---
local: longformer
sections:
- local: overview
  title: Overview
- local: longformer-self-attention
  title: Longformer Self Attention
- local: training
  title: Training
- local: transformers.LongformerConfig
  title: LongformerConfig
- local: transformers.LongformerTokenizer
  title: LongformerTokenizer
- local: transformers.LongformerTokenizerFast
  title: LongformerTokenizerFast
- local: transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput
  title: Longformer specific outputs
- local: transformers.LongformerModel
  title: LongformerModel
- local: transformers.LongformerForMaskedLM
  title: LongformerForMaskedLM
- local: transformers.LongformerForSequenceClassification
  title: LongformerForSequenceClassification
- local: transformers.LongformerForMultipleChoice
  title: LongformerForMultipleChoice
- local: transformers.LongformerForTokenClassification
  title: LongformerForTokenClassification
- local: transformers.LongformerForQuestionAnswering
  title: LongformerForQuestionAnswering
- local: transformers.TFLongformerModel
  title: TFLongformerModel
- local: transformers.TFLongformerForMaskedLM
  title: TFLongformerForMaskedLM
- local: transformers.TFLongformerForQuestionAnswering
  title: TFLongformerForQuestionAnswering
- local: transformers.TFLongformerForSequenceClassification
  title: TFLongformerForSequenceClassification
- local: transformers.TFLongformerForTokenClassification
  title: TFLongformerForTokenClassification
- local: transformers.TFLongformerForMultipleChoice
  title: TFLongformerForMultipleChoice
title: Longformer
---
<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

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->

<h1 id="longformer">Longformer</h1>

**DISCLAIMER:** This model is still a work in progress, if you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).

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

The Longformer model was presented in [Longformer: The Long-Document Transformer](https://arxiv.org/pdf/2004.05150.pdf) 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.*

Tips:

- Since the Longformer is based on RoBERTa, it doesn't have `token_type_ids`. You don't need to indicate which
  token belongs to which segment. Just separate your segments with the separation token `tokenizer.sep_token` (or
  `&amp;lt;/s>`).

This model was contributed by [beltagy](https://huggingface.co/beltagy). The Authors' code can be found [here](https://github.com/allenai/longformer).

<h2 id="longformer-self-attention">Longformer Self Attention</h2>

Longformer self attention employs self attention on both a "local" context and a "global" context. Most tokens only
attend "locally" to each other meaning that each token attends to its \\(\frac&amp;lcub;1}&amp;lcub;2} w\\) previous tokens and
\\(\frac&amp;lcub;1}&amp;lcub;2} w\\) succeding tokens with \\(w\\) being the window length as defined in
`config.attention_window`. Note that `config.attention_window` can be of type `List` to define a
different \\(w\\) for each layer. A selected few tokens attend "globally" to all other tokens, as it is
conventionally done for all tokens in `BertSelfAttention`.

Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices. Also note
that every "locally" attending token not only attends to tokens within its window \\(w\\), but also to all "globally"
attending tokens so that global attention is *symmetric*.

The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor
`global_attention_mask` at run-time appropriately. All Longformer models employ the following logic for
`global_attention_mask`:

- 0: the token attends "locally",
- 1: the token attends "globally".

For more information please also refer to [forward()](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerModel.forward) method.

Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually
represents the memory and time bottleneck, can be reduced from \\(\mathcal&amp;lcub;O}(n_s \times n_s)\\) to
\\(\mathcal&amp;lcub;O}(n_s \times w)\\), with \\(n_s\\) being the sequence length and \\(w\\) being the average window
size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of
"locally" attending tokens.

For more information, please refer to the official [paper](https://arxiv.org/pdf/2004.05150.pdf).


