MBart and MBart-50¶

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

Overview of 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.

This model was contributed by valhalla. The Authors’ code can be found here

Training of MBart¶

MBart is a multilingual encoder-decoder (sequence-to-sequence) 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 regular __call__() will encode source text format, and it should be wrapped inside the context manager as_target_tokenizer() to encode target text format.

Supervised training

>>> from transformers import MBartForConditionalGeneration, 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"

>>> inputs = tokenizer(example_english_phrase, return_tensors="pt", src_lang="en_XX", tgt_lang="ro_RO")
>>> with tokenizer.as_target_tokenizer():
...     labels = tokenizer(expected_translation_romanian, return_tensors="pt")

>>> model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-en-ro")
>>> # forward pass
>>> model(**inputs, labels=batch['labels'])

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

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

Overview of MBart-50¶

MBart-50 was introduced in the Multilingual Translation with Extensible Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401> paper by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan. MBart-50 is created using the original mbart-large-cc25 checkpoint by extendeding its embedding layers with randomly initialized vectors for an extra set of 25 language tokens and then pretrained on 50 languages.

According to the abstract

Multilingual translation models can be created through multilingual finetuning. Instead of finetuning on one direction, a pretrained model is finetuned on many directions at the same time. It demonstrates that pretrained models can be extended to incorporate additional languages without loss of performance. Multilingual finetuning improves on average 1 BLEU over the strongest baselines (being either multilingual from scratch or bilingual finetuning) while improving 9.3 BLEU on average over bilingual baselines from scratch.

Training of MBart-50¶

The text format for MBart-50 is slightly different from mBART. For MBart-50 the language id token is used as a prefix for both source and target text i.e the text format is [lang_code] X [eos], where lang_code is source language id for source text and target language id for target text, with X being the source or target text respectively.

MBart-50 has its own tokenizer MBart50Tokenizer.

Supervised training

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50")
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) # forward pass

Generation

To generate using the mBART-50 multilingual translation models, eos_token_id is used as the decoder_start_token_id and the target language id is forced as the first generated token. To force the target language id as the first generated token, pass the forced_bos_token_id parameter to the generate method. The following example shows how to translate between Hindi to French and Arabic to English using the facebook/mbart-50-large-many-to-many checkpoint.

from transformers import MBartForConditionalGeneration, MBart50TokenizerFast

article_hi = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
article_ar = "الأمين العام للأمم المتحدة يقول إنه لا يوجد حل عسكري في سوريا."

model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")

# translate Hindi to French
tokenizer.src_lang = "hi_IN"
encoded_hi = tokenizer(article_hi, return_tensors="pt")
generated_tokens = model.generate(**encoded_hi, forced_bos_token_id=tokenizer.lang_code_to_id["fr_XX"])
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "Le chef de l 'ONU affirme qu 'il n 'y a pas de solution militaire en Syria."

# translate Arabic to English
tokenizer.src_lang = "ar_AR"
encoded_ar = tokenizer(article_ar, return_tensors="pt")
generated_tokens = model.generate(**encoded_ar, forced_bos_token_id=tokenizer.lang_code_to_id["en_XX"])
tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
# => "The Secretary-General of the United Nations says there is no military solution in Syria."

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)[source]¶

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, tokenizer_file=None, src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs)[source]¶

Construct an MBART tokenizer.

MBartTokenizer is a subclass of XLMRobertaTokenizer. Refer to superclass XLMRobertaTokenizer 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 MBartTokenizer
>>> tokenizer = MBartTokenizer.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()[source]¶: 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][source]¶

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) 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]

MBartTokenizerFast¶

class transformers.MBartTokenizerFast(vocab_file=None, tokenizer_file=None, src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs)[source]¶

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()[source]¶: 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][source]¶

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]

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[source]¶

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

slow_tokenizer_class¶: alias of transformers.models.mbart.tokenization_mbart.MBartTokenizer

MBart50Tokenizer¶

class transformers.MBart50Tokenizer(vocab_file, src_lang=None, tgt_lang=None, eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs)[source]¶

Construct a MBart50 tokenizer. Based on SentencePiece.

This tokenizer inherits from PreTrainedTokenizer 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.
sp_model_kwargs (dict, optional) –
Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for SentencePiece can be used, among other things, to set:
- enable_sampling: Enable subword regularization.
- nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout.
  - nbest_size = {0,1}: No sampling is performed.
  - nbest_size > 1: samples from the nbest_size results.
  - nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm.
- alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout.

Examples:

>>> from transformers import MBart50Tokenizer
>>> tokenizer = MBart50Tokenizer.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()[source]¶: 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][source]¶

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. 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]

convert_tokens_to_string(tokens: List[str]) → str[source]¶: Converts a sequence of tokens (strings for sub-words) in a single string.

