---
local: led
sections:
- local: overview
  title: Overview
- local: transformers.LEDConfig
  title: LEDConfig
- local: transformers.LEDTokenizer
  title: LEDTokenizer
- local: transformers.LEDTokenizerFast
  title: LEDTokenizerFast
- local: transformers.models.led.modeling_led.LEDEncoderBaseModelOutput
  title: LED specific outputs
- local: transformers.LEDModel
  title: LEDModel
- local: transformers.LEDForConditionalGeneration
  title: LEDForConditionalGeneration
- local: transformers.LEDForSequenceClassification
  title: LEDForSequenceClassification
- local: transformers.LEDForQuestionAnswering
  title: LEDForQuestionAnswering
- local: transformers.TFLEDModel
  title: TFLEDModel
- local: transformers.TFLEDForConditionalGeneration
  title: TFLEDForConditionalGeneration
title: LED
---
<script>
import Tip from "./Tip.svelte";
import Youtube from "./Youtube.svelte";
import Docstring from "./Docstring.svelte";
import CodeBlock from "./CodeBlock.svelte";
import CodeBlockFw from "./CodeBlockFw.svelte";
import DocNotebookDropdown from "./DocNotebookDropdown.svelte";
import IconCopyLink from "./IconCopyLink.svelte";
export let fw: "pt" | "tf"
</script>
<!--Copyright 2020 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

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specific language governing permissions and limitations under the License.
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<h1 id="led">LED</h1>

<h2 id="overview">Overview</h2>

The LED model was proposed in [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz
Beltagy, Matthew E. Peters, Arman Cohan.

The abstract from the paper is the following:

*Transformer-based models are unable to process long sequences due to their self-attention operation, which scales
quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention
mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or
longer. Longformer's attention mechanism is a drop-in replacement for the standard self-attention and combines a local
windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we
evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In
contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our
pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on
WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting
long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization
dataset.*

Tips:

- [LEDForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDForConditionalGeneration) is an extension of
  [BartForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartForConditionalGeneration) exchanging the traditional *self-attention* layer with
  *Longformer*'s *chunked self-attention* layer. [LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer) is an alias of
  [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.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](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer) is used with the `pad_to_multiple_of` argument.
- LED makes use of *global attention* by means of the `global_attention_mask` (see
  [LongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerModel)). For summarization, it is advised to put *global attention* only on the first
  `&amp;lt;s>` token. For question answering, it is advised to put *global attention* on all tokens of the question.
- To fine-tune LED on all 16384, it is necessary to enable *gradient checkpointing* by executing
  `model.gradient_checkpointing_enable()`.
- A notebook showing how to evaluate LED, can be accessed [here](https://colab.research.google.com/drive/12INTTR6n64TzS4RrXZxMSXfrOd9Xzamo?usp=sharing).
- A notebook showing how to fine-tune LED, can be accessed [here](https://colab.research.google.com/drive/12LjJazBl7Gam0XBPy_y0CTOJZeZ34c2v?usp=sharing).

This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).


