LED¶
Overview¶
The LED model was proposed in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
The abstract from the paper is the following:
Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset.
Tips:
LEDForConditionalGeneration
is an extension ofBartForConditionalGeneration
exchanging the traditional self-attention layer with Longformer’s chunked self-attention layer.LEDTokenizer
is an alias ofBartTokenizer
.LED works very well on long-range sequence-to-sequence tasks where the
input_ids
largely exceed a length of 1024 tokens.LED pads the
input_ids
to be a multiple ofconfig.attention_window
if required. Therefore a small speed-up is gained, whenLEDTokenizer
is used with thepad_to_multiple_of
argument.LED makes use of global attention by means of the
global_attention_mask
(seeLongformerModel
). For summarization, it is advised to put global attention only on the first<s>
token. For question answering, it is advised to put global attention on all tokens of the question.To fine-tune LED on all 16384, it is necessary to enable gradient checkpointing by setting
config.gradient_checkpointing = True
.A notebook showing how to evaluate LED, can be accessed here.
A notebook showing how to fine-tune LED, can be accessed here.
This model was contributed by patrickvonplaten.
LEDConfig¶
-
class
transformers.
LEDConfig
(vocab_size=50265, max_encoder_position_embeddings=16384, max_decoder_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, decoder_start_token_id=2, classifier_dropout=0.0, pad_token_id=1, bos_token_id=0, eos_token_id=2, gradient_checkpointing=False, attention_window: Union[List[int], int] = 512, **kwargs)[source]¶ This is the configuration class to store the configuration of a
LEDModel
. It is used to instantiate an LED 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 LED allenai/led-base-16384 architecture.Configuration objects inherit from
PretrainedConfig
and can be used to control the model outputs. Read the documentation fromPretrainedConfig
for more information.- Parameters
vocab_size (
int
, optional, defaults to 50265) – Vocabulary size of the LED model. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingLEDModel
orTFLEDModel
.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
orfunction
, 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_encoder_position_embeddings (
int
, optional, defaults to 16384) – The maximum sequence length that the encoder might ever be used with.max_decoder_position_embeddings (
int
, optional, defaults to 16384) – The maximum sequence length that the decoder might ever be used with.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.use_cache (
bool
, optional, defaults toTrue
) – Whether or not the model should return the last key/values attentions (not used by all models)gradient_checkpointing (
bool
, optional, defaults toFalse
) – If True, use gradient checkpointing to save memory at the expense of slower backward pass.Example:: –
from transformers import LEDModel (>>>) –
LEDConfig –
# Initializing a LED allenai/led-base-16384 style configuration (>>>) –
configuration = LEDConfig() (>>>) –
# Initializing a model from the allenai/led-base-16384 style configuration (>>>) –
model = LEDModel (>>>) –
# Accessing the model configuration (>>>) –
configuration = model.config (>>>) –
LEDTokenizer¶
-
class
transformers.
LEDTokenizer
(vocab_file, merges_file, errors='replace', bos_token='<s>', eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', add_prefix_space=False, **kwargs)[source]¶ Construct a LED tokenizer.
LEDTokenizer
is identical toBartTokenizer
and runs end-to-end tokenization: punctuation splitting and wordpiece.Refer to superclass
BartTokenizer
for usage examples and documentation concerning parameters.-
build_inputs_with_special_tokens
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int]¶ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format:
single sequence:
<s> X </s>
pair of sequences:
<s> A </s></s> B </s>
- 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]
-
create_token_type_ids_from_sequences
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) → List[int]¶ Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned.
- Parameters
token_ids_0 (
List[int]
) – List of IDs.token_ids_1 (
List[int]
, optional) – Optional second list of IDs for sequence pairs.
- Returns
List of zeros.
- Return type
List[int]
-
get_special_tokens_mask
(token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False) → List[int]¶ 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 toFalse
) – 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]
-
save_vocabulary
(save_directory: str, filename_prefix: Optional[str] = None) → Tuple[str]¶ 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)
-
LEDTokenizerFast¶
-
class
transformers.
