sparse.Embedding

Resizes input token embeddings matrix of the model if new_num_tokens != config.vocab_size.

Takes care of tying weights embeddings afterwards if the model class has a tie_weights() method.

Parameters

new_num_tokens (int, optional) – The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens torch.nn.Embedding module of the model without doing anything.

Returns

Pointer to the input tokens Embeddings Module of the model.

Return type

torch.nn.Embedding

MBartForQuestionAnswering

class transformers.MBartForQuestionAnswering(config)

MBART Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layer on top of the hidden-states output to compute span start logits and span end logits).

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (MBartConfig) – 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() method to load the model weights.

forward(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs=None, start_positions=None, end_positions=None, inputs_embeds=None, decoder_inputs_embeds=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

The MBartForQuestionAnswering forward method, overrides the __call__() special method.

Note

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.

Parameters
  • input_ids (torch.LongTensor of shape (batch_size, sequence_length)) –

    Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are 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?

  • decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) – Provide for translation and summarization training. By default, the model will create this tensor by shifting the input_ids to the right, following the paper.

  • decoder_input_ids

    Indices of decoder input sequence tokens in the vocabulary.

    Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

    What are input IDs?

    MBart uses a specific language id token as the starting token for decoder_input_ids generation that varies according to source and target language, e.g. 25004 for en_XX, and 25003 for de_DE. If past_key_values is used, optionally only the last decoder_input_ids have to be input (see past_key_values).

    For translation and summarization training, decoder_input_ids should be provided. If no decoder_input_ids is provided, the model will create this tensor by shifting the input_ids to the right for denoising pre-training following the paper.

  • decoder_attention_mask (torch.LongTensor of shape (batch_size, target_sequence_length), optional) –

    Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.

    If you want to change padding behavior, you should read modeling_mbart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

  • head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the heas is masked.

  • decoder_head_mask (torch.Tensor of shape (num_layers, num_heads), optional) –

    Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in [0, 1]:

    • 1 indicates the head is not masked,

    • 0 indicates the head is masked.

  • encoder_outputs (tuple(tuple(torch.FloatTensor), optional) – Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions) last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.

  • past_key_values (Tuple[Tuple[torch.Tensor]] of length config.n_layers with each tuple having 2 tuples each of which has 2 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) –

    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).

  • inputs_embeds (torch.FloatTensor 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.

  • decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) –

    Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be input (see past_key_values). This is useful if you want more control over how to convert decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.

    If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value of inputs_embeds.

  • 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 instead of a plain tuple.

  • start_positions (torch.LongTensor of shape (batch_size,), optional) – Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

  • end_positions (torch.LongTensor of shape (batch_size,), optional) – Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss.

Returns

A Seq2SeqQuestionAnsweringModelOutput (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (MBartConfig) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.

  • start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) – Span-start scores (before SoftMax).

  • end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) – Span-end scores (before SoftMax).

  • past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).

    Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • decoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.

  • decoder_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 of the decoder, 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).

    Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.

  • encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.

  • encoder_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 + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.

  • encoder_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 of the encoder, after the attention softmax, used to compute the weighted average in the self-attention heads.

Return type

Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import MBartTokenizer, MBartForQuestionAnswering
>>> import torch

>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
>>> model = MBartForQuestionAnswering.from_pretrained('facebook/mbart-large-cc25')

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors='pt')
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])

>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits

MBartForSequenceClassification

class transformers.MBartForSequenceClassification(config: transformers.models.mbart.configuration_mbart.MBartConfig, **kwargs)

MBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.

This model inherits from 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 subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

Parameters

config (MBartConfig) – 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() method to load the model weights.

MBartForCausalLM

class transformers.MBartForCausalLM(config)
forward(input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, encoder_head_mask=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)
Args:
input_ids (torch.LongTensor of shape (batch_size, sequence_length)):

Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it.

Indices can be obtained using MBartTokenizer. See transformers.PreTrainedTokenizer.encode() and transformers.PreTrainedTokenizer.__call__() for details.

What are 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?

encoder_hidden_states (torch.FloatTensor 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.FloatTensor 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]:

head_mask (torch.Tensor of shape (num_layers, num_heads), optional):

Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]:

  • 1 indicates the head is not masked,

  • 0 indicates the heas is masked.

encoder_head_mask (torch.Tensor of shape (num_layers, num_heads), optional):

Mask to nullify selected heads of the attention modules in encoder to avoid performing cross-attention on hidden heads. Mask values selected in [0, 1]:

  • 1 indicates the head is not masked,

  • 0 indicates the heas is masked.

past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)):

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).

labels (torch.LongTensor of shape (batch_size, sequence_length), optional):

Labels for computing the masked language modeling loss. Indices should either be in [0, ..., config.vocab_size] or -100 (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].

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).

  • 1 for tokens that are not masked,

  • 0 for tokens that are masked.

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 instead of a plain tuple.

Returns

A CausalLMOutputWithCrossAttentions (if return_dict=True is passed or when config.return_dict=True) or a tuple of torch.FloatTensor comprising various elements depending on the configuration (MBartConfig) 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 + 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(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 (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.

    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.

Example:

>>> from transformers import MBartTokenizer, MBartForCausalLM

>>> tokenizer = MBartTokenizer.from_pretrained('facebook/bart-large')
>>> model = MBartForCausalLM.from_pretrained('facebook/bart-large', add_cross_attention=False)
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state

Return type

CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)