<h2 id="transformers.LEDConfig">LEDConfig</h2>

<div class="docstring">

<docstring><name>class transformers.LEDConfig</name><anchor>transformers.LEDConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/configuration_led.py#L31</source><parameters>[{"name": "vocab_size", "val": " = 50265"}, {"name": "max_encoder_position_embeddings", "val": " = 16384"}, {"name": "max_decoder_position_embeddings", "val": " = 1024"}, {"name": "encoder_layers", "val": " = 12"}, {"name": "encoder_ffn_dim", "val": " = 4096"}, {"name": "encoder_attention_heads", "val": " = 16"}, {"name": "decoder_layers", "val": " = 12"}, {"name": "decoder_ffn_dim", "val": " = 4096"}, {"name": "decoder_attention_heads", "val": " = 16"}, {"name": "encoder_layerdrop", "val": " = 0.0"}, {"name": "decoder_layerdrop", "val": " = 0.0"}, {"name": "use_cache", "val": " = True"}, {"name": "is_encoder_decoder", "val": " = True"}, {"name": "activation_function", "val": " = 'gelu'"}, {"name": "d_model", "val": " = 1024"}, {"name": "dropout", "val": " = 0.1"}, {"name": "attention_dropout", "val": " = 0.0"}, {"name": "activation_dropout", "val": " = 0.0"}, {"name": "init_std", "val": " = 0.02"}, {"name": "decoder_start_token_id", "val": " = 2"}, {"name": "classifier_dropout", "val": " = 0.0"}, {"name": "pad_token_id", "val": " = 1"}, {"name": "bos_token_id", "val": " = 0"}, {"name": "eos_token_id", "val": " = 2"}, {"name": "attention_window", "val": ": typing.Union[typing.List[int], int] = 512"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **vocab_size** (`int`, *optional*, defaults to 50265) --
  Vocabulary size of the LED model. Defines the number of different tokens that can be represented by the
  `inputs_ids` passed when calling [LEDModel](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDModel) or [TFLEDModel](/docs/transformers/pr_15592/en/model_doc/led#transformers.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 feed-forward) layer in decoder.
- **activation_function** (`str` or `function`, *optional*, defaults to `"gelu"`) --
  The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
  `"relu"`, `"silu"` and `"gelu_new"` are supported.
- **dropout** (`float`, *optional*, defaults to 0.1) --
  The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- **attention_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for the attention probabilities.
- **activation_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for activations inside the fully connected layer.
- **classifier_dropout** (`float`, *optional*, defaults to 0.0) --
  The dropout ratio for classifier.
- **max_encoder_position_embeddings** (`int`, *optional*, defaults to 16384) --
  The maximum sequence length that the encoder might ever be used with.
- **max_decoder_position_embeddings** (`int`, *optional*, defaults to 16384) --
  The maximum sequence length that the decoder might ever be used with.
- **init_std** (`float`, *optional*, defaults to 0.02) --
  The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  encoder_layerdrop -- (`float`, *optional*, defaults to 0.0):
  The LayerDrop probability for the encoder. See the [LayerDrop paper](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)</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [LEDModel](/docs/transformers/pr_15592/en/model_doc/led#transformers.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](https://huggingface.co/allenai/led-base-16384) architecture.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the
documentation from [PretrainedConfig](/docs/transformers/pr_15592/en/main_classes/configuration#transformers.PretrainedConfig) for more information.




Example:

```python

```

>>> 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.config


</div>

<h2 id="transformers.LEDTokenizer">LEDTokenizer</h2>

<div class="docstring">

<docstring><name>class transformers.LEDTokenizer</name><anchor>transformers.LEDTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/tokenization_led.py#L39</source><parameters>[{"name": "vocab_file", "val": ""}, {"name": "merges_file", "val": ""}, {"name": "errors", "val": " = 'replace'"}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "sep_token", "val": " = '</s>'"}, {"name": "cls_token", "val": " = '<s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "mask_token", "val": " = '<mask>'"}, {"name": "add_prefix_space", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

Construct a LED tokenizer.

[LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer) is identical to [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer) and runs end-to-end tokenization: punctuation splitting and
wordpiece.

Refer to superclass [BartTokenizer](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizer) for usage examples and documentation concerning parameters.



<div class="docstring">
<docstring><name>build\_inputs\_with\_special\_tokens</name><anchor>transformers.BartTokenizer.build_inputs_with_special_tokens</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L338</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs to which the special tokens will be added.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>List of [input IDs](../glossary#input-ids) with the appropriate special tokens.</retdesc></docstring>

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BART sequence has the following format:

- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s>&amp;lt;/s> B &amp;lt;/s>`








</div>
<div class="docstring">
<docstring><name>get\_special\_tokens\_mask</name><anchor>transformers.BartTokenizer.get_special_tokens_mask</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L363</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.
- **already_has_special_tokens** (`bool`, *optional*, defaults to `False`) --
  Whether or not the token list is already formatted with special tokens for the model.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</retdesc></docstring>

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.








