Longformer

[![Models](https://img.shields.io/badge/All_model_pages-longformer-blueviolet)](https://huggingface.co/models?filter=longformer) [![Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/docs-demos/longformer-base-4096-finetuned-squadv1)

## [](#overview)Overview

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 `
`).
-   A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g., what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the local attention section for more information.

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

## [](#longformer-self-attention)Longformer Self Attention

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 12w\\frac{1}{2} w previous tokens and 12w\\frac{1}{2} w succeeding tokens with ww being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type `List` to define a different ww 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 ww, 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/v4.30.0/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 O(ns×ns)\\mathcal{O}(n\_s \\times n\_s) to O(ns×w)\\mathcal{O}(n\_s \\times w), with nsn\_s being the sequence length and ww 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).

## [](#training)Training

[LongformerForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForMaskedLM) is trained the exact same way [RobertaForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/roberta#transformers.RobertaForMaskedLM) is trained and should be used as follows:

```
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]```

## [](#documentation-resources)Documentation resources

-   [Text classification task guide](../tasks/sequence_classification)
-   [Token classification task guide](../tasks/token_classification)
-   [Question answering task guide](../tasks/question_answering)
-   [Masked language modeling task guide](../tasks/masked_language_modeling)
-   [Multiple choice task guide](../tasks/multiple_choice)

## [](#transformers.LongformerConfig)LongformerConfig

### class transformers.LongformerConfig

[](#transformers.LongformerConfig)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/configuration_longformer.py#L46)

( attention\_window: typing.Union\[typing.List\[int\], int\] = 512sep\_token\_id: int = 2pad\_token\_id: int = 1bos\_token\_id: int = 0eos\_token\_id: int = 2vocab\_size: int = 30522hidden\_size: int = 768num\_hidden\_layers: int = 12num\_attention\_heads: int = 12intermediate\_size: int = 3072hidden\_act: str = 'gelu'hidden\_dropout\_prob: float = 0.1attention\_probs\_dropout\_prob: float = 0.1max\_position\_embeddings: int = 512type\_vocab\_size: int = 2initializer\_range: float = 0.02layer\_norm\_eps: float = 1e-12onnx\_export: bool = False\*\*kwargs )

This is the configuration class to store the configuration of a [LongformerModel](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerModel) or a [TFLongformerModel](/docs/transformers/v4.30.0/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/v4.30.0/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 LongFormer [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) architecture with a sequence length 4,096.

Configuration objects inherit from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](/docs/transformers/v4.30.0/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

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

>>> 
>>> configuration = LongformerConfig()

>>> 
>>> model = LongformerModel(configuration)

>>> 
>>> configuration = model.config```

## [](#transformers.LongformerTokenizer)LongformerTokenizer

### class transformers.LongformerTokenizer

[](#transformers.LongformerTokenizer)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer.py#L116)

( vocab\_filemerges\_fileerrors = 'replace'bos\_token = '
'eos\_token = '
'sep\_token = '
'cls\_token = '
'unk\_token = '<unk>'pad\_token = '<pad>'mask\_token = '<mask>'add\_prefix\_space = False\*\*kwargs )

Constructs a Longformer tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will

be encoded differently whether it is at the beginning of the sentence (without space) or not:

```
>>> from transformers import LongformerTokenizer

>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]

>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

When used with `is_split_into_words=True`, this tokenizer will add a space before each word (even the first one).

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizer) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

#### build\_inputs\_with\_special\_tokens

[](#transformers.LongformerTokenizer.build_inputs_with_special_tokens)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer.py#L355)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.LongformerTokenizer.build_inputs_with_special_tokens.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs to which the special tokens will be added.
-   [](#transformers.LongformerTokenizer.build_inputs_with_special_tokens.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.

List of [input IDs](../glossary#input-ids) with the appropriate special tokens.

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

-   single sequence: `
 X 
`
-   pair of sequences: `
 A 

 B 
`

Converts a sequence of tokens (string) in a single string.

#### create\_token\_type\_ids\_from\_sequences

[](#transformers.LongformerTokenizer.create_token_type_ids_from_sequences)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer.py#L407)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.LongformerTokenizer.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.LongformerTokenizer.create_token_type_ids_from_sequences.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.

List of zeros.

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

#### get\_special\_tokens\_mask

[](#transformers.LongformerTokenizer.get_special_tokens_mask)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer.py#L380)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = Nonealready\_has\_special\_tokens: bool = False ) → `List[int]`

Parameters

-   [](#transformers.LongformerTokenizer.get_special_tokens_mask.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.LongformerTokenizer.get_special_tokens_mask.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.
-   [](#transformers.LongformerTokenizer.get_special_tokens_mask.already_has_special_tokens)**already\_has\_special\_tokens** (`bool`, _optional_, defaults to `False`) — Whether or not the token list is already formatted with special tokens for the model.

A list of integers in the range \[0, 1\]: 1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method.

## [](#transformers.LongformerTokenizerFast)LongformerTokenizerFast

### class transformers.LongformerTokenizerFast

[](#transformers.LongformerTokenizerFast)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer_fast.py#L91)

( vocab\_file = Nonemerges\_file = Nonetokenizer\_file = Noneerrors = 'replace'bos\_token = '
'eos\_token = '
'sep\_token = '
'cls\_token = '
'unk\_token = '<unk>'pad\_token = '<pad>'mask\_token = '<mask>'add\_prefix\_space = Falsetrim\_offsets = True\*\*kwargs )

Construct a “fast” Longformer tokenizer (backed by HuggingFace’s _tokenizers_ library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.