LEDTokenizerFast
(vocab_file, merges_file, tokenizer_file=None, errors='replace', bos_token='<s>', eos_token='</s>', sep_token='</s>', cls_token='<s>', unk_token='<unk>', pad_token='<pad>', mask_token='<mask>', add_prefix_space=False, **kwargs)[source]¶ Construct a “fast” LED tokenizer (backed by HuggingFace’s tokenizers library).
LEDTokenizerFast
is identical toBartTokenizerFast
and runs end-to-end tokenization: punctuation splitting and wordpiece.Refer to superclass
BartTokenizerFast
for usage examples and documentation concerning parameters.-
slow_tokenizer_class
¶ alias of
transformers.models.led.tokenization_led.LEDTokenizer
-
LED specific outputs¶
-
class
transformers.models.led.modeling_led.
LEDEncoderBaseModelOutput
(last_hidden_state: torch.FloatTensor, hidden_states: Optional[Tuple[torch.FloatTensor]] = None, attentions: Optional[Tuple[torch.FloatTensor]] = None, global_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for LEDEncoder’s outputs, with potential hidden states, local and global attentions.
- Parameters
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 model.hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x + attention_window + 1)
, wherex
is the number of tokens with global attention mask.Local attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token in the sequence to every token with global attention (first
x
values) and to every token in the attention window (remainingattention_window + 1
values). Note that the firstx
values refer to tokens with fixed positions in the text, but the remainingattention_window + 1
values refer to tokens with relative positions: the attention weight of a token to itself is located at indexx + attention_window / 2
and theattention_window / 2
preceding (succeeding) values are the attention weights to theattention_window / 2
preceding (succeeding) tokens. If the attention window contains a token with global attention, the attention weight at the corresponding index is set to 0; the value should be accessed from the firstx
attention weights. If a token has global attention, the attention weights to all other tokens inattentions
is set to 0, the values should be accessed fromglobal_attentions
.global_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_led.
LEDSeq2SeqModelOutput
(last_hidden_state: torch.FloatTensor = None, past_key_values: Optional[List[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential decoding.
- Parameters
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 (
List[torch.FloatTensor]
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
torch.FloatTensor
of lengthconfig.n_layers
, with each tensor of shape(2, batch_size, num_heads, sequence_length, embed_size_per_head)
).Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see
past_key_values
input) to speed up sequential decoding.decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_led.
LEDSeq2SeqLMOutput
(loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, past_key_values: Optional[List[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for sequence-to-sequence language models outputs.
- Parameters
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 (
List[torch.FloatTensor]
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
torch.FloatTensor
of lengthconfig.n_layers
, with each tensor of shape(2, batch_size, num_heads, sequence_length, embed_size_per_head)
).Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see
past_key_values
input) to speed up sequential decoding.decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_led.
LEDSeq2SeqSequenceClassifierOutput
(loss: Optional[torch.FloatTensor] = None, logits: torch.FloatTensor = None, past_key_values: Optional[List[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for outputs of sequence-to-sequence sentence classification models.
- Parameters
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabel
is provided) – Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensor
of shape(batch_size, config.num_labels)
) – Classification (or regression if config.num_labels==1) scores (before SoftMax).past_key_values (
List[torch.FloatTensor]
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
torch.FloatTensor
of lengthconfig.n_layers
, with each tensor of shape(2, batch_size, num_heads, sequence_length, embed_size_per_head)
).Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see
past_key_values
input) to speed up sequential decoding.decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_led.
LEDSeq2SeqQuestionAnsweringModelOutput
(loss: Optional[torch.FloatTensor] = None, start_logits: torch.FloatTensor = None, end_logits: torch.FloatTensor = None, past_key_values: Optional[List[torch.FloatTensor]] = None, decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, cross_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_last_hidden_state: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None, encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None, encoder_global_attentions: Optional[Tuple[torch.FloatTensor]] = None)[source]¶ Base class for outputs of sequence-to-sequence question answering models.
