MBart

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Overview

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 pre-training 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.

The Authors’ code can be found here

Training

MBart is a multilingual encoder-decoder (seq-to-seq) model primarily intended for translation task. As the model is multilingual it expects the sequences in a different format. A special language id token is added in both the source and target text. The source text format is X [eos, src_lang_code] where X is the source text. The target text format is [tgt_lang_code] X [eos]. bos is never used.

The prepare_seq2seq_batch() handles this automatically and should be used to encode the sequences for sequence-to-sequence fine-tuning.

• Supervised training

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"
batch = tokenizer.prepare_seq2seq_batch(example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian)
input_ids = batch["input_ids"]
target_ids = batch["decoder_input_ids"]
decoder_input_ids = target_ids[:, :-1].contiguous()
labels = target_ids[:, 1:].clone()
model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, labels=labels) #forward

• Generation

  While generating the target text set the decoder_start_token_id to the target language id. The following example shows how to translate English to Romanian using the facebook/mbart-large-en-ro model.

from transformers import MBartForConditionalGeneration, MBartTokenizer
model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
article = "UN Chief Says There Is No Military Solution in Syria"
batch = tokenizer.prepare_seq2seq_batch(src_texts=[article], src_lang="en_XX")
translated_tokens = model.generate(**batch, decoder_start_token_id=tokenizer.lang_code_to_id["ro_RO"])
translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"

MBartConfig

class transformers.MBartConfig(activation_dropout=0.0, extra_pos_embeddings=2, activation_function='gelu', vocab_size=50265, d_model=1024, encoder_ffn_dim=4096, encoder_layers=12, encoder_attention_heads=16, decoder_ffn_dim=4096, decoder_layers=12, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, attention_dropout=0.0, dropout=0.1, max_position_embeddings=1024, init_std=0.02, classifier_dropout=0.0, num_labels=3, is_encoder_decoder=True, pad_token_id=1, bos_token_id=0, eos_token_id=2, normalize_before=False, add_final_layer_norm=False, do_blenderbot_90_layernorm=False, scale_embedding=False, normalize_embedding=True, static_position_embeddings=False, add_bias_logits=False, force_bos_token_to_be_generated=False, **common_kwargs)

This is the configuration class to store the configuration of a MBartForConditionalGeneration. It is used to instantiate a BART model according to the specified arguments, defining the model 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 250027) – Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling MBartForConditionalGeneration.

• 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” (i.e., feed-forward) layer in decoder.

• encoder_ffn_dim (int, optional, defaults to 4096) – Dimensionality of the “intermediate” (i.e., 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", "swish" and "gelu_new" are supported.

• dropout (float, optional, defaults to 0.1) – The dropout probabilitiy 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.

• add_bias_logits (bool, optional, defaults to False) – This should be completed, specific to marian.

• normalize_before (bool, optional, defaults to True) – Call layernorm before attention ops.

• normalize_embedding (bool, optional, defaults to True) – Call layernorm after embeddings. Only True for Bart.

• static_position_embeddings (bool, optional, defaults to False) – Don’t learn positional embeddings, use sinusoidal.

• add_final_layer_norm (bool, optional, defaults to True) – Why not add another layernorm?

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

• eos_token_id (int, optional, defaults to 2) – End of stream token id.

• pad_token_id (int, optional, defaults to 1) – Padding token id.

• bos_token_id (int, optional, defaults to 0) – Beginning of stream token id.

• 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.

• extra_pos_embeddings – (int, optional, defaults to 2): How many extra learned positional embeddings to use. Should be equal to pad_token_id+1.

• is_encoder_decoder (bool, optional, defaults to True) – Whether this is an encoder/decoder model

• force_bos_token_to_be_generated (bool, optional, defaults to False) – Whether or not to force BOS token to be generated at step 1 (after decoder_start_token_id).

MBartTokenizer

class transformers.MBartTokenizer(*args, tokenizer_file=None, **kwargs)

Construct an MBART tokenizer.

MBartTokenizer is a subclass of XLMRobertaTokenizer and adds a new prepare_seq2seq_batch()

Refer to superclass XLMRobertaTokenizer for usage examples and documentation concerning the initialization parameters and other methods.

Warning

prepare_seq2seq_batch should be used to encode inputs. Other tokenizer methods like encode do not work properly.

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 MBartTokenizer
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-en-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"
>>> batch: dict = tokenizer.prepare_seq2seq_batch(
...     example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian
... )

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. 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) [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]

prepare_seq2seq_batch(src_texts: List[str], src_lang: str = 'en_XX', tgt_texts: Optional[List[str]] = None, tgt_lang: str = 'ro_RO', max_length: Optional[int] = None, max_target_length: Optional[int] = None, truncation: bool = True, padding: str = 'longest', return_tensors: str = 'pt', add_prefix_space: bool = False, **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, defaults to “pt”) –

  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.

• decoder_input_ids – List of token ids to be fed to the decoder.

• decoder_attention_mask – List of indices specifying which tokens should be attended to by the decoder.

  This does not include causal mask, which is built by the model.

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

Return type

BatchEncoding

MBartForConditionalGeneration

class transformers.MBartForConditionalGeneration(config: transformers.configuration_bart.BartConfig)

This class overrides BartForConditionalGeneration. Please check the superclass for the appropriate documentation alongside usage examples.

Examples::

>>> from transformers import MBartForConditionalGeneration, MBartTokenizer
>>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro")
>>> article = "UN Chief Says There Is No Military Solution in Syria"
>>> batch = tokenizer.prepare_seq2seq_batch(src_texts=[article])
>>> translated_tokens = model.generate(**batch)
>>> translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
>>> assert translation == "Şeful ONU declară că nu există o soluţie militară în Siria"

forward(input_ids, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, encoder_outputs=None, past_key_values=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **unused)

The BartForConditionalGeneration 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 BartTokenizer. 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_attention_mask (torch.BoolTensor of shape (batch_size, tgt_seq_len), 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_bart._prepare_decoder_inputs() and modify to your needs. See diagram 1 in the paper for more information on the default strategy.

• 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.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).

• 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 (BartConfig) and inputs.

• loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) – Languaged 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 (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder 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.

• 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.

Conditional generation example:

>>> # Mask filling only works for bart-large
>>> from transformers import BartTokenizer, BartForConditionalGeneration
>>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large')
>>> TXT = "My friends are <mask> but they eat too many carbs."

>>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
>>> input_ids = tokenizer([TXT], 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()
>>> # ['good', 'great', 'all', 'really', 'very']

Return type

Seq2SeqLMOutput or tuple(torch.FloatTensor)

Summarization example:

>>> from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig

>>> # see ``examples/summarization/bart/run_eval.py`` for a longer example
>>> model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
>>> tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> 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])