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

Retrieve 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]

get_vocab() → Dict[source]¶

Returns the vocabulary as a dictionary of token to index.

tokenizer.get_vocab()[token] is equivalent to tokenizer.convert_tokens_to_ids(token) when token is in the vocab.

Returns: The vocabulary.
Return type: Dict[str, 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[source]¶

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][source]¶

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[source]¶: 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[source]¶: Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos].

property vocab_size¶

Size of the base vocabulary (without the added tokens).

Type: int

MBart50TokenizerFast¶

class transformers.MBart50TokenizerFast(vocab_file=None, 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)[source]¶

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()[source]¶: 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][source]¶

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]

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[source]¶

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][source]¶

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

slow_tokenizer_class¶: alias of transformers.models.mbart.tokenization_mbart50.MBart50Tokenizer

MBartModel¶

class transformers.MBartModel(config: transformers.models.mbart.configuration_mbart.MBartConfig)[source]¶

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, cross_attn_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)[source]¶

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) –
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 decoder 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.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_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 head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_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.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) –
Mask to nullify selected heads of the cross-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.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.

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 or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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()[source]¶

Returns the model’s input embeddings.

Returns: A torch module mapping vocabulary to hidden states.
Return type: nn.Module

set_input_embeddings(value)[source]¶

Set model’s input embeddings.

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

MBartForConditionalGeneration¶

class transformers.MBartForConditionalGeneration(config: transformers.models.mbart.configuration_mbart.MBartConfig)[source]¶

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, cross_attn_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)[source]¶

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) –
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 decoder 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.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_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 head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_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.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) –
Mask to nullify selected heads of the cross-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.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.

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 or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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()[source]¶

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, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, use_cache=None, encoder_outputs=None, **kwargs)[source]¶: 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[source]¶

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)[source]¶

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, cross_attn_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)[source]¶

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) –
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 decoder 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.
head_mask (torch.Tensor of shape (encoder_layers, encoder_attention_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 head is masked.
decoder_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_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.
cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) –
Mask to nullify selected heads of the cross-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.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.

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 or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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)[source]¶

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)[source]¶

forward(input_ids=None, attention_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, head_mask=None, cross_attn_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)[source]¶

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 (decoder_layers, decoder_attention_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 head is masked.

cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:

1 indicates the head is not masked,

0 indicates the head is masked.

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). The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model.

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.

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 or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (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)

TFMBartModel¶

class transformers.TFMBartModel(*args, **kwargs)[source]¶

The bare MBART Model outputting raw hidden-states without any specific head on top. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

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.

call(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[Union[Tuple, transformers.modeling_tf_outputs.TFBaseModelOutput]] = 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, training=False, **kwargs)[source]¶

The TFMBartModel 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 (tf.Tensor of shape (batch_size, sequence_length)) –
Indices of 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?
attention_mask (tf.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 (tf.Tensor of shape (batch_size, target_sequence_length), optional) –
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 decoder 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 (tf.Tensor of shape (batch_size, target_sequence_length), optional) – will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_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 head is masked.
decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_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.
cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) –
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tf.FloatTensor, optional) – hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) – contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation
output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to False) – Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).

Returns

A TFSeq2SeqModelOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

last_hidden_state (tf.Tensor 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 (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights 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 (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

Return type

TFSeq2SeqModelOutput or tuple(tf.Tensor)

Example:

>>> from transformers import MBartTokenizer, TFMBartModel
>>> import tensorflow as tf

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

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

>>> last_hidden_states = outputs.last_hidden_state

TFMBartForConditionalGeneration¶

class transformers.TFMBartForConditionalGeneration(*args, **kwargs)[source]¶

The MBART Model with a language modeling head. Can be used for summarization. This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a tf.keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.

Note

TF 2.0 models accepts two formats as inputs:

having all inputs as keyword arguments (like PyTorch models), or
having all inputs as a list, tuple or dict in the first positional arguments.

This second option is useful when using tf.keras.Model.fit() method which currently requires having all the tensors in the first argument of the model call function: model(inputs).