<h2 id="training">Training</h2>

[LongformerForMaskedLM](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForMaskedLM) is trained the exact same way [RobertaForMaskedLM](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.RobertaForMaskedLM) is
trained and should be used as follows:

```python
input_ids = tokenizer.encode("This is a sentence from [MASK] training data", return_tensors="pt")
mlm_labels = tokenizer.encode("This is a sentence from the training data", return_tensors="pt")

loss = model(input_ids, labels=input_ids, masked_lm_labels=mlm_labels)[0]
```

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

<div class="docstring">

<docstring><name>class transformers.LongformerConfig</name><anchor>transformers.LongformerConfig</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/configuration_longformer.py#L33</source><parameters>[{"name": "attention_window", "val": ": typing.Union[typing.List[int], int] = 512"}, {"name": "sep_token_id", "val": ": int = 2"}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **attention_window** (`int` or `List[int]`, *optional*, defaults to 512) --
  Size of an attention window around each token. If an `int`, use the same size for all layers. To specify a
  different window size for each layer, use a `List[int]` where `len(attention_window) == num_hidden_layers`.</paramsdesc><paramgroups>0</paramgroups></docstring>

This is the configuration class to store the configuration of a [LongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerModel) or a [TFLongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerModel). It
is used to instantiate a Longformer model according to the specified arguments, defining the model architecture.

This is the configuration class to store the configuration of a [LongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerModel). It is used to instantiate an
Longformer 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 RoBERTa
[roberta-base](https://huggingface.co/roberta-base) architecture with a sequence length 4,096.

The [LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig) class directly inherits [RobertaConfig](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.RobertaConfig). It reuses the same defaults. Please check the
parent class for more information.



