BioGptForSequenceClassification

AutoTokenizer.from_pretrained("microsoft/biogpt")
>>> model = BioGptForSequenceClassification.from_pretrained("microsoft/biogpt", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = BioGptForSequenceClassification.from_pretrained(
...     "microsoft/biogpt", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).los


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


Overview

 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 of
BartForConditionalGeneration exchanging the traditional self-attention layer with
Longformer’s chunked self-attention layer. LEDTokenizer is an alias of
BartTokenizer. 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 of config.attention_window if required. Therefore a small speed-up is
gained, when LEDTokenizer i


Overview

s used with the pad_to_multiple_of argument. LED makes use of global attention by means of the global_attention_mask (see
LongformerModel). 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, gradient checkpointing can be enabled in case training leads to out-of-memory (OOM)
errors. This can be done by executing model.gradient_checkpointing_enable().
Moreover, the use_cache=False
flag can be used to disable the caching mechanism to save memory. A notebook showing how to evaluate LED, can be accessed here. A notebook showing how to fine-tune LED, can be accessed here. LED is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
the left.
This model was contributed by patrickvonplaten


Documentation resources

Text classification task guide Question answering task guide Translation task guide Summarization task guide


LEDConfig

class transformers.LEDConfig  < source > ( 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 attention_window: typing.Union[typing.List[int], int] = 512 **kwargs  )   Parameters   vocab_size (int, optional, defaults to 50265) —
Vocabulary size of the LED model. Defines the number of different tokens that can be represented


LEDConfig

 by the
inputs_ids passed when calling LEDModel or TFLEDModel.   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 f


LEDConfig

eed-forward) layer in decoder.   activation_function (str or function, optional, defaults to "gelu") —
The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu",
"relu", "silu" and "gelu_new" are supported.   dropout (float, optional, defaults to 0.1) —
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.   attention_dropout (float, optional, defaults to 0.0) —
The dropout ratio for the attention probabilities.   activation_dropout (float, optional, defaults to 0.0) —
The dropout ratio for activations inside the fully connected layer.   classifier_dropout (float, optional, defaults to 0.0) —
The dropout ratio for classifier.   max_encoder_position_embeddings (int, optional, defaults to 16384) —
The maximum sequence length that the encoder


LEDConfig

 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](see https://arxiv.org/abs/1909.11556)
for more details.   decoder_layerdrop (float, optional, defaults to 0.0) —
The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
for more details.   use_cache (bool, optional, defaults to True) —
Whether or not the model should return the last key/values attentions (not used by all models)    This is t


LEDConfig

he 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 from PretrainedConfig for more information.  Example:   Copied >>> 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(configuration)
>>> # Accessing the model configuration
>>> configuration = model.confi


LEDTokenizer

class transformers.LEDTokenizer  < source > ( 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  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   bos_token (str, optional, defaults to "<s>") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the cls_token.
   eos_token (str, optional, defaults to "</s>") —
The end of sequence token.
When building a seque


LEDTokenizer

nce using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token.
   sep_token (str, optional, defaults to "</s>") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   cls_token (str, optional, defaults to "<s>") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   unk_token (str, optional, defaults to "<unk>") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   pad_token (str, optional, defaults to "


LEDTokenizer

<pad>") —
The token used for padding, for example when batching sequences of different lengths.   mask_token (str, optional, defaults to "<mask>") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (BART tokenizer detect beginning of words by the preceding space).    Constructs a LED tokenizer, which is smilar to the ROBERTa tokenizer, using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will  be encoded differently whether it is at the beginning of the sentence (without space) or not:   Copied >>> from transformers im


LEDTokenizer

port LEDTokenizer
>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2] You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one). This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.  build_inputs_with_special_tokens  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  Parameters   token_ids_0 (List[int]) —


LEDTokenizer

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[int]

List of input IDs with the appropriate special tokens.
  Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A LED sequence has the following format: single sequence: <s> X </s> pair of sequences: <s> A </s></s> B </s>  get_special_tokens_mask  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False  ) → List[int]  Parameters   token_ids_0 (List[int]) —
List of IDs.   token_ids_1 (List[int], optional) —
Optional second list of IDs for sequence pairs.   already_has_special_tokens (bool, optional, defaults to False) —
Whether or not the token list is already formatted with sp


LEDTokenizer

ecial tokens for the model.   Returns
List[int]

A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
  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.  create_token_type_ids_from_sequences  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  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[int]