If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the first positional argument :

a single Tensor with input_ids only and nothing else: model(input_ids)
a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])
a dictionary with one or several input Tensors associated to the input names given in the docstring: model({"input_ids": input_ids, "token_type_ids": token_type_ids})

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.

call(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None, encoder_outputs: Optional[transformers.modeling_tf_outputs.TFBaseModelOutput] = 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, labels=None, training=False, **kwargs)[source]¶

The TFMBartForConditionalGeneration 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 (tf.Tensor of shape ({0})) –
Indices of 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?
attention_mask (tf.Tensor of shape ({0}), optional) –
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
- 1 for tokens that are not masked,
- 0 for tokens that are masked.
What are attention masks?
decoder_input_ids (tf.Tensor of shape (batch_size, target_sequence_length), optional) –
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 decoder 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 (tf.Tensor of shape (batch_size, target_sequence_length), optional) – will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
head_mask (tf.Tensor of shape (encoder_layers, encoder_attention_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 head is masked.
decoder_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_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.
cross_attn_head_mask (tf.Tensor of shape (decoder_layers, decoder_attention_heads), optional) –
Mask to nullify selected heads of the cross-attention modules. Mask values selected in [0, 1]:
- 1 indicates the head is not masked,
- 0 indicates the head is masked.
encoder_outputs (tf.FloatTensor, optional) – hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. of shape (batch_size, sequence_length, hidden_size) is a sequence of
past_key_values (Tuple[Tuple[tf.Tensor]] of length config.n_layers) – contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional, defaults to True) – If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation
output_attentions (bool, optional) – Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
output_hidden_states (bool, optional) – Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead.
return_dict (bool, optional) – Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True.
training (bool, optional, defaults to False) – Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
labels (tf.Tensor 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 TFSeq2SeqLMOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) – Language modeling loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) – Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) – List of tf.Tensor 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(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

Attentions weights 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 (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) – Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

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

Return type

TFSeq2SeqLMOutput or tuple(tf.Tensor)

Summarization example:

>>> from transformers import MBartTokenizer, TFMBartForConditionalGeneration, 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='tf')

>>> # 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, TFMBartForConditionalGeneration
>>> 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='tf')['input_ids']
>>> logits = model(input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token

FlaxMBartModel¶

class transformers.FlaxMBartModel(config: transformers.models.mbart.configuration_mbart.MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]¶

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

Finally, this model supports inherent JAX features such as:

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.

__call__(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_input_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Returns

A FlaxSeq2SeqModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

last_hidden_state (jax_xla.DeviceArray 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(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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 (jax_xla.DeviceArray 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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

FlaxSeq2SeqModelOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import MBartTokenizer, FlaxMBartModel

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

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

>>> last_hidden_states = outputs.last_hidden_state

decode(decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) –
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 decoder input IDs?

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.
encoder_outputs (tuple(tuple(jnp.ndarray)) – 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.
encoder_attention_mask (jnp.ndarray 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_attention_mask (jnp.ndarray 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 modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) – Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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 FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 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 optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state

Return type

FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

encode(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

input_ids (jnp.ndarray 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 (jnp.ndarray 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?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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 FlaxBaseModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

Return type

FlaxBaseModelOutput or tuple(torch.FloatTensor)

FlaxMBartForConditionalGeneration¶

class transformers.FlaxMBartForConditionalGeneration(config: transformers.models.mbart.configuration_mbart.MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]¶

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

Finally, this model supports inherent JAX features such as:

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.

__call__(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_input_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

The FlaxMBartPreTrainedModel 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 (jnp.ndarray 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 (jnp.ndarray 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 (jnp.ndarray of shape (batch_size, target_sequence_length), optional) –
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 decoder input IDs?

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 (jnp.ndarray 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 modify to your needs. See diagram 1 in the paper for more information on the default strategy.
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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 FlaxSeq2SeqLMOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

logits (jax_xla.DeviceArray 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(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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 (jax_xla.DeviceArray 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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

FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)

Summarization example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='jax')

>>> # Generate Summary
>>> summary_ids = model.generate(inputs['input_ids'], decoder_start_token_id=tokenizer.lang_code_to_id[tgt_lang]).sequences
>>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False))

Mask filling example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
>>> tokenizer = MBartTokenizer.from_pretrained('facebook/mbart-large-cc25')
>>> TXT = "My friends are <mask> but they eat too many carbs."

>>> model = FlaxMBartForConditionalGeneration.from_pretrained('facebook/mbart-large-cc25')
>>> input_ids = tokenizer([TXT], return_tensors='jax')['input_ids']
>>> logits = model(input_ids).logits

>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = jax.nn.softmax(logits[0, masked_index], axis=0)
>>> values, predictions = jax.lax.top_k(probs)

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

decode(decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, deterministic: bool = True, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)[source]¶

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) –
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 decoder input IDs?

