Encoder Decoder Models¶
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.
EncoderDecoderConfig¶
-
class
transformers.
EncoderDecoderConfig
(**kwargs)[source]¶ EncoderDecoderConfig
is the configuration class to store the configuration of aEncoderDecoderModel
. 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 fromPretrainedConfig
for more information.- 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.
Examples:
>>> 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)
-
classmethod
from_encoder_decoder_configs
(encoder_config: transformers.configuration_utils.PretrainedConfig, decoder_config: transformers.configuration_utils.PretrainedConfig, **kwargs) → transformers.configuration_utils.PretrainedConfig[source]¶ Instantiate a
EncoderDecoderConfig
(or a derived class) from a pre-trained encoder model configuration and decoder model configuration.- Returns
An instance of a configuration object
- Return type
EncoderDecoderModel¶
-
class
transformers.
EncoderDecoderModel
(config: Optional[transformers.configuration_utils.PretrainedConfig] = None, encoder: Optional[transformers.modeling_utils.PreTrainedModel] = None, decoder: Optional[transformers.modeling_utils.PreTrainedModel] = None)[source]¶ 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 viafrom_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.
- 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 thefrom_pretrained()
method to load the model weights.
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
(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_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, **kwargs)[source]¶ The
EncoderDecoderModel
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.
Indices can be obtained using
PreTrainedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.If
past_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).Provide for sequence to sequence training to the decoder. Indices can be obtained using
PreTrainedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.decoder_attention_mask (
torch.BoolTensor
of shape(batch_size, target_sequence_length)
, optional) – Default behavior: generate a tensor that ignores pad tokens indecoder_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 lengthconfig.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 lastdecoder_input_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of alldecoder_input_ids
of shape(batch_size, sequence_length)
.inputs_embeds (
torch.FloatTensor
of shape(batch_size, sequence_length, hidden_size)
, optional) – Optionally, instead of passinginput_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertinput_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 passingdecoder_input_ids
you can choose to directly pass an embedded representation. This is useful if you want more control over how to convertdecoder_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]
(seeinput_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 toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
).output_attentions (
bool
, optional) – Whether or not to return the attentions tensors of all attention layers. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – If set toTrue
, the model will return aSeq2SeqLMOutput
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
A
Seq2SeqLMOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (EncoderDecoderConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) – Tuple oftuple(torch.FloatTensor)
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftorch.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftorch.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftorch.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.
Examples:
>>> 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 >>> # forward >>> input_ids = torch.tensor(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True)).unsqueeze(0) # 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("bert2bert") >>> model = EncoderDecoderModel.from_pretrained("bert2bert") >>> # generation >>> generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
- Return type
Seq2SeqLMOutput
ortuple(torch.FloatTensor)
-
classmethod
from_encoder_decoder_pretrained
(encoder_pretrained_model_name_or_path: str = None, decoder_pretrained_model_name_or_path: str = None, *model_args, **kwargs) → transformers.modeling_utils.PreTrainedModel[source]¶ 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 withmodel.train()
.- Params:
- encoder_pretrained_model_name_or_path (:obj: 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, likedbmdz/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 toTrue
and a configuration object should be provided asconfig
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 (:obj: 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, likedbmdz/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 toTrue
and a configuration object should be provided asconfig
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.
Example:
>>> 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")
FlaxEncoderDecoderModel¶
-
class
transformers.
FlaxEncoderDecoderModel
(config: transformers.models.encoder_decoder.configuration_encoder_decoder.EncoderDecoderConfig, input_shape: Optional[Tuple] = None, seed: int = 0, dtype: numpy.dtype = <class 'jax._src.numpy.lax_numpy.float32'>, **kwargs)[source]¶ 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 viafrom_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.
- 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 thefrom_pretrained()
method to load the model weights.
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__
(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)[source]¶ The
FlaxEncoderDecoderModel
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
PreTrainedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.For sequence to sequence training,
decoder_input_ids
should be provided. If nodecoder_input_ids
is provided, the model will create this tensor by shifting theinput_ids
to the right for denoising pre-training.decoder_attention_mask (
jnp.ndarray
of shape(batch_size, target_sequence_length)
, optional) – Default behavior: generate a tensor that ignores pad tokens indecoder_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. Seeattentions
under returned tensors for more detail.output_hidden_states (
bool
, optional) – Whether or not to return the hidden states of all layers. Seehidden_states
under returned tensors for more detail.return_dict (
bool
, optional) – If set toTrue
, the model will return aFlaxSeq2SeqLMOutput
instead of a plain tuple.
- Returns
A
FlaxSeq2SeqLMOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) – Tuple oftuple(jnp.ndarray)
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple ofjnp.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple ofjnp.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple ofjnp.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple ofjnp.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple ofjnp.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.
Examples:
>>> 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"
- Return type
FlaxSeq2SeqLMOutput
ortuple(torch.FloatTensor)
-
classmethod
from_encoder_decoder_pretrained
(encoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, decoder_pretrained_model_name_or_path: Optional[Union[str, os.PathLike]] = None, *model_args, **kwargs) → transformers.modeling_flax_utils.FlaxPreTrainedModel[source]¶ Instantiate an encoder and a decoder from one or two base classes of the library from pretrained model checkpoints.
- Params:
- encoder_pretrained_model_name_or_path (:obj: 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, likedbmdz/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 (:obj: 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, likedbmdz/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.
Example:
>>> 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")