
 
Encoder Decoder Models
	
Overview
	
The EncoderDecoderModel can be used to initialize a sequence-to-sequence model with any
pretrained autoencoding model as the encoder and any pretrained autoregressive model as the decoder.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation tasks
was shown in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by
Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
After such an EncoderDecoderModel has been trained/fine-tuned, it can be saved/loaded just like
any other models (see the examples for more information).
An application of this architecture could be to leverage two pretrained BertModel as the encoder
and decoder for a summarization model as was shown in: Text Summarization with Pretrained Encoders by Yang Liu and Mirella Lapata.
Randomly initializing EncoderDecoderModel from model configurations.
	
EncoderDecoderModel can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default BertModel configuration for the encoder and the default BertForCausalLM configuration for the decoder.
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 from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
 config_encoder = BertConfig()
 config_decoder = BertConfig()
 config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
 model = EncoderDecoderModel(config=config)
Initialising EncoderDecoderModel from a pretrained encoder and a pretrained decoder.
	
EncoderDecoderModel can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained auto-encoding model, e.g. BERT, can serve as the encoder and both pretrained auto-encoding models, e.g. BERT, pretrained causal language models, e.g. GPT2, as well as the pretrained decoder part of sequence-to-sequence models, e.g. decoder of BART, can be used as the decoder.
Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
Initializing EncoderDecoderModel from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in the Warm-starting-encoder-decoder blog post.
To do so, the EncoderDecoderModel class provides a EncoderDecoderModel.from_encoder_decoder_pretrained() method.
	Copied
 from transformers import EncoderDecoderModel, BertTokenizer
 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
Loading an existing EncoderDecoderModel checkpoint and perform inference.
	
To load fine-tuned checkpoints of the EncoderDecoderModel class, EncoderDecoderModel provides the from_pretrained(...) method just like any other model architecture in Transformers.
To perform inference, one uses the generate method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
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 from transformers import AutoTokenizer, EncoderDecoderModel
 # load a fine-tuned seq2seq model and corresponding tokenizer
 model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
 tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
 # let's perform inference on a long piece of text
 ARTICLE_TO_SUMMARIZE = (
...     "PG&E stated it scheduled the blackouts in response to forecasts for high winds "
...     "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were "
...     "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."
... )
 input_ids = tokenizer(ARTICLE_TO_SUMMARIZE, return_tensors="pt").input_ids
 # autoregressively generate summary (uses greedy decoding by default)
 generated_ids = model.generate(input_ids)
 generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(generated_text)
nearly 800 thousand customers were affected by the shutoffs. the aim is to reduce the risk of wildfires. nearly 800, 000 customers were expected to be affected by high winds amid dry conditions. pg & e said it scheduled the blackouts to last through at least midday tomorrow.
Loading a PyTorch checkpoint into TFEncoderDecoderModel.
	
TFEncoderDecoderModel.from_pretrained() currently doesn’t support initializing the model from a
pytorch checkpoint. Passing from_pt=True to this method will throw an exception. If there are only pytorch
checkpoints for a particular encoder-decoder model, a workaround is:
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 # a workaround to load from pytorch checkpoint
 from transformers import EncoderDecoderModel, TFEncoderDecoderModel
 _model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
 _model.encoder.save_pretrained("./encoder")
 _model.decoder.save_pretrained("./decoder")
 model = TFEncoderDecoderModel.from_encoder_decoder_pretrained(
...     "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True
... )
 # This is only for copying some specific attributes of this particular model.
 model.config = _model.config
Training
	
Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model.
As you can see, only 2 inputs are required for the model in order to compute a loss: input_ids (which are the
input_ids of the encoded input sequence) and labels (which are the input_ids of the encoded
target sequence).
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 from transformers import BertTokenizer, EncoderDecoderModel
 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
 model.config.decoder_start_token_id = tokenizer.cls_token_id
 model.config.pad_token_id = tokenizer.pad_token_id
 input_ids = tokenizer(
...     "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side.During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was  finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft).Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.",
...     return_tensors="pt",
... ).input_ids
 labels = tokenizer(
...     "the eiffel tower surpassed the washington monument to become the tallest structure in the world. it was the first structure to reach a height of 300 metres in paris in 1930. it is now taller than the chrysler building by 5. 2 metres ( 17 ft ) and is the second tallest free - standing structure in paris.",
...     return_tensors="pt",
... ).input_ids
 # the forward function automatically creates the correct decoder_input_ids
 loss = model(input_ids=input_ids, labels=labels).loss
Detailed colab for training.
This model was contributed by thomwolf. This model’s TensorFlow and Flax versions
were contributed by ydshieh.
EncoderDecoderConfig
	