- Parameters
loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
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 (
List[torch.FloatTensor]
, optional, returned whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
torch.FloatTensor
of lengthconfig.n_layers
, with each tensor of shape(2, batch_size, num_heads, sequence_length, embed_size_per_head)
).Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be used (see
past_key_values
input) to speed up sequential decoding.decoder_hidden_states (
tuple(torch.FloatTensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(torch.FloatTensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
torch.FloatTensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_tf_led.
TFLEDEncoderBaseModelOutput
(last_hidden_state: tensorflow.python.framework.ops.Tensor = None, hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, global_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None)[source]¶ Base class for Longformer’s outputs, with potential hidden states, local and global attentions.
- Parameters
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 model.hidden_states (
tuple(tf.Tensor)
, optional, returned whenoutput_hidden_states=True
is passed or whenconfig.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 model at the output of each layer plus the initial embedding outputs.
attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x + attention_window + 1)
, wherex
is the number of tokens with global attention mask.Local attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token in the sequence to every token with global attention (first
x
values) and to every token in the attention window (remainingattention_window + 1
values). Note that the firstx
values refer to tokens with fixed positions in the text, but the remainingattention_window + 1
values refer to tokens with relative positions: the attention weight of a token to itself is located at indexx + attention_window / 2
and theattention_window / 2
preceding (succeeding) values are the attention weights to theattention_window / 2
preceding (succeeding) tokens. If the attention window contains a token with global attention, the attention weight at the corresponding index is set to 0; the value should be accessed from the firstx
attention weights. If a token has global attention, the attention weights to all other tokens inattentions
is set to 0, the values should be accessed fromglobal_attentions
.global_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_tf_led.
TFLEDSeq2SeqModelOutput
(last_hidden_state: tensorflow.python.framework.ops.Tensor = None, past_key_values: Optional[List[tensorflow.python.framework.ops.Tensor]] = None, decoder_hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, decoder_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, cross_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_last_hidden_state: Optional[tensorflow.python.framework.ops.Tensor] = None, encoder_hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_global_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None)[source]¶ Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential decoding.
- Parameters
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
tf.Tensor
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
-
class
transformers.models.led.modeling_tf_led.
TFLEDSeq2SeqLMOutput
(loss: Optional[tensorflow.python.framework.ops.Tensor] = None, logits: tensorflow.python.framework.ops.Tensor = None, past_key_values: Optional[List[tensorflow.python.framework.ops.Tensor]] = None, decoder_hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, decoder_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, cross_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_last_hidden_state: Optional[tensorflow.python.framework.ops.Tensor] = None, encoder_hidden_states: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None, encoder_global_attentions: Optional[Tuple[tensorflow.python.framework.ops.Tensor]] = None)[source]¶ Base class for sequence-to-sequence language models outputs.
- Parameters
loss (
tf.Tensor
of shape(1,)
, optional, returned whenlabels
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –List of
tf.Tensor
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.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 whenoutput_attentions=True
is passed or whenconfig.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.
encoder_global_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) –Tuple of
tf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
LEDModel¶
-
class
transformers.
LEDModel
(config: transformers.models.led.configuration_led.LEDConfig)[source]¶ The bare LED 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 (
LEDConfig
) – 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.
-
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, global_attention_mask=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
LEDModel
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
LEDTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.If you want to change padding behavior, you should read
modeling_led._prepare_decoder_inputs()
and modify to your needs. See diagram 1 in the paper for more information on the default strategy.global_attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) –Mask to decide the attention given on each token, local attention or global attention for the encoder. Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for task-specific finetuning because it makes the model more flexible at representing the task. For example, for classification, the <s> token should be given global attention. For QA, all question tokens should also have global attention. Please refer to the Longformer paper for more details. Mask values selected in
[0, 1]
:0 for local attention (a sliding window attention),
1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –Tuple of
tuple(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.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 passing
decoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). 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.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
.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) – Whether or not to return aModelOutput
instead of a plain tuple.
