MBart

DISCLAIMER: If you see something strange, file a Github Issue and assign @sshleifer

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 pre-trained 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 `MBartTokenizer.prepare_seq2seq_batch` handles this automatically and should be used to encode the sequences for seq-2-seq 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, scale_embedding=False, normalize_embedding=True, static_position_embeddings=False, add_bias_logits=False, force_bos_token_to_be_generated=False, **common_kwargs)

model_type: str = 'mbart'

//s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/config.json.

Type

See real config values at https

MBartTokenizer

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

This inherits from XLMRobertaTokenizer. 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. 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) [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)

The BART Model with a language modeling head. Can be used for machine translation.

This model is a PyTorch torch.nn.Module sub-class. 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.

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, encoder_outputs=None, decoder_input_ids=None, decoder_attention_mask=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. Use BartTokenizer.encode to produce them. Padding will be ignored by default should you provide it. Indices can be obtained using transformers.BartTokenizer.encode(text).

• attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) – Mask to avoid performing attention on padding token indices in input_ids. Mask values selected in [0, 1]: 1 for tokens that are NOT MASKED, 0 for MASKED tokens.

• 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.

• 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 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 _prepare_decoder_inputs() and modify. See diagram 1 in the paper for more info on the default strategy

• 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 pre-computed key and value hidden-states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).

• use_cache (bool, optional, defaults to True) – If use_cache is True, past_key_values are returned and can be used to speed up decoding (see past_key_values).

• output_attentions (bool, optional) – If set to True, the attentions tensors of all attention layers are returned. See attentions under returned tensors for more detail.

• output_hidden_states (bool, optional) – If set to True, the hidden states of all layers are returned. See hidden_states under returned tensors for more detail.

• return_dict (bool, optional) –

  If set to True, the model will 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])

generate(input_ids: Optional[torch.LongTensor] = None, max_length: Optional[int] = None, min_length: Optional[int] = None, do_sample: Optional[bool] = None, early_stopping: Optional[bool] = None, num_beams: Optional[int] = None, temperature: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[float] = None, repetition_penalty: Optional[float] = None, bad_words_ids: Optional[Iterable[int]] = None, bos_token_id: Optional[int] = None, pad_token_id: Optional[int] = None, eos_token_id: Optional[int] = None, length_penalty: Optional[float] = None, no_repeat_ngram_size: Optional[int] = None, num_return_sequences: Optional[int] = None, attention_mask: Optional[torch.LongTensor] = None, decoder_start_token_id: Optional[int] = None, use_cache: Optional[bool] = None, **model_kwargs) → torch.LongTensor

Generates sequences for models with a language modeling head. The method currently supports greedy decoding, beam-search decoding, sampling with temperature, sampling with top-k or nucleus sampling.

Adapted in part from Facebook’s XLM beam search code.

Apart from input_ids and attention_mask, all the arguments below will default to the value of the attribute of the same name inside the PretrainedConfig of the model. The default values indicated are the default values of those config.

Most of these parameters are explained in more detail in this blog post.

Parameters

• input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) – The sequence used as a prompt for the generation. If None the method initializes it as an empty torch.LongTensor of shape (1,).

• max_length (int, optional, defaults to 20) – The maximum length of the sequence to be generated.

• min_length (int, optional, defaults to 10) – The minimum length of the sequence to be generated.

• do_sample (bool, optional, defaults to False) – Whether or not to use sampling ; use greedy decoding otherwise.

• early_stopping (bool, optional, defaults to False) – Whether to stop the beam search when at least num_beams sentences are finished per batch or not.

• num_beams (int, optional, defaults to 1) – Number of beams for beam search. 1 means no beam search.

• temperature (float, optional, defaults tp 1.0) – The value used to module the next token probabilities.

• top_k (int, optional, defaults to 50) – The number of highest probability vocabulary tokens to keep for top-k-filtering.

• top_p (float, optional, defaults to 1.0) – If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.

• repetition_penalty (float, optional, defaults to 1.0) – The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details.

• pad_token_id (int, optional) – The id of the padding token.

• bos_token_id (int, optional) – The id of the beginning-of-sequence token.

• eos_token_id (int, optional) – The id of the end-of-sequence token.

• length_penalty (float, optional, defaults to 1.0) –

  Exponential penalty to the length. 1.0 means no penalty.

  Set to values < 1.0 in order to encourage the model to generate shorter sequences, to a value > 1.0 in order to encourage the model to produce longer sequences.

• no_repeat_ngram_size (int, optional, defaults to 0) – If set to int > 0, all ngrams of that size can only occur once.

• bad_words_ids (List[int], optional) – List of token ids that are not allowed to be generated. In order to get the tokens of the words that should not appear in the generated text, use tokenizer.encode(bad_word, add_prefix_space=True).

• num_return_sequences (int, optional, defaults to 1) – The number of independently computed returned sequences for each element in the batch.

• attention_mask (torch.LongTensor of shape (batch_size, sequence_length), optional) –

  Mask to avoid performing attention on padding token indices. Mask values are in [0, 1], 1 for tokens that are not masked, and 0 for masked tokens.

  If not provided, will default to a tensor the same shape as input_ids that masks the pad token.

  What are attention masks?

• decoder_start_token_id (int, optional) – If an encoder-decoder model starts decoding with a different token than bos, the id of that token.

• use_cache – (bool, optional, defaults to True): Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding.

• model_kwargs – Additional model specific kwargs will be forwarded to the forward function of the model.

Returns

The generated sequences. The second dimension (sequence_length) is either equal to max_length or shorter if all batches finished early due to the eos_token_id.

Return type

torch.LongTensor of shape (batch_size * num_return_sequences, sequence_length)

Examples:

tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
outputs = model.generate(max_length=40)  # do greedy decoding
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('openai-gpt')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('openai-gpt')    # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, num_beams=5, num_return_sequences=3, temperature=1.5)  # generate 3 independent sequences using beam search decoding (5 beams) with sampling from initial context 'The dog'
for i in range(3): #  3 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('distilgpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('distilgpt2')    # Download model and configuration from S3 and cache.
input_context = 'The dog'
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.7, num_return_sequences=3, do_sample=True)  # generate 3 candidates using sampling
for i in range(3): #  3 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('ctrl')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('ctrl')    # Download model and configuration from S3 and cache.
input_context = 'Legal My neighbor is'  # "Legal" is one of the control codes for ctrl
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=50, temperature=0.7, repetition_penalty=1.2)  # generate sequences
print('Generated: {}'.format(tokenizer.decode(outputs[0], skip_special_tokens=True)))

tokenizer = AutoTokenizer.from_pretrained('gpt2')   # Initialize tokenizer
model = AutoModelWithLMHead.from_pretrained('gpt2')    # Download model and configuration from S3 and cache.
input_context = 'My cute dog'  # "Legal" is one of the control codes for ctrl
bad_words_ids = [tokenizer.encode(bad_word, add_prefix_space=True) for bad_word in ['idiot', 'stupid', 'shut up']]
input_ids = tokenizer.encode(input_context, return_tensors='pt')  # encode input context
outputs = model.generate(input_ids=input_ids, max_length=100, do_sample=True, bad_words_ids=bad_words_ids)  # generate sequences without allowing bad_words to be generated