class transformers.EncoderDecoderConfig
<
source
>
(
**kwargs
)
Parameters 
kwargs (optional) —
Dictionary of keyword arguments. Notably:
encoder (PretrainedConfig, optional) — An instance of a configuration object that defines
the encoder config.
decoder (PretrainedConfig, optional) — An instance of a configuration object that defines
the decoder config.
EncoderDecoderConfig is the configuration class to store the configuration of a EncoderDecoderModel. It is
used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder
configs.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the
documentation from PretrainedConfig for more information.
Examples:
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 from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
 # Initializing a BERT bert-base-uncased style configuration
 config_encoder = BertConfig()
 config_decoder = BertConfig()
 config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
 # Initializing a Bert2Bert model from the bert-base-uncased style configurations
 model = EncoderDecoderModel(config=config)
 # Accessing the model configuration
 config_encoder = model.config.encoder
 config_decoder = model.config.decoder
 # set decoder config to causal lm
 config_decoder.is_decoder = True
 config_decoder.add_cross_attention = True
 # Saving the model, including its configuration
 model.save_pretrained("my-model")
 # loading model and config from pretrained folder
 encoder_decoder_config = EncoderDecoderConfig.from_pretrained("my-model")
 model = EncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
from_encoder_decoder_configs
<
source
>
(
encoder_config: PretrainedConfig
decoder_config: PretrainedConfig
**kwargs
)
→
EncoderDecoderConfig
Returns
EncoderDecoderConfig
An instance of a configuration object
Instantiate a EncoderDecoderConfig (or a derived class) from a pre-trained encoder model configuration and
decoder model configuration.
to_dict
<
source
>
(
)
→
Dict[str, any]
Returns
Dict[str, any]
Dictionary of all the attributes that make up this configuration instance,
Serializes this instance to a Python dictionary. Override the default to_dict() from PretrainedConfig.
EncoderDecoderModel
	
class transformers.EncoderDecoderModel
<
source
>
(
config: typing.Optional[transformers.configuration_utils.PretrainedConfig] = None
encoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None
decoder: typing.Optional[transformers.modeling_utils.PreTrainedModel] = None
)
Parameters 
config (EncoderDecoderConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
from_pretrained() function and the decoder is loaded via from_pretrained()
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like summarization.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models
(see the examples for more information).
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.
EncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture with one
of the base model classes of the library as encoder and another one as decoder when created with the
:meth~transformers.AutoModel.from_pretrained class method for the encoder and
:meth~transformers.AutoModelForCausalLM.from_pretrained class method for the decoder.
forward
<
source
>
(
input_ids: typing.Optional[torch.LongTensor] = None
attention_mask: typing.Optional[torch.FloatTensor] = None
decoder_input_ids: typing.Optional[torch.LongTensor] = None
decoder_attention_mask: typing.Optional[torch.BoolTensor] = None
encoder_outputs: typing.Optional[typing.Tuple[torch.FloatTensor]] = None
past_key_values: typing.Tuple[typing.Tuple[torch.FloatTensor]] = None
inputs_embeds: typing.Optional[torch.FloatTensor] = None
decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None
labels: typing.Optional[torch.LongTensor] = None
use_cache: typing.Optional[bool] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
**kwargs
)
→
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)
Parameters 
input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (torch.FloatTensor 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 PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
For training, decoder_input_ids are automatically created by the model by shifting the labels to the
right, replacing -100 by the pad_token_id and prepending them with the decoder_start_token_id.
decoder_attention_mask (torch.BoolTensor 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.
encoder_outputs (tuple(torch.FloatTensor), optional) —
This tuple must consist of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) is a tensor
of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the
decoder.
past_key_values (tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
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. 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.
labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss for the decoder. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
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) —
If set to True, the model will return a ~utils.Seq2SeqLMOutput instead of a plain tuple.
kwargs (optional) — Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
Without a prefix which will be input as **encoder_kwargs for the encoder forward function.
With a decoder_ prefix which will be input as **decoder_kwargs for the decoder forward function.
Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_outputs.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 (EncoderDecoderConfig) 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, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the 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, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the 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.
The EncoderDecoderModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import EncoderDecoderModel, BertTokenizer
 import torch
 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 model = EncoderDecoderModel.from_encoder_decoder_pretrained(
...     "bert-base-uncased", "bert-base-uncased"
... )  # initialize Bert2Bert from pre-trained checkpoints
 # training
 model.config.decoder_start_token_id = tokenizer.cls_token_id
 model.config.pad_token_id = tokenizer.pad_token_id
 model.config.vocab_size = model.config.decoder.vocab_size
 input_ids = tokenizer("This is a really long text", return_tensors="pt").input_ids
 labels = tokenizer("This is the corresponding summary", return_tensors="pt").input_ids
 outputs = model(input_ids=input_ids, labels=labels)
 loss, logits = outputs.loss, outputs.logits
 # save and load from pretrained
 model.save_pretrained("bert2bert")
 model = EncoderDecoderModel.from_pretrained("bert2bert")
 # generation
 generated = model.generate(input_ids)
from_encoder_decoder_pretrained
<
source
>
(
encoder_pretrained_model_name_or_path: str = None
decoder_pretrained_model_name_or_path: str = None
*model_args
**kwargs
)
Parameters 
encoder_pretrained_model_name_or_path (str, optional) —
Information necessary to initiate the encoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In
this case, from_tf should be set to True and a configuration object should be provided as
config argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
decoder_pretrained_model_name_or_path (str, optional, defaults to None) —
Information necessary to initiate the decoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
A path or url to a tensorflow index checkpoint file (e.g, ./tf_model/model.ckpt.index). In
this case, from_tf should be set to True and a configuration object should be provided as
config argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args (remaining positional arguments, optional) —
All remaining positional arguments will be passed to the underlying model’s __init__ method.
kwargs (remaining dictionary of keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True).
To update the encoder configuration, use the prefix encoder_ for each configuration parameter.
To update the decoder configuration, use the prefix decoder_ for each configuration parameter.
To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a config is provided or automatically loaded.
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
The model is set in evaluation mode by default using model.eval() (Dropout modules are deactivated). To train
the model, you need to first set it back in training mode with model.train().
Example:
	Copied
 from transformers import EncoderDecoderModel
 # initialize a bert2bert from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
 model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
 # saving model after fine-tuning
 model.save_pretrained("./bert2bert")
 # load fine-tuned model
 model = EncoderDecoderModel.from_pretrained("./bert2bert")
TFEncoderDecoderModel
	