Example:

```python
>>> from transformers import LongformerConfig, LongformerModel

>>> # Initializing a Longformer configuration
>>> configuration = LongformerConfig()

>>> # Initializing a model from the configuration
>>> model = LongformerModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

</div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerTokenizer</name><anchor>transformers.LongformerTokenizer</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/tokenization_longformer.py#L51</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 Longformer tokenizer.

[LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer) is identical to [RobertaTokenizer](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.RobertaTokenizer). Refer to the superclass for usage examples and
documentation concerning parameters.


</div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerTokenizerFast</name><anchor>transformers.LongformerTokenizerFast</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/tokenization_longformer_fast.py#L59</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" Longformer tokenizer (backed by HuggingFace's *tokenizers* library).

[LongformerTokenizerFast](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizerFast) is identical to [RobertaTokenizerFast](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.RobertaTokenizerFast). Refer to the superclass for usage examples
and documentation concerning parameters.


</div>

<h2 id="transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput">Longformer specific outputs</h2>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerBaseModelOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L61</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 Longformer's outputs, with potential hidden states, local and global attentions.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerBaseModelOutputWithPooling</name><anchor>transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L104</source><parameters>[{"name": "last_hidden_state", "val": ": FloatTensor"}, {"name": "pooler_output", "val": ": FloatTensor = None"}, {"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.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) --
  Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.
- **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 Longformer's outputs that also contains a pooling of the last hidden states.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerMaskedLMOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L152</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"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>- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) --
  Masked language modeling (MLM) 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).
- **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 masked language models outputs.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerQuestionAnsweringModelOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L198</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "start_logits", "val": ": FloatTensor = None"}, {"name": "end_logits", "val": ": FloatTensor = None"}, {"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>- **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).
- **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 outputs of question answering Longformer models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerSequenceClassifierOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L247</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"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>- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` 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).
- **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 outputs of sentence classification models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerMultipleChoiceModelOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L293</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"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>- **loss** (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided) --
  Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, num_choices)`) --
  *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **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 outputs of multiple choice Longformer models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_longformer.LongformerTokenClassifierOutput</name><anchor>transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L341</source><parameters>[{"name": "loss", "val": ": typing.Optional[torch.FloatTensor] = None"}, {"name": "logits", "val": ": FloatTensor = None"}, {"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>- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  --
  Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) --
  Classification scores (before SoftMax).
- **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 outputs of token classification models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerBaseModelOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L66</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.longformer.modeling\_tf\_longformer.TFLongformerBaseModelOutputWithPooling</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L109</source><parameters>[{"name": "last_hidden_state", "val": ": Tensor = None"}, {"name": "pooler_output", "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.
- **pooler_output** (`tf.Tensor` of shape `(batch_size, hidden_size)`) --
  Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.
- **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 that also contains a pooling of the last hidden states.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMaskedLMOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L157</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "logits", "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>- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) --
  Masked language modeling (MLM) 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).
- **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 masked language models outputs.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerQuestionAnsweringModelOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L203</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "start_logits", "val": ": Tensor = None"}, {"name": "end_logits", "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>- **loss** (`tf.Tensor` 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** (`tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Span-start scores (before SoftMax).
- **end_logits** (`tf.Tensor` of shape `(batch_size, sequence_length)`) --
  Span-end scores (before SoftMax).
- **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 outputs of question answering Longformer models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerSequenceClassifierOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L252</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "logits", "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>- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) --
  Classification (or regression if config.num_labels==1) loss.
- **logits** (`tf.Tensor` of shape `(batch_size, config.num_labels)`) --
  Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **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 outputs of sentence classification models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMultipleChoiceModelOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L298</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "logits", "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>- **loss** (`tf.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided) --
  Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, num_choices)`) --
  *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **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 outputs of multiple choice models.




</div>

<div class="docstring">

<docstring><name>class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerTokenClassifierOutput</name><anchor>transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L346</source><parameters>[{"name": "loss", "val": ": typing.Optional[tensorflow.python.framework.ops.Tensor] = None"}, {"name": "logits", "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>- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  --
  Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`) --
  Classification scores (before SoftMax).
- **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 outputs of token classification models.




</div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerModel</name><anchor>transformers.LongformerModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1497</source><parameters>[{"name": "config", "val": ""}, {"name": "add_pooling_layer", "val": " = True"}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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 Longformer 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.



This class copied code from [RobertaModel](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.RobertaModel) and overwrote standard self-attention with longformer self-attention
to provide the ability to process long sequences following the self-attention approach described in [Longformer:
the Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, and Arman Cohan.
Longformer self-attention combines a local (sliding window) and global attention to extend to long documents
without the O(n^2) increase in memory and compute.

The self-attention module `LongformerSelfAttention` implemented here supports the combination of local and global
attention but it lacks support for autoregressive attention and dilated attention. Autoregressive and dilated
attention are more relevant for autoregressive language modeling than finetuning on downstream tasks. Future
release will add support for autoregressive attention, but the support for dilated attention requires a custom CUDA
kernel to be memory and compute efficient.




<div class="docstring">
<docstring><name>forward</name><anchor>transformers.LongformerModel.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1608</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "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.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

  [What are attention masks?](../glossary#attention-mask)
- **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. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.
- **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.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) 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 model.
- **pooler_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) -- Last layer hidden-state of the first token of the sequence (classification token) further processed by a
  Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
  prediction (classification) objective during pretraining.
- **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.</retdesc></docstring>
The [LongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerModel) 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
>>> import torch
>>> from transformers import LongformerModel, LongformerTokenizer

>>> model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")

>>> SAMPLE_TEXT = " ".join(["Hello world! "] * 1000)  # long input document
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1

>>> attention_mask = torch.ones(
...     input_ids.shape, dtype=torch.long, device=input_ids.device
>>> )  # initialize to local attention
>>> global_attention_mask = torch.zeros(
...     input_ids.shape, dtype=torch.long, device=input_ids.device
>>> )  # initialize to global attention to be deactivated for all tokens
>>> global_attention_mask[
...     :,
...     [
...         1,
...         4,
...         21,
...     ],
>>> ] = 1  # Set global attention to random tokens for the sake of this example
>>> # Usually, set global attention based on the task. For example,
>>> # classification: the &amp;lt;s> token
>>> # QA: question tokens
>>> # LM: potentially on the beginning of sentences and paragraphs
>>> outputs = model(input_ids, attention_mask=attention_mask, global_attention_mask=global_attention_mask)
>>> sequence_output = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output
```

</div></div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerForMaskedLM</name><anchor>transformers.LongformerForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1732</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>
Longformer Model with a `language modeling` 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.LongformerForMaskedLM.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1751</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

  [What are attention masks?](../glossary#attention-mask)
- **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. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.
- **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 be in `[-100, 0, ...,
  config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
  loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
- **kwargs** (`Dict[str, any]`, optional, defaults to *&amp;lcub;}*) --
  Used to hide legacy arguments that have been deprecated.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) 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).
- **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.</retdesc></docstring>
The [LongformerForMaskedLM](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForMaskedLM) 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
>>> import torch
>>> from transformers import LongformerForMaskedLM, LongformerTokenizer