</div>
<div class="docstring">
<docstring><name>create\_token\_type\_ids\_from\_sequences</name><anchor>transformers.BartTokenizer.create_token_type_ids_from_sequences</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/bart/tokenization_bart.py#L390</source><parameters>[{"name": "token_ids_0", "val": ": typing.List[int]"}, {"name": "token_ids_1", "val": ": typing.Optional[typing.List[int]] = None"}]</parameters><paramsdesc>- **token_ids_0** (`List[int]`) --
  List of IDs.
- **token_ids_1** (`List[int]`, *optional*) --
  Optional second list of IDs for sequence pairs.</paramsdesc><paramgroups>0</paramgroups><rettype>`List[int]`</rettype><retdesc>List of zeros.</retdesc></docstring>

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








</div>
<div class="docstring"></div></div>

<h2 id="transformers.LEDTokenizerFast">LEDTokenizerFast</h2>

<div class="docstring">

<docstring><name>class transformers.LEDTokenizerFast</name><anchor>transformers.LEDTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/tokenization_led_fast.py#L40</source><parameters>[{"name": "vocab_file", "val": " = None"}, {"name": "merges_file", "val": " = None"}, {"name": "tokenizer_file", "val": " = None"}, {"name": "errors", "val": " = 'replace'"}, {"name": "bos_token", "val": " = '<s>'"}, {"name": "eos_token", "val": " = '</s>'"}, {"name": "sep_token", "val": " = '</s>'"}, {"name": "cls_token", "val": " = '<s>'"}, {"name": "unk_token", "val": " = '<unk>'"}, {"name": "pad_token", "val": " = '<pad>'"}, {"name": "mask_token", "val": " = '<mask>'"}, {"name": "add_prefix_space", "val": " = False"}, {"name": "trim_offsets", "val": " = True"}, {"name": "**kwargs", "val": ""}]</parameters></docstring>

Construct a "fast" LED tokenizer (backed by HuggingFace's *tokenizers* library).

[LEDTokenizerFast](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizerFast) is identical to [BartTokenizerFast](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizerFast) and runs end-to-end tokenization: punctuation splitting
and wordpiece.

Refer to superclass [BartTokenizerFast](/docs/transformers/pr_15592/en/model_doc/bart#transformers.BartTokenizerFast) for usage examples and documentation concerning parameters.


</div>

<h2 id="transformers.models.led.modeling_led.LEDEncoderBaseModelOutput">LED specific outputs</h2>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_led.LEDEncoderBaseModelOutput</name><anchor>transformers.models.led.modeling_led.LEDEncoderBaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L1126</source><parameters>[{"name": "last_hidden_state", "val": ": FloatTensor"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "global_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **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_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 +
  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
  + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
  remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
  token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
  (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
  If the attention window contains a token with global attention, the attention weight at the corresponding
  index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
  attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
  accessed from `global_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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for LEDEncoder's outputs, with potential hidden states, local and global attentions.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_led.LEDSeq2SeqModelOutput</name><anchor>transformers.models.led.modeling_led.LEDSeq2SeqModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L1169</source><parameters>[{"name": "last_hidden_state", "val": ": FloatTensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[torch.FloatTensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **last_hidden_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) --
  Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`List[torch.FloatTensor]`, *optional*, returned 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_attentions=True`) --
  Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_led.LEDSeq2SeqLMOutput</name><anchor>transformers.models.led.modeling_led.LEDSeq2SeqLMOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L1237</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[torch.FloatTensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **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 decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for sequence-to-sequence language models outputs.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_led.LEDSeq2SeqSequenceClassifierOutput</name><anchor>transformers.models.led.modeling_led.LEDSeq2SeqSequenceClassifierOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L1304</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[torch.FloatTensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **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** (`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_attentions=True`) --
  Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
  sequence_length)`.