class transformers.TFEncoderDecoderModel
<
source
>
(
*args
**kwargs
)
Parameters 
config (EncoderDecoderConfig) — Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the from_pretrained() method to load the model weights.
This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
from_pretrained() function and the decoder is loaded via from_pretrained()
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like summarization.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models
(see the examples for more information).
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.
TFEncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture with one
of the base model classes of the library as encoder and another one as decoder when created with the
from_pretrained() class method for the encoder and from_pretrained() class
method for the decoder.
call
<
source
>
(
input_ids: TFModelInputType | None = None
attention_mask: np.ndarray | tf.Tensor | None = None
decoder_input_ids: np.ndarray | tf.Tensor | None = None
decoder_attention_mask: np.ndarray | tf.Tensor | None = None
encoder_outputs: np.ndarray | tf.Tensor | None = None
past_key_values: Tuple[Tuple[tf.Tensor]] | None = None
inputs_embeds: np.ndarray | tf.Tensor | None = None
decoder_inputs_embeds: np.ndarray | tf.Tensor | None = None
labels: np.ndarray | tf.Tensor | None = None
use_cache: Optional[bool] = None
output_attentions: Optional[bool] = None
output_hidden_states: Optional[bool] = None
return_dict: Optional[bool] = None
training: bool = False
**kwargs
)
→
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)
Parameters 
input_ids (np.ndarray, tf.Tensor, List[tf.Tensor] `Dict[str, tf.Tensor] or Dict[str, np.ndarray] and each example must have the shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
attention_mask (np.ndarray or 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 (np.ndarray or tf.Tensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
If past_key_values is used, optionally only the last decoder_input_ids have to be input (see
past_key_values).
Provide for sequence to sequence training to the decoder. Indices can be obtained using
PreTrainedTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for
details.
decoder_attention_mask (np.ndarray or tf.Tensor 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.
encoder_outputs (tuple(tuple(tf.Tensor), optional) —
This tuple must consist of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) is a tensor 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(tf.Tensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) —
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).
inputs_embeds (np.ndarray or tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.
decoder_inputs_embeds (np.ndarray or tf.Tensor 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. 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.
labels (np.ndarray or tf.Tensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss for the decoder. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size]
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) —
If set to True, the model will return a ~utils.Seq2SeqLMOutput instead of a plain tuple.
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).
kwargs (optional) — Remaining dictionary of keyword arguments. Keyword arguments come in two flavors:
Without a prefix which will be input as **encoder_kwargs for the encoder forward function.
With a decoder_ prefix which will be input as `**decoder_kwargs“ for the decoder forward function.
Returns
transformers.modeling_tf_outputs.TFSeq2SeqLMOutput or tuple(tf.Tensor)
A transformers.modeling_tf_outputs.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 (EncoderDecoderConfig) 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.
The TFEncoderDecoderModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import TFEncoderDecoderModel, BertTokenizer
 # initialize a bert2gpt2 from a pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized
 model = TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "gpt2")
 tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
 # forward
 input_ids = tokenizer.encode(
...     "Hello, my dog is cute", add_special_tokens=True, return_tensors="tf"
... )  # Batch size 1
 outputs = model(input_ids=input_ids, decoder_input_ids=input_ids)
 # training
 outputs = model(input_ids=input_ids, decoder_input_ids=input_ids, labels=input_ids)
 loss, logits = outputs.loss, outputs.logits
 # save and load from pretrained
 model.save_pretrained("bert2gpt2")
 model = TFEncoderDecoderModel.from_pretrained("bert2gpt2")
 # generation
 generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.bos_token_id)
from_encoder_decoder_pretrained
<
source
>
(
encoder_pretrained_model_name_or_path: str = None
decoder_pretrained_model_name_or_path: str = None
*model_args
**kwargs
)
Parameters 
encoder_pretrained_model_name_or_path (str, optional) —
Information necessary to initiate the encoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
A path or url to a pytorch index checkpoint file (e.g, ./pt_model/). In this case,
encoder_from_pt should be set to True.
decoder_pretrained_model_name_or_path (str, optional, defaults to None) —
Information necessary to initiate the decoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
A path or url to a pytorch checkpoint file (e.g, ./pt_model/). In this case,
decoder_from_pt should be set to True.
model_args (remaining positional arguments, optional) —
All remaning positional arguments will be passed to the underlying model’s __init__ method.
kwargs (remaining dictionary of keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True).
To update the encoder configuration, use the prefix encoder_ for each configuration parameter.
To update the decoder configuration, use the prefix decoder_ for each configuration parameter.
To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a config is provided or automatically loaded.
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Example:
	Copied
 from transformers import TFEncoderDecoderModel
 # initialize a bert2gpt2 from two pretrained BERT models. Note that the cross-attention layers will be randomly initialized
 model = TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "gpt2")
 # saving model after fine-tuning
 model.save_pretrained("./bert2gpt2")
 # load fine-tuned model
 model = TFEncoderDecoderModel.from_pretrained("./bert2gpt2")
FlaxEncoderDecoderModel
	