>>> model = LongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")

>>> SAMPLE_TEXT = " ".join(["Hello world! "] * 1000)  # long input document
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  # batch of size 1

>>> attention_mask = None  # default is local attention everywhere, which is a good choice for MaskedLM
>>> # check `LongformerModel.forward` for more details how to set *attention_mask*
>>> outputs = model(input_ids, attention_mask=attention_mask, labels=input_ids)
>>> loss = outputs.loss
>>> prediction_logits = outputs.logits
```

</div></div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerForSequenceClassification</name><anchor>transformers.LongformerForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1837</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model transformer with a sequence classification/regression 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.LongformerForSequenceClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1852</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

  [What are attention masks?](../glossary#attention-mask)
- **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. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.
- **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 regression loss is computed (Mean-Square loss), If
  `config.num_labels > 1` a classification loss is computed (Cross-Entropy).</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` 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).
- **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.</retdesc></docstring>
The [LongformerForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForSequenceClassification) 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 LongformerTokenizer, LongformerForSequenceClassification
>>> import torch

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = LongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096")

>>> 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 LongformerTokenizer, LongformerForSequenceClassification
>>> import torch

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = LongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096", 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.LongformerForMultipleChoice">LongformerForMultipleChoice</h2>

<div class="docstring">

<docstring><name>class transformers.LongformerForMultipleChoice</name><anchor>transformers.LongformerForMultipleChoice</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L2188</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG 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.LongformerForMultipleChoice.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L2199</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "labels", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "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, num_choices, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, num_choices, 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)
- **global_attention_mask** (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`torch.FloatTensor` of shape `(batch_size, num_choices, 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.
- **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 multiple choice classification loss. Indices should be in `[0, ...,
  num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
  `input_ids` above)</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, num_choices)`) -- *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **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.</retdesc></docstring>
The [LongformerForMultipleChoice](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForMultipleChoice) 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 LongformerTokenizer, LongformerForMultipleChoice
>>> import torch

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = LongformerForMultipleChoice.from_pretrained("allenai/longformer-base-4096")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)  # choice0 is correct (according to Wikipedia ;)), batch size 1

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**&amp;lcub;k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerForTokenClassification</name><anchor>transformers.LongformerForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L2103</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) 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.LongformerForTokenClassification.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L2118</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "labels", "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.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

  [What are attention masks?](../glossary#attention-mask)
- **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. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.
- **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 token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **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.</retdesc></docstring>
The [LongformerForTokenClassification](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForTokenClassification) 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 LongformerTokenizer, LongformerForTokenClassification
>>> import torch

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = LongformerForTokenClassification.from_pretrained("allenai/longformer-base-4096")

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

>>> outputs = model(**inputs, labels=labels)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.LongformerForQuestionAnswering</name><anchor>transformers.LongformerForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1964</source><parameters>[{"name": "config", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD /
TriviaQA (a linear layers 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.LongformerForQuestionAnswering.forward</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_longformer.py#L1978</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "start_positions", "val": " = None"}, {"name": "end_positions", "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.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). 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.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

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

  [What are attention masks?](../glossary#attention-mask)
- **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. 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 `(num_layers, num_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 `(num_layers, num_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**.

- **token_type_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **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.
- **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.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) 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).
- **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.</retdesc></docstring>
The [LongformerForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerForQuestionAnswering) 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 LongformerTokenizer, LongformerForQuestionAnswering
>>> import torch

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
>>> model = LongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> encoding = tokenizer(question, text, return_tensors="pt")
>>> input_ids = encoding["input_ids"]

>>> # default is local attention everywhere
>>> # the forward method will automatically set global attention on question tokens
>>> attention_mask = encoding["attention_mask"]

>>> outputs = model(input_ids, attention_mask=attention_mask)
>>> start_logits = outputs.start_logits
>>> end_logits = outputs.end_logits
>>> all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())

>>> answer_tokens = all_tokens[torch.argmax(start_logits) : torch.argmax(end_logits) + 1]
>>> answer = tokenizer.decode(
...     tokenizer.convert_tokens_to_ids(answer_tokens)
>>> )  # remove space prepending space token
```

</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerModel</name><anchor>transformers.TFLongformerModel</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2006</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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 Longformer 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(inputs_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>



This class copies code from [TFRobertaModel](/docs/transformers/pr_15592/en/model_doc/roberta#transformers.TFRobertaModel) and overwrites standard self-attention with longformer
self-attention to provide the ability to process long sequences following the self-attention approach described in
[Longformer: the Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, and
Arman Cohan. Longformer self-attention combines a local (sliding window) and global attention to extend to long
documents without the O(n^2) increase in memory and compute.