- Returns
A
Seq2SeqModelOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (LEDConfig
) 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 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.
- Return type
Seq2SeqModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import LEDTokenizer, LEDModel >>> import torch >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384') >>> model = LEDModel.from_pretrained('allenai/led-base-16384') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state
LEDForConditionalGeneration¶
-
class
transformers.
LEDForConditionalGeneration
(config: transformers.models.led.configuration_led.LEDConfig)[source]¶ The LED 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 (
LEDConfig
) – 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.
-
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, global_attention_mask=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
LEDForConditionalGeneration
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
LEDTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.If you want to change padding behavior, you should read
modeling_led._prepare_decoder_inputs()
and modify to your needs. See diagram 1 in the paper for more information on the default strategy.global_attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) –Mask to decide the attention given on each token, local attention or global attention for the encoder. Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for task-specific finetuning because it makes the model more flexible at representing the task. For example, for classification, the <s> token should be given global attention. For QA, all question tokens should also have global attention. Please refer to the Longformer paper for more details. Mask values selected in
[0, 1]
:0 for local attention (a sliding window attention),
1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –Tuple of
tuple(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.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 passing
decoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). 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.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
.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) – Whether or not to return aModelOutput
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 (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]
.
- 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 (LEDConfig
) 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.
Conditional generation example:
>>> from transformers import LEDTokenizer, LEDForConditionalGeneration >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384') >>> TXT = "My friends are <mask> but they eat too many carbs." >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384') >>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids'] >>> prediction = model.generate(input_ids)[0] >>> print(tokenizer.decode(prediction, skip_special_tokens=True))
- Return type
Seq2SeqLMOutput
ortuple(torch.FloatTensor)
Summarization example:
>>> import torch >>> from transformers import LEDTokenizer, LEDForConditionalGeneration >>> model = LEDForConditionalGeneration.from_pretrained('allenai/led-large-16384-arxiv') >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-large-16384-arxiv') >>> ARTICLE_TO_SUMMARIZE = '''Transformers (Vaswani et al., 2017) have achieved state-of-the-art ... results in a wide range of natural language tasks including generative ... language modeling (Dai et al., 2019; Radford et al., 2019) and discriminative ... language understanding (Devlin et al., 2019). This success is partly due to ... the self-attention component which enables the network to capture contextual ... information from the entire sequence. While powerful, the memory and computational ... requirements of self-attention grow quadratically with sequence length, making ... it infeasible (or very expensive) to process long sequences. ... ... To address this limitation, we present Longformer, a modified Transformer ... architecture with a self-attention operation that scales linearly with the ... sequence length, making it versatile for processing long documents (Fig 1). This ... is an advantage for natural language tasks such as long document classification, ... question answering (QA), and coreference resolution, where existing approaches ... partition or shorten the long context into smaller sequences that fall within the ... typical 512 token limit of BERT-style pretrained models. Such partitioning could ... potentially result in loss of important cross-partition information, and to ... mitigate this problem, existing methods often rely on complex architectures to ... address such interactions. On the other hand, our proposed Longformer is able to ... build contextual representations of the entire context using multiple layers of ... attention, reducing the need for task-specific architectures.''' >>> inputs = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt') >>> # Global attention on the first token (cf. Beltagy et al. 2020) >>> global_attention_mask = torch.zeros_like(inputs) >>> global_attention_mask[:, 0] = 1 >>> # Generate Summary >>> summary_ids = model.generate(inputs, global_attention_mask=global_attention_mask, ... num_beams=3, max_length=32, early_stopping=True) >>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
LEDForSequenceClassification¶
-
class
transformers.
LEDForSequenceClassification
(config: transformers.models.led.configuration_led.LEDConfig, **kwargs)[source]¶ LED 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 (
LEDConfig
) – 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.