class transformers.FlaxEncoderDecoderModel
<
source
>
(
config: EncoderDecoderConfig
input_shape: typing.Optional[typing.Tuple] = None
seed: int = 0
dtype: dtype = <class 'jax.numpy.float32'>
_do_init: bool = True
**kwargs
)
Parameters 
config (EncoderDecoderConfig) — 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.
dtype (jax.numpy.dtype, optional, defaults to jax.numpy.float32) —
The data type of the computation. Can be one of jax.numpy.float32, jax.numpy.float16 (on GPUs) and
jax.numpy.bfloat16 (on TPUs).
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given dtype.
Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.
If you wish to change the dtype of the model parameters, see to_fp16() and
to_bf16().
This class can be used to initialize a sequence-to-sequence model with any pretrained autoencoding model as the
encoder and any pretrained autoregressive model as the decoder. The encoder is loaded via
from_pretrained() function and the decoder is loaded via from_pretrained()
function. Cross-attention layers are automatically added to the decoder and should be fine-tuned on a downstream
generative task, like summarization.
The effectiveness of initializing sequence-to-sequence models with pretrained checkpoints for sequence generation
tasks was shown in Leveraging Pre-trained Checkpoints for Sequence Generation
Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. Michael Matena, Yanqi
Zhou, Wei Li, Peter J. Liu.
After such an Encoder Decoder model has been trained/fine-tuned, it can be saved/loaded just like any other models
(see the examples for more information).
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.
FlaxEncoderDecoderModel is a generic model class that will be instantiated as a transformer architecture with
the module (flax.nn.Module) of one of the base model classes of the library as encoder module and another one as
decoder module when created with the :meth~transformers.FlaxAutoModel.from_pretrained class method for the
encoder and :meth~transformers.FlaxAutoModelForCausalLM.from_pretrained class method for the decoder.
__call__
<
source
>
(
input_ids: Array
attention_mask: typing.Optional[jax.Array] = None
decoder_input_ids: typing.Optional[jax.Array] = None
decoder_attention_mask: typing.Optional[jax.Array] = None
position_ids: typing.Optional[jax.Array] = None
decoder_position_ids: typing.Optional[jax.Array] = None
output_attentions: typing.Optional[bool] = None
output_hidden_states: typing.Optional[bool] = None
return_dict: typing.Optional[bool] = None
train: bool = False
params: dict = None
dropout_rng: PRNGKey = None
)
→
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
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 PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
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 PreTrainedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are decoder input IDs?
For sequence to sequence training, decoder_input_ids should be provided. decoder_input_ids should be
created outside of the model by shifting the labels to the right, replacing -100 by the pad_token_id
and prepending them with the decoder_start_token_id.
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.
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.encoder.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.decoder.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) —
If set to True, the model will return a ~utils.FlaxSeq2SeqLMOutput instead of a plain tuple.
Returns
transformers.modeling_flax_outputs.FlaxSeq2SeqLMOutput or tuple(torch.FloatTensor)
A transformers.modeling_flax_outputs.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 (EncoderDecoderConfig) and inputs.
logits (jnp.ndarray 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(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(jnp.ndarray) 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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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 (jnp.ndarray 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(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of jnp.ndarray (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(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of jnp.ndarray (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.
The FlaxEncoderDecoderModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.
Examples:
	Copied
 from transformers import FlaxEncoderDecoderModel, BertTokenizer, GPT2Tokenizer
 # load a fine-tuned bert2gpt2 model
 model = FlaxEncoderDecoderModel.from_pretrained("patrickvonplaten/bert2gpt2-cnn_dailymail-fp16")
 # load input & output tokenizer
 tokenizer_input = BertTokenizer.from_pretrained("bert-base-cased")
 tokenizer_output = GPT2Tokenizer.from_pretrained("gpt2")
 article = '''Sigma Alpha Epsilon is under fire for a video showing party-bound fraternity members
 singing a racist chant. SAE's national chapter suspended the students,
 but University of Oklahoma President David Boren took it a step further,
 saying the university's affiliation with the fraternity is permanently done.'''
 input_ids = tokenizer_input(article, add_special_tokens=True, return_tensors="np").input_ids
 # use GPT2's eos_token as the pad as well as eos token
 model.config.eos_token_id = model.config.decoder.eos_token_id
 model.config.pad_token_id = model.config.eos_token_id
 sequences = model.generate(input_ids, num_beams=4, max_length=12).sequences
 summary = tokenizer_output.batch_decode(sequences, skip_special_tokens=True)[0]
 assert summary == "SAS Alpha Epsilon suspended Sigma Alpha Epsilon members"
from_encoder_decoder_pretrained
<
source
>
(
encoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = None
decoder_pretrained_model_name_or_path: typing.Union[str, os.PathLike, NoneType] = None
*model_args
**kwargs
)
Parameters 
encoder_pretrained_model_name_or_path (Union[str, os.PathLike], optional) —
Information necessary to initiate the encoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
decoder_pretrained_model_name_or_path (Union[str, os.PathLike], optional, defaults to None) —
Information necessary to initiate the decoder. Can be either:
A string, the model id of a pretrained model hosted inside a model repo on huggingface.co.
Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased.
A path to a directory containing model weights saved using
save_pretrained(), e.g., ./my_model_directory/.
model_args (remaining positional arguments, optional) —
All remaning positional arguments will be passed to the underlying model’s __init__ method.
kwargs (remaining dictionary of keyword arguments, optional) —
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
output_attentions=True).
To update the encoder configuration, use the prefix encoder_ for each configuration parameter.
To update the decoder configuration, use the prefix decoder_ for each configuration parameter.
To update the parent model configuration, do not use a prefix for each configuration parameter.
Behaves differently depending on whether a config is provided or automatically loaded.
Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model
checkpoints.
Example:
	Copied
 from transformers import FlaxEncoderDecoderModel
 # initialize a bert2gpt2 from pretrained BERT and GPT2 models. Note that the cross-attention layers will be randomly initialized
 model = FlaxEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "gpt2")
 # saving model after fine-tuning
 model.save_pretrained("./bert2gpt2")
 # load fine-tuned model
 model = FlaxEncoderDecoderModel.from_pretrained("./bert2gpt2")
←ELECTRA
ERNIE→
Encoder Decoder Models
Overview
Randomly initializing `EncoderDecoderModel` from model configurations.
Initialising `EncoderDecoderModel` from a pretrained encoder and a pretrained decoder.
Loading an existing `EncoderDecoderModel` checkpoint and perform inference.
Loading a PyTorch checkpoint into `TFEncoderDecoderModel`.
Training
EncoderDecoderConfig
EncoderDecoderModel
TFEncoderDecoderModel
FlaxEncoderDecoderModel