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for outputs of sequence-to-sequence sentence classification models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_led.LEDSeq2SeqQuestionAnsweringModelOutput</name><anchor>transformers.models.led.modeling_led.LEDSeq2SeqQuestionAnsweringModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L1371</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "start_logits", "val": ": FloatTensor = None"}, {"name": "end_logits", "val": ": FloatTensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[torch.FloatTensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[torch.FloatTensor]] = None"}]</parameters><paramsdesc>- **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 (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for outputs of sequence-to-sequence question answering models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_tf\_led.TFLEDEncoderBaseModelOutput</name><anchor>transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L1330</source><parameters>[{"name": "last_hidden_state", "val": ": Tensor = None"}, {"name": "hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "global_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}]</parameters><paramsdesc>- **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, 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
  + 1` values). Note that the first `x` values refer to tokens with fixed positions in the text, but the
  remaining `attention_window + 1` values refer to tokens with relative positions: the attention weight of a
  token to itself is located at index `x + attention_window / 2` and the `attention_window / 2` preceding
  (succeeding) values are the attention weights to the `attention_window / 2` preceding (succeeding) tokens.
  If the attention window contains a token with global attention, the attention weight at the corresponding
  index is set to 0; the value should be accessed from the first `x` attention weights. If a token has global
  attention, the attention weights to all other tokens in `attentions` is set to 0, the values should be
  accessed from `global_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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for Longformer's outputs, with potential hidden states, local and global attentions.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_tf\_led.TFLEDSeq2SeqModelOutput</name><anchor>transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L1373</source><parameters>[{"name": "last_hidden_state", "val": ": Tensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}]</parameters><paramsdesc>- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) --
  Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
decoding.




</div>

<div class="docstring">

<docstring><name>class transformers.models.led.modeling\_tf\_led.TFLEDSeq2SeqLMOutput</name><anchor>transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L1441</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "logits", "val": ": Tensor = None"}, {"name": "past_key_values", "val": ": typing.Optional[typing.List[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "decoder_hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "decoder_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "cross_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_last_hidden_state", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "encoder_hidden_states", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}, {"name": "encoder_global_attentions", "val": ": typing.Optional[typing.Tuple[tensorflow.python.framework.ops.Tensor]] = None"}]</parameters><paramsdesc>- **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 tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) --
  Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</paramsdesc><paramgroups>0</paramgroups></docstring>

Base class for sequence-to-sequence language models outputs.




</div>

<h2 id="transformers.LEDModel">LEDModel</h2>

<div class="docstring">

<docstring><name>class transformers.LEDModel</name><anchor>transformers.LEDModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2154</source><parameters>[{"name": "config", "val": ": LEDConfig"}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare LED Model outputting raw hidden-states without any specific head on top.
This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LEDModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2181</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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](https://arxiv.org/abs/1910.13461) 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 &amp;lt;s> token should be given global attention. For QA, all question
  tokens should also have global attention. Please refer to the [Longformer
  paper](https://arxiv.org/abs/2004.05150) for more details. Mask values selected in `[0, 1]`:

  - 0 for local attention (a sliding window attention),
  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

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

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.
- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

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

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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 decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [LEDModel](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDModel) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Example:

```python
>>> from transformers import LEDTokenizer, LEDModel
>>> import torch

>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = LEDModel.from_pretrained("allenai/led-base-16384")

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

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

<h2 id="transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.LEDForConditionalGeneration</name><anchor>transformers.LEDForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2277</source><parameters>[{"name": "config", "val": ": LEDConfig"}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The LED Model with a language modeling head. Can be used for summarization.
This model inherits from [PreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LEDForConditionalGeneration.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2321</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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](https://arxiv.org/abs/1910.13461) 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 &amp;lt;s> token should be given global attention. For QA, all question
  tokens should also have global attention. Please refer to the [Longformer
  paper](https://arxiv.org/abs/2004.05150) for more details. Mask values selected in `[0, 1]`:

  - 0 for local attention (a sliding window attention),
  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

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

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.
- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

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

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
  config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
  (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqLMOutput](/docs/transformers/pr_15592/en/main_classes/output#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](/docs/transformers/pr_15592/en/model_doc/led#transformers.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_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [LEDForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDForConditionalGeneration) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Conditional generation example:

```python
>>> from transformers import LEDTokenizer, LEDForConditionalGeneration

>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> TXT = "My friends are &amp;lt;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:

```python
>>> import torch
>>> from transformers import LEDTokenizer, LEDForConditionalGeneration

>>> model = LEDForConditionalGeneration.from_pretrained("allenai/led-large-16384-arxiv")
>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-large-16384-arxiv")

>>> ARTICLE_TO_SUMMARIZE = '''Transformers (Vaswani et al., 2017) have achieved state-of-the-art
...     results in a wide range of natural language tasks including generative language modeling
...     (Dai et al., 2019; Radford et al., 2019) and discriminative ... language understanding (Devlin et al., 2019).
...     This success is partly due to the self-attention component which enables the network to capture contextual
...     information from the entire sequence. While powerful, the memory and computational requirements of
...     self-attention grow quadratically with sequence length, making it infeasible (or very expensive) to
...     process long sequences. To address this limitation, we present Longformer, a modified Transformer
...     architecture with a self-attention operation that scales linearly with the sequence length, making it
...     versatile for processing long documents (Fig 1). This is an advantage for natural language tasks such as
...     long document classification, question answering (QA), and coreference resolution, where existing approaches
...     partition or shorten the long context into smaller sequences that fall within the typical 512 token limit
...     of BERT-style pretrained models. Such partitioning could potentially result in loss of important
...     cross-partition information, and to mitigate this problem, existing methods often rely on complex
...     architectures to address such interactions. On the other hand, our proposed Longformer is able to build
...     contextual representations of the entire context using multiple layers of attention, reducing the need for
...     task-specific architectures.'''
>>> inputs = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors="pt")

>>> # Global attention on the first token (cf. Beltagy et al. 2020)
>>> global_attention_mask = torch.zeros_like(inputs)
>>> global_attention_mask[:, 0] = 1

>>> # Generate Summary
>>> summary_ids = model.generate(inputs, global_attention_mask=global_attention_mask, num_beams=3, max_length=32)
>>> print(tokenizer.decode(summary_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
```


</div></div>

<h2 id="transformers.LEDForSequenceClassification">LEDForSequenceClassification</h2>

<div class="docstring">

<docstring><name>class transformers.LEDForSequenceClassification</name><anchor>transformers.LEDForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2468</source><parameters>[{"name": "config", "val": ": LEDConfig"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

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](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LEDForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2481</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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](https://arxiv.org/abs/1910.13461) 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 &amp;lt;s> token should be given global attention. For QA, all question
  tokens should also have global attention. Please refer to the [Longformer
  paper](https://arxiv.org/abs/2004.05150) for more details. Mask values selected in `[0, 1]`:

  - 0 for local attention (a sliding window attention),
  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

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

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.
- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

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

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **labels** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
  config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqSequenceClassifierOutput](/docs/transformers/pr_15592/en/main_classes/output#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](/docs/transformers/pr_15592/en/model_doc/led#transformers.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_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [LEDForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDForSequenceClassification) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Example of single-label classification:

```python
>>> from transformers import LEDTokenizer, LEDForSequenceClassification
>>> import torch

>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = LEDForSequenceClassification.from_pretrained("allenai/led-base-16384")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> labels = torch.tensor([1]).unsqueeze(0)  # Batch size 1
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```

Example of multi-label classification:

```python
>>> from transformers import LEDTokenizer, LEDForSequenceClassification
>>> import torch

>>> tokenizer = LEDTokenizer.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")
>>> labels = torch.tensor([[1, 1]], dtype=torch.float)  # need dtype=float for BCEWithLogitsLoss
>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

<h2 id="transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</h2>

<div class="docstring">

<docstring><name>class transformers.LEDForQuestionAnswering</name><anchor>transformers.LEDForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2596</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>

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](/docs/transformers/pr_15592/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.





<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LEDForQuestionAnswering.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_led.py#L2608</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "cross_attn_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "start_positions", "val": " = None"}, {"name": "end_positions", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}]</parameters><paramsdesc>- **input_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
  it.