List of zeros.
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not
make use of token type ids, therefore a list of zeros is returned.  save_vocabulary  < source > ( save_directory: str filename_prefix: typing.Optional[str] = Non


LEDTokenizerFast

class transformers.LEDTokenizerFast  < source > ( vocab_file = None merges_file = None 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 trim_offsets = True **kwargs  )   Parameters   vocab_file (str) —
Path to the vocabulary file.   merges_file (str) —
Path to the merges file.   errors (str, optional, defaults to "replace") —
Paradigm to follow when decoding bytes to UTF-8. See
bytes.decode for more information.   bos_token (str, optional, defaults to "<s>") —
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The


LEDTokenizerFast

 token used is the cls_token.
   eos_token (str, optional, defaults to "</s>") —
The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the sep_token.
   sep_token (str, optional, defaults to "</s>") —
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.   cls_token (str, optional, defaults to "<s>") —
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.   unk_token (str,


LEDTokenizerFast

 optional, defaults to "<unk>") —
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.   pad_token (str, optional, defaults to "<pad>") —
The token used for padding, for example when batching sequences of different lengths.   mask_token (str, optional, defaults to "<mask>") —
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.   add_prefix_space (bool, optional, defaults to False) —
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (LED tokenizer detect beginning of words by the preceding space).   trim_offsets (bool, optional, defaults to True) —
Whether the post processing step sh


LEDTokenizerFast

ould trim offsets to avoid including whitespaces.    Construct a “fast” LED tokenizer (backed by HuggingFace’s tokenizers library), derived from the GPT-2 tokenizer,
using byte-level Byte-Pair-Encoding. This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will  be encoded differently whether it is at the beginning of the sentence (without space) or not:   Copied >>> from transformers import LEDTokenizerFast
>>> tokenizer = LEDTokenizerFast.from_pretrained("allenai/led-base-16384")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2] You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you
call it on some text, but since the model was not pretrained th


LEDTokenizerFast

is way, it might yield a decrease in performance. When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True. This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.  create_token_type_ids_from_sequences  < source > ( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None  ) → List[int]  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[int]

List of zeros.
  Create a mask from the two sequences passed to be used in a sequence-pair classification task. LED does not
make use of token type ids, therefore a list of zeros is returned


LED specific outputs

class transformers.models.led.modeling_led.LEDEncoderBaseModelOutput  < source > ( last_hidden_state: FloatTensor hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None global_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   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 when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.   attentions (tuple(torch.FloatTensor), optional, returned when output_at


LED specific outputs

tentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x + attention_window + 1), where x 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 (remaining `attention_window
1values). Note that the firstxvalues refer to tokens with fixed positions in the text, but the remainingattention_window + 1values refer to tokens with relative positions: the attention weight of a token to itself is located at indexx + attention_window / 2and theattention_window / 2preceding (succeeding) values are the attention weights to theattention_window / 2preceding (succeeding) tokens. If the attention


LED specific outputs

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 firstxattention weights. If a token has global attention, the attention weights to all other tokens inattentionsis set to 0, the values should be accessed fromglobal_attentions`.
   global_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x),
where x 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.    Base class for LEDEncoder’s outputs, with potential hidden states, local and global attentions.
 class transformers


LED specific outputs

.models.led.modeling_led.LEDSeq2SeqModelOutput  < source > ( last_hidden_state: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_global_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   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


LED specific outputs

of shape (batch_size, 1, hidden_size) is output.   past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.   decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_att


LED specific outputs

entions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(torch.FloatTensor),


LED specific outputs

optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.   encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   encoder_global_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads


LED specific outputs

, sequence_length, x),
where x 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.    Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
 class transformers.models.led.modeling_led.LEDSeq2SeqLMOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None logits: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_st


LED specific outputs

ates: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_global_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Language modeling loss.   logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decodin


LED specific outputs

g.   decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one fo


LED specific outputs

r each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.   encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions


LED specific outputs

=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 when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x),
where x 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.    Base class for sequence-to-sequence language models outputs.
 class transformers.models.led.modeling_led.LEDSeq2SeqSequenceClassifierOutput  < source > ( loss: typing.Optional[to


LED specific outputs

rch.FloatTensor] = None logits: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_global_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when label 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).