The self-attention module `TFLongformerSelfAttention` implemented here supports the combination of local and global
attention but it lacks support for autoregressive attention and dilated attention. Autoregressive and dilated
attention are more relevant for autoregressive language modeling than finetuning on downstream tasks. Future
release will add support for autoregressive attention, but the support for dilated attention requires a custom CUDA
kernel to be memory and compute efficient.




<div class="docstring">
<docstring><name>call</name><anchor>transformers.TFLongformerModel.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2028</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "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 [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **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></docstring>
The [TFLongformerModel](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerModel) 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>




</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerForMaskedLM</name><anchor>transformers.TFLongformerForMaskedLM</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2094</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>
Longformer Model with a `language modeling` 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(inputs_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.TFLongformerForMaskedLM.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2111</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "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 `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **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).

- **labels** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
  config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
  loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Masked language modeling (MLM) 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).
- **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.</retdesc></docstring>
The [TFLongformerForMaskedLM](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerForMaskedLM) 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 LongformerTokenizer, TFLongformerForMaskedLM
>>> import tensorflow as tf

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = TFLongformerForMaskedLM.from_pretrained("allenai/longformer-base-4096")

>>> inputs = tokenizer("The capital of France is &amp;lt;mask>.", return_tensors="tf")
>>> inputs["labels"] = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]

>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerForQuestionAnswering</name><anchor>transformers.TFLongformerForQuestionAnswering</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2205</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a span classification head on top for extractive question-answering tasks like SQuAD /
TriviaQA (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).


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(inputs_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.TFLongformerForQuestionAnswering.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2220</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "inputs_embeds", "val": " = None"}, {"name": "output_attentions", "val": " = None"}, {"name": "output_hidden_states", "val": " = None"}, {"name": "return_dict", "val": " = None"}, {"name": "start_positions", "val": " = None"}, {"name": "end_positions", "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 [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **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).

- **start_positions** (`tf.Tensor` 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** (`tf.Tensor` 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.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`tf.Tensor` 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** (`tf.Tensor` of shape `(batch_size, sequence_length)`) -- Span-start scores (before SoftMax).
- **end_logits** (`tf.Tensor` of shape `(batch_size, sequence_length)`) -- Span-end scores (before SoftMax).
- **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.</retdesc></docstring>
The [TFLongformerForQuestionAnswering](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering) 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 LongformerTokenizer, TFLongformerForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")
>>> model = TFLongformerForQuestionAnswering.from_pretrained("allenai/longformer-large-4096-finetuned-triviaqa")

>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> input_dict = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(input_dict)
>>> start_logits = outputs.start_logits
>>> end_logits = outputs.end_logits

>>> all_tokens = tokenizer.convert_ids_to_tokens(input_dict["input_ids"].numpy()[0])
>>> answer = " ".join(all_tokens[tf.math.argmax(start_logits, 1)[0] : tf.math.argmax(end_logits, 1)[0] + 1])
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerForSequenceClassification</name><anchor>transformers.TFLongformerForSequenceClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2377</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.


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(inputs_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.TFLongformerForSequenceClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2389</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "inputs_embeds", "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 `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **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.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) -- Classification (or regression if config.num_labels==1) loss.
- **logits** (`tf.Tensor` of shape `(batch_size, config.num_labels)`) -- Classification (or regression if config.num_labels==1) scores (before SoftMax).
- **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.</retdesc></docstring>
The [TFLongformerForSequenceClassification](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification) 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 LongformerTokenizer, TFLongformerForSequenceClassification
>>> import tensorflow as tf

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = TFLongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> inputs["labels"] = tf.reshape(tf.constant(1), (-1, 1))  # Batch size 1

>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerForTokenClassification</name><anchor>transformers.TFLongformerForTokenClassification</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2652</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.