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.
encoder_outputs (tuple(tuple(jnp.ndarray)) – 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.
encoder_attention_mask (jnp.ndarray 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_attention_mask (jnp.ndarray 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 modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) – Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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 FlaxCausalLMOutputWithCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

logits (jax_xla.DeviceArray 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of jax_xla.DeviceArray 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, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> logits = outputs.logits

Return type

FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)

encode(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

input_ids (jnp.ndarray 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 (jnp.ndarray 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?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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 FlaxBaseModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

Return type

FlaxBaseModelOutput or tuple(torch.FloatTensor)

FlaxMBartForSequenceClassification¶

class transformers.FlaxMBartForSequenceClassification(config: transformers.models.mbart.configuration_mbart.MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]¶

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 FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

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.

__call__(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_input_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Returns

A FlaxSeq2SeqSequenceClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

logits (jax_xla.DeviceArray of shape (batch_size, config.num_labels)) – Classification (or regression if config.num_labels==1) scores (before SoftMax).
past_key_values (tuple(tuple(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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 (jax_xla.DeviceArray 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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

FlaxSeq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForSequenceClassification

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

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors='jax')

>>> outputs = model(**inputs, labels=labels)
>>> logits = outputs.logits

decode(decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) –
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 decoder input IDs?

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.
encoder_outputs (tuple(tuple(jnp.ndarray)) – 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.
encoder_attention_mask (jnp.ndarray 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_attention_mask (jnp.ndarray 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 modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) – Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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 FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 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 optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state

Return type

FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

encode(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

input_ids (jnp.ndarray 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 (jnp.ndarray 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?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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 FlaxBaseModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

Return type

FlaxBaseModelOutput or tuple(torch.FloatTensor)

FlaxMBartForQuestionAnswering¶

class transformers.FlaxMBartForQuestionAnswering(config: transformers.models.mbart.configuration_mbart.MBartConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]¶

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 FlaxPreTrainedModel. 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 Flax Linen flax.nn.Module subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.

Finally, this model supports inherent JAX features such as:

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.

__call__(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_input_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Returns

A FlaxSeq2SeqQuestionAnsweringModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (MBartConfig) and inputs.

start_logits (jax_xla.DeviceArray of shape (batch_size, sequence_length)) – Span-start scores (before SoftMax).
end_logits (jax_xla.DeviceArray of shape (batch_size, sequence_length)) – Span-end scores (before SoftMax).
past_key_values (tuple(tuple(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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 (jax_xla.DeviceArray 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(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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

FlaxSeq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor)

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForQuestionAnswering

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

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors='jax')

>>> outputs = model(**inputs)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits

decode(decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, decoder_position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

decoder_input_ids (jnp.ndarray of shape (batch_size, target_sequence_length)) –
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 decoder input IDs?

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.
encoder_outputs (tuple(tuple(jnp.ndarray)) – 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.
encoder_attention_mask (jnp.ndarray 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_attention_mask (jnp.ndarray 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 modify to your needs. See diagram 1 in the paper for more information on the default strategy.
decoder_position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
past_key_values (Dict[str, np.ndarray], optional, returned by init_cache or when passing previous past_key_values) – Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape [batch_size, max_length].
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 FlaxBaseModelOutputWithPastAndCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.

If past_key_values is used only the last hidden-state of the sequences of shape (batch_size, 1, hidden_size) is output.
past_key_values (tuple(tuple(jax_xla.DeviceArray)), optional, returned when use_cache=True is passed or when config.use_cache=True) – Tuple of tuple(jax_xla.DeviceArray) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 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 optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id

>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state

Return type

FlaxBaseModelOutputWithPastAndCrossAttentions or tuple(torch.FloatTensor)

encode(input_ids: jax._src.numpy.lax_numpy.ndarray, attention_mask: Optional[jax._src.numpy.lax_numpy.ndarray] = None, position_ids: Optional[jax._src.numpy.lax_numpy.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: jax._src.random.PRNGKey = None)¶

Parameters

input_ids (jnp.ndarray 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 (jnp.ndarray 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?
position_ids (numpy.ndarray of shape (batch_size, sequence_length), optional) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
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 FlaxBaseModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (~transformers.) and inputs.

last_hidden_state (jax_xla.DeviceArray of shape (batch_size, sequence_length, hidden_size)) – Sequence of hidden-states at the output of the last layer of the model.
hidden_states (tuple(jax_xla.DeviceArray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) – Tuple of jax_xla.DeviceArray (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(jax_xla.DeviceArray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) – Tuple of jax_xla.DeviceArray (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.

Example:

>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration

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

>>> text = "My friends are cool but they eat too many carbs."
>>> inputs = tokenizer(text, max_length=1024, return_tensors='jax')
>>> encoder_outputs = model.encode(**inputs)

Return type

FlaxBaseModelOutput or tuple(torch.FloatTensor)