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 past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.   decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_atten


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tions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True


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is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.   encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   encoder_global_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x),
where x is the number of to


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kens 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.    Base class for outputs of sequence-to-sequence sentence classification models.
 class transformers.models.led.modeling_led.LEDSeq2SeqQuestionAnsweringModelOutput  < source > ( loss: typing.Optional[torch.FloatTensor] = None start_logits: FloatTensor = None end_logits: FloatTensor = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None decoder_hidden_states: typing.Optional[typing.Tuple[torch.FloatTensor]] = None decoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None cross_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_last_hidden_state: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[typing.Tuple[torch


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.FloatTensor]] = None encoder_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None encoder_global_attentions: typing.Optional[typing.Tuple[torch.FloatTensor]] = None  )   Parameters   loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) —
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.   start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) —
Span-start scores (before SoftMax).   end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) —
Span-end scores (before SoftMax).   past_key_values (List[torch.FloatTensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of torch.FloatTensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used


LED specific outputs

(see past_key_values input) to speed up sequential decoding.   decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.ou


LED specific outputs

tput_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of torch.FloatTensor (one for the output of the embeddings + one for the output of each layer) of
shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.   encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_


LED specific outputs

attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   encoder_global_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x),
where x 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.    Base class for outputs of sequence-to-sequence question answering models.
 class transformers.models.led.modeling_tf_led


LED specific outputs

.TFLEDEncoderBaseModelOutput  < source > ( last_hidden_state: tf.Tensor = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None global_attentions: Tuple[tf.Tensor] | None = None  )   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 when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.   attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequ


LED specific outputs

ence_length, x + attention_window + 1), where x 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 (remaining `attention_window
1values). Note that the firstxvalues refer to tokens with fixed positions in the text, but the remainingattention_window + 1values refer to tokens with relative positions: the attention weight of a token to itself is located at indexx + attention_window / 2and theattention_window / 2preceding (succeeding) values are the attention weights to theattention_window / 2preceding (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 firs


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txattention weights. If a token has global attention, the attention weights to all other tokens inattentionsis set to 0, the values should be accessed fromglobal_attentions`.
   global_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x), where x
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.    Base class for Longformer’s outputs, with potential hidden states, local and global attentions.
 class transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput  < source > ( last_hidden_state: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_st


LED specific outputs

ates: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None encoder_global_attentions: Tuple[tf.Tensor] | None = None  )   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 when use_cache=True is passed or when config.use_cache=True) —
List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the


LED specific outputs

attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.   decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when


LED specific outputs

 config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.   encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or w


LED specific outputs

hen config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   encoder_global_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x), where x
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.    Base class for model encoder’s outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.
 class transformers.models.led.modeling_tf_led.T


LED specific outputs

FLEDSeq2SeqLMOutput  < source > ( loss: tf.Tensor | None = None logits: tf.Tensor = None past_key_values: List[tf.Tensor] | None = None decoder_hidden_states: Tuple[tf.Tensor] | None = None decoder_attentions: Tuple[tf.Tensor] | None = None cross_attentions: Tuple[tf.Tensor] | None = None encoder_last_hidden_state: tf.Tensor | None = None encoder_hidden_states: Tuple[tf.Tensor] | None = None encoder_attentions: Tuple[tf.Tensor] | None = None encoder_global_attentions: Tuple[tf.Tensor] | None = None  )   Parameters   loss (tf.Tensor of shape (1,), optional, returned when labels is provided) —
Language modeling loss.   logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) —
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).   past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) —
List of tf.Tensor of length config.n_layers, with each


LED specific outputs

 tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
used (see past_key_values input) to speed up sequential decoding.   decoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.   decoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weigh


LED specific outputs

ted average in the
self-attention heads.   cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.   encoder_last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) —
Sequence of hidden-states at the output of the last layer of the encoder of the model.   encoder_hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) —
Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape
(batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each


LED specific outputs

layer plus the initial embedding outputs.   encoder_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.   encoder_global_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) —
Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, x), where x
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.    Base class for sequence-to-sequ


LEDModel

class transformers.LEDModel  < source > ( config: LEDConfig  )   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 the
from_pretrained() method to load the model weights.    The bare LED Model outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. See the superclass documentation for the generic methods the library
implements for all its models (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 general usage and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = No


LEDModel

ne decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None global_attention_mask: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length)) —
Indices of input sequence tokens in the vocabulary. Pa


LEDModel

dding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using LedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
LED uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values
is used, optionally only the last decoder_input_ids have to be input (see past_key_values).   decoder_attention_mask (torch.LongTe


LEDModel

nsor 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  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 value


LEDModel

s 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.