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(inputs_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.TFLongformerForTokenClassification.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2667</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "inputs_embeds", "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 `(batch_size, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

  Indices can be obtained using [LongformerTokenizer](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerTokenizer). See [PreTrainedTokenizer.__call__()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) and
  [PreTrainedTokenizer.encode()](/docs/transformers/pr_15592/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) --
  Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
  is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
  model's internal embedding lookup matrix.
- **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).

- **labels** (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*) --
  Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided)  -- Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`) -- Classification scores (before SoftMax).
- **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.</retdesc></docstring>
The [TFLongformerForTokenClassification](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerForTokenClassification) 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 LongformerTokenizer, TFLongformerForTokenClassification
>>> import tensorflow as tf

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = TFLongformerForTokenClassification.from_pretrained("allenai/longformer-base-4096")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> input_ids = inputs["input_ids"]
>>> inputs["labels"] = tf.reshape(
...     tf.constant([1] * tf.size(input_ids).numpy()), (-1, tf.size(input_ids))
>>> )  # Batch size 1

>>> outputs = model(inputs)
>>> loss = outputs.loss
>>> logits = outputs.logits
```


</div></div>

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

<div class="docstring">

<docstring><name>class transformers.TFLongformerForMultipleChoice</name><anchor>transformers.TFLongformerForMultipleChoice</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2493</source><parameters>[{"name": "*args", "val": ""}, {"name": "**kwargs", "val": ""}]</parameters><paramsdesc>- **config** ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) -- 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>

Longformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.


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(inputs_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.TFLongformerForMultipleChoice.call</anchor><source>https://github.com/huggingface/transformers/blob/pr_15592/src/transformers/models/longformer/modeling_tf_longformer.py#L2513</source><parameters>[{"name": "input_ids", "val": " = None"}, {"name": "attention_mask", "val": " = None"}, {"name": "head_mask", "val": " = None"}, {"name": "token_type_ids", "val": " = None"}, {"name": "position_ids", "val": " = None"}, {"name": "global_attention_mask", "val": " = None"}, {"name": "inputs_embeds", "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 `(batch_size, num_choices, sequence_length)`) --
  Indices of input sequence tokens in the vocabulary.

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

  [What are input IDs?](../glossary#input-ids)
- **attention_mask** (`tf.Tensor` of shape `(batch_size, num_choices, 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)
- **head_mask** (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*) --
  Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

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

- **global_attention_mask** (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Mask to decide the attention given on each token, local attention or global attention. 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).

- **token_type_ids** (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
  1]`:

  - 0 corresponds to a *sentence A* token,
  - 1 corresponds to a *sentence B* token.

  [What are token type IDs?](../glossary#token-type-ids)
- **position_ids** (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*) --
  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
  config.max_position_embeddings - 1]`.

  [What are position IDs?](../glossary#position-ids)
- **inputs_embeds** (`tf.Tensor` of shape `(batch_size, num_choices, 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.
- **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).

- **labels** (`tf.Tensor` of shape `(batch_size,)`, *optional*) --
  Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
  where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above)</paramsdesc><paramgroups>0</paramgroups><rettype>[transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput) or `tuple(tf.Tensor)`</rettype><retdesc>A [transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput) 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 ([LongformerConfig](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.LongformerConfig)) and inputs.

- **loss** (`tf.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided) -- Classification loss.
- **logits** (`tf.Tensor` of shape `(batch_size, num_choices)`) -- *num_choices* is the second dimension of the input tensors. (see *input_ids* above).

  Classification scores (before SoftMax).
- **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.</retdesc></docstring>
The [TFLongformerForMultipleChoice](/docs/transformers/pr_15592/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice) 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 LongformerTokenizer, TFLongformerForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> model = TFLongformerForMultipleChoice.from_pretrained("allenai/longformer-base-4096")

>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = &amp;lcub;k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)  # batch size is 1

>>> # the linear classifier still needs to be trained
>>> logits = outputs.logits
```


</div></div>