  Indices can be obtained using [LEDTokenizer](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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?](../glossary#attention-mask)
- **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()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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](https://arxiv.org/abs/1910.13461) 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 &amp;lt;s> token should be given global attention. For QA, all question
  tokens should also have global attention. Please refer to the [Longformer
  paper](https://arxiv.org/abs/2004.05150) for more details. Mask values selected in `[0, 1]`:

  - 0 for local attention (a sliding window attention),
  - 1 for global attention (tokens that attend to all other tokens, and all other tokens attend to them).
- **head_mask** (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

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

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.
- **cross_attn_head_mask** (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0,
  1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tuple(tuple(torch.FloatTensor)`, *optional*) --
  Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
  `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
  hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) --
  Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

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

  If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
  of `inputs_embeds`.
- **use_cache** (`bool`, *optional*) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`).
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple.

- **start_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the start of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
  are not taken into account for computing the loss.
- **end_positions** (`torch.LongTensor` of shape `(batch_size,)`, *optional*) --
  Labels for position (index) of the end of the labelled span for computing the token classification loss.
  Positions are clamped to the length of the sequence (*sequence_length*). Position outside of the sequence
  are not taken into account for computing the loss.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.modeling_outputs.Seq2SeqQuestionAnsweringModelOutput) or a tuple of
`torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various
elements depending on the configuration ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
- **start_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **past_key_values** (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
  `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
  `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
  blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
  shape `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.</retdesc></docstring>
The [LEDForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDForQuestionAnswering) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Example:

```python
>>> from transformers import LEDTokenizer, LEDForQuestionAnswering
>>> import torch

>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = LEDForQuestionAnswering.from_pretrained("allenai/led-base-16384")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> start_positions = torch.tensor([1])
>>> end_positions = torch.tensor([3])

>>> outputs = model(**inputs, start_positions=start_positions, end_positions=end_positions)
>>> loss = outputs.loss
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits
```


</div></div>

<h2 id="transformers.TFLEDModel">TFLEDModel</h2>

<div class="docstring">

<docstring><name>class transformers.TFLEDModel</name><anchor>transformers.TFLEDModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L2256</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The bare LED Model outputting raw hidden-states without any specific head on top.
This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

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

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

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

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

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLEDModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L2268</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": ": typing.Union[typing.Tuple, transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput, NoneType] = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "training", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`tf.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using `LedTokenizer`. See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tf.FloatTensor`, *optional*) --
  hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
- **past_key_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) --
  contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`). Set to `False` during training, `True` during generation
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput](/docs/transformers/pr_15592/en/model_doc/led#transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput](/docs/transformers/pr_15592/en/model_doc/led#transformers.models.led.modeling_tf_led.TFLEDSeq2SeqModelOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDConfig)) and inputs.

- **last_hidden_state** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`) -- Sequence of hidden-states at the output of the last layer of the decoder of the model.

  If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
  hidden_size)` is output.
- **past_key_values** (`List[tf.Tensor]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`) -- List of `tf.Tensor` of length `config.n_layers`, with each tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</retdesc></docstring>
The [TFLEDModel](/docs/transformers/pr_15592/en/model_doc/led#transformers.TFLEDModel) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Example:

```python
>>> from transformers import LEDTokenizer, TFLEDModel
>>> import tensorflow as tf

>>> tokenizer = LEDTokenizer.from_pretrained("allenai/led-base-16384")
>>> model = TFLEDModel.from_pretrained("allenai/led-base-16384")

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

>>> last_hidden_states = outputs.last_hidden_state
```


</div></div>

<h2 id="transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</h2>

<div class="docstring">

<docstring><name>class transformers.TFLEDForConditionalGeneration</name><anchor>transformers.TFLEDForConditionalGeneration</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L2363</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.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()](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel.from_pretrained) method to load the model weights.</paramsdesc><paramgroups>0</paramgroups></docstring>
The LED Model with a language modeling head. Can be used for summarization.
This model inherits from [TFPreTrainedModel](/docs/transformers/pr_15592/en/main_classes/model#transformers.TFPreTrainedModel). Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)

This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.