-
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, global_attention_mask=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
LEDForSequenceClassification
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
LEDTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.If you want to change padding behavior, you should read
modeling_led._prepare_decoder_inputs()
and modify to your needs. See diagram 1 in the paper for more information on the default strategy.global_attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) –Mask to decide the attention given on each token, local attention or global attention for the encoder. Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for task-specific finetuning because it makes the model more flexible at representing the task. For example, for classification, the <s> token should be given global attention. For QA, all question tokens should also have global attention. Please refer to the Longformer paper for more details. Mask values selected in
[0, 1]
:0 for local attention (a sliding window attention),
1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –Tuple of
tuple(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.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 passing
decoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). 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.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
.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) – Whether or not to return aModelOutput
instead of a plain tuple.labels (
torch.LongTensor
of shape(batch_size,)
, optional) – Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., config.num_labels - 1]
. Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
- Returns
A
Seq2SeqSequenceClassifierOutput
or a tuple oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (LEDConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabel
is provided) – Classification (or regression if config.num_labels==1) loss.logits (
torch.FloatTensor
of shape(batch_size, config.num_labels)
) – Classification (or regression if config.num_labels==1) scores (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.
- Return type
Seq2SeqSequenceClassifierOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import LEDTokenizer, LEDForSequenceClassification >>> import torch >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384') >>> model = LEDForSequenceClassification.from_pretrained('allenai/led-base-16384') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> labels = torch.tensor([1]).unsqueeze(0) # Batch size 1 >>> outputs = model(**inputs, labels=labels) >>> loss = outputs.loss >>> logits = outputs.logits
LEDForQuestionAnswering¶
-
class
transformers.
LEDForQuestionAnswering
(config)[source]¶ LED 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 (
LEDConfig
) – 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.
-
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, global_attention_mask=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
LEDForQuestionAnswering
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
LEDTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
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
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.If you want to change padding behavior, you should read
modeling_led._prepare_decoder_inputs()
and modify to your needs. See diagram 1 in the paper for more information on the default strategy.global_attention_mask (
torch.FloatTensor
of shape(batch_size, sequence_length)
, optional) –Mask to decide the attention given on each token, local attention or global attention for the encoder. Tokens with global attention attends to all other tokens, and all other tokens attend to them. This is important for task-specific finetuning because it makes the model more flexible at representing the task. For example, for classification, the <s> token should be given global attention. For QA, all question tokens should also have global attention. Please refer to the Longformer paper for more details. Mask values selected in
[0, 1]
:0 for local attention (a sliding window attention),
1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) –Tuple of
tuple(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.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 passing
decoder_input_ids
you can choose to directly pass an embedded representation. Ifpast_key_values
is used, optionally only the lastdecoder_inputs_embeds
have to be input (seepast_key_values
). 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.If
decoder_input_ids
anddecoder_inputs_embeds
are both unset,decoder_inputs_embeds
takes the value ofinputs_embeds
.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) – Whether or not to return aModelOutput
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 oftorch.FloatTensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (LEDConfig
) and inputs.loss (
torch.FloatTensor
of shape(1,)
, optional, returned whenlabels
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 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.
- Return type
Seq2SeqQuestionAnsweringModelOutput
ortuple(torch.FloatTensor)
Example:
>>> from transformers import LEDTokenizer, LEDForQuestionAnswering >>> import torch >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384') >>> model = LEDForQuestionAnswering.from_pretrained('allenai/led-base-16384') >>> 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
TFLEDModel¶
-
class
transformers.
TFLEDModel
(*args, **kwargs)[source]¶ The bare LED 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])
ormodel([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 (
LEDConfig
) – 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.
-
call
(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs: Optional[Union[Tuple, transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput]] = None, global_attention_mask=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
TFLEDModel
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
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
decoder_input_ids (
tf.LongTensor
of shape(batch_size, target_sequence_length)
, optional) –Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.