LEDModel

encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can op


LEDModel

tionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indic


LEDModel

es into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   Returns
transformers.modeling_outputs.Seq2SeqModelOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqModelOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config


LEDModel

.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 when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential deco


LEDModel

ding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the optional initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of


LEDModel

 torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the optional initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attention


LEDModel

s=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The LEDModel forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.  Example:   Copied >>> from transformers import AutoTokenizer, LEDModel
>>> import torch
>>> tokenizer = AutoTokenizer.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_hid


LEDForConditionalGeneration

class transformers.LEDForConditionalGeneration  < source > ( config: LEDConfig  )   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 the
from_pretrained() method to load the model weights.    The LED Model with a language modeling head. Can be used for summarization.
This model inherits from PreTrainedModel. See the superclass documentation for the generic methods the library
implements for all its models (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 general usage and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.Long


LEDForConditionalGeneration

Tensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None global_attention_mask: typing.Optional[torch.FloatTensor] = None past_key_values: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length))


LEDForConditionalGeneration

 —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using LedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
LED uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values
is used, optionally only the last decoder_input_ids have to be input (see


LEDForConditionalGeneration

past_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  token should be given global attention. For QA, all question
tokens should also have global attention. Please ref


LEDForConditionalGeneration

er 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 h


LEDForConditionalGeneration

ead is not masked,
0 indicates the head is masked.
   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential d


LEDForConditionalGeneration

ecoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more


LEDForConditionalGeneration

 control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size, sequence_length), optional) —
Labels for computing the masked language modeling loss. Indices should either be in [0, ..


LEDForConditionalGeneration

., config.vocab_size] or -100 (see input_ids docstring). Tokens with indices set to -100 are ignored
(masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size].   Returns
transformers.modeling_outputs.Seq2SeqLMOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqLMOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (LEDConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_l


LEDForConditionalGeneration

ayers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — T


LEDForConditionalGeneration

uple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_s


LEDForConditionalGeneration

tates=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The LEDForConditionalGeneration forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pre and post processing steps w


LEDForConditionalGeneration

hile
the latter silently ignores them.  Conditional generation example:   Copied >>> from transformers import AutoTokenizer, LEDForConditionalGeneration
>>> tokenizer = AutoTokenizer.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))  Summarization example:   Copied >>> import torch
>>> from transformers import AutoTokenizer, LEDForConditionalGeneration
>>> model = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv")
>>> tokenizer = AutoTokenizer.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


LEDForConditionalGeneration

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 se


LEDForConditionalGeneration

quences 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)
>>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_space


LEDForSequenceClassification

class transformers.LEDForSequenceClassification  < source > ( config: LEDConfig **kwargs  )   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 the
from_pretrained() method to load the model weights.    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. See the superclass documentation for the generic methods the library
implements for all its models (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 general usage and behavior.  forward  < source > ( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[tor


LEDForSequenceClassification

ch.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None global_attention_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None  ) → transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor)  Parameters   input_ids (torch.LongTensor of shape (batch_size, sequence_length


LEDForSequenceClassification

)) —
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using LedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?
LED uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values
is used, optionally only the last decoder_input_ids have to be


LEDForSequenceClassification

 input (see past_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  token should be given global attention. For QA, all question
tokens should also have global


LEDForSequenceClassification

 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 selec


LEDForSequenceClassification

ted in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_


LEDForSequenceClassification

key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup matrix.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (s


LEDForSequenceClassification

ee past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hidden_states (bool, optional) —
Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for
more detail.   return_dict (bool, optional) —
Whether or not to return a ModelOutput instead of a plain tuple.   labels (torch.LongTensor of shape (batch_size,), optional) —
Labels for computing the sequence classifica


LEDForSequenceClassification

tion/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels > 1 a classification loss is computed (Cross-Entropy).   Returns
transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or tuple(torch.FloatTensor)

A transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput or a tuple of
torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various
elements depending on the configuration (LEDConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when label 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 when use_cache=True is passed or when config.use_cache=True) — Tuple of tuple(torch.FloatTensor) of length config.n_lay


LEDForSequenceClassification

ers, with each tuple having 2 tensors of shape
(batch_size, num_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
decoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the decoder at the output of each layer plus the initial embedding outputs.
decoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=T


LEDForSequenceClassification

rue) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.
encoder_last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — Sequence of hidden-states at the output of the last layer of the encoder of the model.
encoder_hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when conf