<Tip>

TF 2.0 models accepts two formats as inputs:

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

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

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

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

</Tip>





<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLEDForConditionalGeneration.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/led/modeling_tf_led.py#L2396</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "decoder_input_ids", "val": " = None"}, {"name": "decoder_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "decoder_head_mask", "val": " = None"}, {"name": "encoder_outputs", "val": ": typing.Optional[transformers.models.led.modeling_tf_led.TFLEDEncoderBaseModelOutput] = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "past_key_values", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "decoder_inputs_embeds", "val": " = None"}, {"name": "use_cache", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "training", "val": " = False"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **input_ids** (`tf.Tensor` of shape `(&amp;lcub;0})`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [BertTokenizer](/docs/transformers/pr_15592/en/model_doc/bert#transformers.BertTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`tf.Tensor` of shape `(&amp;lcub;0})`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

  - 1 for tokens that are **not masked**,
  - 0 for tokens that are **masked**.

  [What are attention masks?](../glossary#attention-mask)
- **decoder_input_ids** (`tf.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  Indices of decoder input sequence tokens in the vocabulary.

  Indices can be obtained using `LedTokenizer`. See [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and
  [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details.

  [What are input IDs?](../glossary#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** (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*) --
  will be made by default and ignore pad tokens. It is not recommended to set this for most use cases.
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **decoder_head_mask** (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:

  - 1 indicates the head is **not masked**,
  - 0 indicates the head is **masked**.

- **encoder_outputs** (`tf.FloatTensor`, *optional*) --
  hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
  of shape `(batch_size, sequence_length, hidden_size)` is a sequence of
- **past_key_values** (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) --
  contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
  If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
  don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
  `decoder_input_ids` of shape `(batch_size, sequence_length)`.
- **use_cache** (`bool`, *optional*, defaults to `True`) --
  If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
  `past_key_values`). Set to `False` during training, `True` during generation
- **output_attentions** (`bool`, *optional*) --
  Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
  tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
  config will be used instead.
- **output_hidden_states** (`bool`, *optional*) --
  Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
  more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
  used instead.
- **return_dict** (`bool`, *optional*) --
  Whether or not to return a [ModelOutput](/docs/transformers/pr_15592/en/main_classes/output#transformers.file_utils.ModelOutput) instead of a plain tuple. This argument can be used
  in eager mode, in graph mode the value will always be set to True.
- **training** (`bool`, *optional*, defaults to `False`) --
  Whether or not to use the model in training mode (some modules like dropout modules have different
  behaviors between training and evaluation).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput](/docs/transformers/pr_15592/en/model_doc/led#transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput](/docs/transformers/pr_15592/en/model_doc/led#transformers.models.led.modeling_tf_led.TFLEDSeq2SeqLMOutput) or a tuple of `tf.Tensor` (if
`return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the
configuration ([LEDConfig](/docs/transformers/pr_15592/en/model_doc/led#transformers.LEDConfig)) and inputs.

- **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 tensor of shape `(2, batch_size, num_heads,
  sequence_length, embed_size_per_head)`).

  Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be
  used (see `past_key_values` input) to speed up sequential decoding.
- **decoder_hidden_states** (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) -- Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape
  `(batch_size, sequence_length, hidden_size)`.

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

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

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

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

  Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
  self-attention heads.
- **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.</retdesc></docstring>
The [TFLEDForConditionalGeneration](/docs/transformers/pr_15592/en/model_doc/led#transformers.TFLEDForConditionalGeneration) forward method, overrides the `__call__` special method.

<Tip>

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.

</Tip>







Examples:

```python
>>> from transformers import LEDTokenizer, TFLEDForConditionalGeneration
>>> import tensorflow as tf

>>> mname = "allenai/led-base-16384"
>>> tokenizer = LEDTokenizer.from_pretrained(mname)
>>> TXT = "My friends are &amp;lt;mask> but they eat too many carbs."
>>> model = TFLEDForConditionalGeneration.from_pretrained(mname)
>>> batch = tokenizer([TXT], return_tensors="tf")
>>> logits = model(inputs=batch.input_ids).logits
>>> probs = tf.nn.softmax(logits[0])
>>> # probs[5] is associated with the mask token
```

</div></div>