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 ofpast_key_values (
Tuple[Tuple[tf.Tensor]]
of lengthconfig.n_layers
) – contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. Ifpast_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)
.use_cache (
bool
, optional, defaults toTrue
) – If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
). Set toFalse
during training,True
during generationoutput_attentions (
bool
, optional) – Whether or not to return the attentions tensors of all attention layers. Seeattentions
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. Seehidden_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 aModelOutput
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 toFalse
) – Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
- Returns
A
TFLEDSeq2SeqModelOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (LEDConfig
) 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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) – List oftf.Tensor
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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.
encoder_global_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
- Return type
TFLEDSeq2SeqModelOutput
ortuple(tf.Tensor)
Example:
>>> from transformers import LEDTokenizer, TFLEDModel >>> import tensorflow as tf >>> tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384') >>> model = TFLEDModel.from_pretrained('allenai/led-base-16384') >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf") >>> outputs = model(inputs) >>> last_hidden_states = outputs.last_hidden_state
TFLEDForConditionalGeneration¶
-
class
transformers.
TFLEDForConditionalGeneration
(*args, **kwargs)[source]¶ The LED 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])
ormodel([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 (
LEDConfig
) – 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.
-
call
(input_ids=None, attention_mask=None, decoder_input_ids=None, decoder_attention_mask=None, head_mask=None, decoder_head_mask=None, encoder_outputs: Optional[transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput] = None, global_attention_mask=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
TFLEDForConditionalGeneration
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
BertTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.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.
decoder_input_ids (
tf.LongTensor
of shape(batch_size, target_sequence_length)
, optional) –Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using
LedTokenizer
. Seetransformers.PreTrainedTokenizer.encode()
andtransformers.PreTrainedTokenizer.__call__()
for details.LED uses the
eos_token_id
as the starting token fordecoder_input_ids
generation. Ifpast_key_values
is used, optionally only the lastdecoder_input_ids
have to be input (seepast_key_values
).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.
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 ofpast_key_values (
Tuple[Tuple[tf.Tensor]]
of lengthconfig.n_layers
) – contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. Ifpast_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)
.use_cache (
bool
, optional, defaults toTrue
) – If set toTrue
,past_key_values
key value states are returned and can be used to speed up decoding (seepast_key_values
). Set toFalse
during training,True
during generationoutput_attentions (
bool
, optional) – Whether or not to return the attentions tensors of all attention layers. Seeattentions
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. Seehidden_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 aModelOutput
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 toFalse
) – Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation).
- Returns
A
TFLEDSeq2SeqLMOutput
or a tuple oftf.Tensor
(ifreturn_dict=False
is passed or whenconfig.return_dict=False
) comprising various elements depending on the configuration (LEDConfig
) and inputs.loss (
tf.Tensor
of shape(1,)
, optional, returned whenlabels
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 whenuse_cache=True
is passed or whenconfig.use_cache=True
) – List oftf.Tensor
of lengthconfig.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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 whenoutput_hidden_states=True
is passed or whenconfig.output_hidden_states=True
) – Tuple oftf.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 whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.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.
encoder_global_attentions (
tuple(tf.Tensor)
, optional, returned whenoutput_attentions=True
is passed or whenconfig.output_attentions=True
) – Tuple oftf.Tensor
(one for each layer) of shape(batch_size, num_heads, sequence_length, x)
, wherex
is the number of tokens with global attention mask.Global attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Those are the attention weights from every token with global attention to every token in the sequence.
Examples:
>>> from transformers import LEDTokenizer, TFLEDForConditionalGeneration >>> import tensorflow as tf >>> mname = 'allenai/led-base-16384' >>> tokenizer = LEDTokenizer.from_pretrained(mname) >>> TXT = "My friends are <mask> but they eat too many carbs." >>> model = TFLEDForConditionalGeneration.from_pretrained(mname) >>> batch = tokenizer([TXT], return_tensors='tf') >>> logits = model(inputs=batch.input_ids).logits >>> probs = tf.nn.softmax(logits[0]) >>> # probs[5] is associated with the mask token
- Return type
TFLEDSeq2SeqLMOutput
ortuple(tf.Tensor)