LEDForSequenceClassification

ig.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the encoder at the output of each layer plus the initial embedding outputs.
encoder_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.
  The LEDForSequenceClassification forward method, overrides the __call__ special method. Although the recipe for forward pass needs to be defined within this function, one should call the Module
instance afterwards instead of this since the former takes care of running the pr


LEDForSequenceClassification

e and post processing steps while
the latter silently ignores them.  Example of single-label classification:   Copied >>> import torch
>>> from transformers import AutoTokenizer, LEDForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = LEDForSequenceClassification.from_pretrained("allenai/led-base-16384")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = LEDForSequenceClassification.from_pretrained("allenai/led-base-16384", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss  Example of multi-label classification:   Copied >>> import torch
>>> from transformers import Aut


LEDForSequenceClassification

oTokenizer, LEDForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = LEDForSequenceClassification.from_pretrained("allenai/led-base-16384", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
...     logits = model(**inputs).logits
>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>> # To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`
>>> num_labels = len(model.config.id2label)
>>> model = LEDForSequenceClassification.from_pretrained(
...     "allenai/led-base-16384", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=la


LEDForQuestionAnswering

class transformers.LEDForQuestionAnswering  < source > ( config  )   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 the
from_pretrained() method to load the model weights.    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. See the superclass documentation for the generic methods the library
implements for all its models (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 general usage and behavior.  forward  < source > ( i


LEDForQuestionAnswering

nput_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None decoder_input_ids: typing.Optional[torch.LongTensor] = None decoder_attention_mask: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.Tensor] = None decoder_head_mask: typing.Optional[torch.Tensor] = None cross_attn_head_mask: typing.Optional[torch.Tensor] = None encoder_outputs: typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None global_attention_mask: typing.Optional[torch.FloatTensor] = None start_positions: typing.Optional[torch.LongTensor] = None end_positions: typing.Optional[torch.LongTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None decoder_inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] =


LEDForQuestionAnswering

None  ) → transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput or tuple(torch.FloatTensor)  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 AutoTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTokenizer.call() for details.
What are input IDs?   attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) —
Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
1 for tokens that are not masked,
0 for tokens that are masked.
What are attention masks?   decoder_input_ids (torch.LongTensor of shape (batch_size, target_sequence_length), optional) —
Indices of decoder input sequence tokens in the vocabulary.
Indices can be obtained using LedTokenizer. See PreTrainedTokenizer.encode() and
PreTrainedTo


LEDForQuestionAnswering

kenizer.call() for details.
What are input IDs?
LED uses the eos_token_id as the starting token for decoder_input_ids generation. If past_key_values
is used, optionally only the last decoder_input_ids have to be input (see past_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


LEDForQuestionAnswering

 is
important for task-specific finetuning because it makes the model more flexible at representing the task.
For example, for classification, the  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]


LEDForQuestionAnswering

:
1 indicates the head is not masked,
0 indicates the head is masked.
   cross_attn_head_mask (torch.Tensor of shape (decoder_layers, decoder_attention_heads), optional) —
Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in [0, 1]:
1 indicates the head is not masked,
0 indicates the head is masked.
   encoder_outputs (tuple(tuple(torch.FloatTensor), optional) —
Tuple consists of (last_hidden_state, optional: hidden_states, optional: attentions)
last_hidden_state of shape (batch_size, sequence_length, hidden_size), optional) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.   past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) —
Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape
(batch_size, num


LEDForQuestionAnswering

_heads, sequence_length, embed_size_per_head)) and 2 additional tensors of shape
(batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see past_key_values input) to speed up sequential decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that
don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all
decoder_input_ids of shape (batch_size, sequence_length).   inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) —
Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert input_ids indices into associated vectors than the
model’s internal embedding lookup mat


LEDForQuestionAnswering

rix.   decoder_inputs_embeds (torch.FloatTensor of shape (batch_size, target_sequence_length, hidden_size), optional) —
Optionally, instead of passing decoder_input_ids you can choose to directly pass an embedded
representation. If past_key_values is used, optionally only the last decoder_inputs_embeds have to be
input (see past_key_values). This is useful if you want more control over how to convert
decoder_input_ids indices into associated vectors than the model’s internal embedding lookup matrix.
If decoder_input_ids and decoder_inputs_embeds are both unset, decoder_inputs_embeds takes the value
of inputs_embeds.   use_cache (bool, optional) —
If set to True, past_key_values key value states are returned and can be used to speed up decoding (see
past_key_values).   output_attentions (bool, optional) —
Whether or not to return the attentions tensors of all attention layers. See attentions under returned
tensors for more detail.   output_hid