This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will

be encoded differently whether it is at the beginning of the sentence (without space) or not:

```
>>> from transformers import LongformerTokenizerFast

>>> tokenizer = LongformerTokenizerFast.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]

>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]```

You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.

When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.

This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v4.30.0/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast) which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

#### create\_token\_type\_ids\_from\_sequences

[](#transformers.LongformerTokenizerFast.create_token_type_ids_from_sequences)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/tokenization_longformer_fast.py#L303)

( token\_ids\_0: typing.List\[int\]token\_ids\_1: typing.Optional\[typing.List\[int\]\] = None ) → `List[int]`

Parameters

-   [](#transformers.LongformerTokenizerFast.create_token_type_ids_from_sequences.token_ids_0)**token\_ids\_0** (`List[int]`) — List of IDs.
-   [](#transformers.LongformerTokenizerFast.create_token_type_ids_from_sequences.token_ids_1)**token\_ids\_1** (`List[int]`, _optional_) — Optional second list of IDs for sequence pairs.

List of zeros.

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

## [](#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput)Longformer specific outputs

### class transformers.models.longformer.modeling\_longformer.LongformerBaseModelOutput

[](#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L56)

( last\_hidden\_state: FloatTensorhidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

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

### class transformers.models.longformer.modeling\_longformer.LongformerBaseModelOutputWithPooling

[](#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L99)

( last\_hidden\_state: FloatTensorpooler\_output: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for Longformer’s outputs that also contains a pooling of the last hidden states.

### class transformers.models.longformer.modeling\_longformer.LongformerMaskedLMOutput

[](#transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L147)

( loss: typing.Optional\[torch.FloatTensor\] = Nonelogits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for masked language models outputs.

### class transformers.models.longformer.modeling\_longformer.LongformerQuestionAnsweringModelOutput

[](#transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L193)

( loss: typing.Optional\[torch.FloatTensor\] = Nonestart\_logits: FloatTensor = Noneend\_logits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for outputs of question answering Longformer models.

### class transformers.models.longformer.modeling\_longformer.LongformerSequenceClassifierOutput

[](#transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L242)

( loss: typing.Optional\[torch.FloatTensor\] = Nonelogits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for outputs of sentence classification models.

### class transformers.models.longformer.modeling\_longformer.LongformerMultipleChoiceModelOutput

[](#transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L288)

( loss: typing.Optional\[torch.FloatTensor\] = Nonelogits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for outputs of multiple choice Longformer models.

### class transformers.models.longformer.modeling\_longformer.LongformerTokenClassifierOutput

[](#transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L336)

( loss: typing.Optional\[torch.FloatTensor\] = Nonelogits: FloatTensor = Nonehidden\_states: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneattentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = Noneglobal\_attentions: typing.Optional\[typing.Tuple\[torch.FloatTensor\]\] = None )

Base class for outputs of token classification models.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerBaseModelOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L69)

( last\_hidden\_state: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

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

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerBaseModelOutputWithPooling

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerBaseModelOutputWithPooling)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L112)

( last\_hidden\_state: tf.Tensor = Nonepooler\_output: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for Longformer’s outputs that also contains a pooling of the last hidden states.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMaskedLMOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L160)

( loss: tf.Tensor | None = Nonelogits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for masked language models outputs.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerQuestionAnsweringModelOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L206)

( loss: tf.Tensor | None = Nonestart\_logits: tf.Tensor = Noneend\_logits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for outputs of question answering Longformer models.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerSequenceClassifierOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L255)

( loss: tf.Tensor | None = Nonelogits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for outputs of sentence classification models.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMultipleChoiceModelOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L301)

( loss: tf.Tensor | None = Nonelogits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for outputs of multiple choice models.

### class transformers.models.longformer.modeling\_tf\_longformer.TFLongformerTokenClassifierOutput

[](#transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L349)

( loss: tf.Tensor | None = Nonelogits: tf.Tensor = Nonehidden\_states: Tuple\[tf.Tensor\] | None = Noneattentions: Tuple\[tf.Tensor\] | None = Noneglobal\_attentions: Tuple\[tf.Tensor\] | None = None )

Base class for outputs of token classification models.

## [](#transformers.LongformerModel)LongformerModel

### class transformers.LongformerModel

[](#transformers.LongformerModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1535)

( configadd\_pooling\_layer = True )

Parameters

-   [](#transformers.LongformerModel.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Longformer Model outputting raw hidden-states without any specific head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/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/v4.30.0/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.

#### forward

[](#transformers.LongformerModel.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1648)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerBaseModelOutputWithPooling](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerBaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

The [LongformerModel](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

```
>>> import torch
>>> from transformers import LongformerModel, AutoTokenizer

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

>>> SAMPLE_TEXT = " ".join(["Hello world! "] * 1000)  
>>> input_ids = torch.tensor(tokenizer.encode(SAMPLE_TEXT)).unsqueeze(0)  

>>> attention_mask = torch.ones(
...     input_ids.shape, dtype=torch.long, device=input_ids.device
... )  
>>> global_attention_mask = torch.zeros(
...     input_ids.shape, dtype=torch.long, device=input_ids.device
... )  
>>> global_attention_mask[
...     :,
...     [
...         1,
...         4,
...         21,
...     ],
... ] = 1  
>>> 
>>> 
>>> 
>>> 
>>> 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```

## [](#transformers.LongformerForMaskedLM)LongformerForMaskedLM

### class transformers.LongformerForMaskedLM

[](#transformers.LongformerForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1772)

( config )

Parameters

-   [](#transformers.LongformerForMaskedLM.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

Longformer Model with a `language modeling` head on top.

This model inherits from [PreTrainedModel](/docs/transformers/v4.30.0/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.

#### forward

[](#transformers.LongformerForMaskedLM.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1791)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerMaskedLMOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMaskedLMOutput) or `tuple(torch.FloatTensor)`

The [LongformerForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForMaskedLM) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Mask filling example:

```
>>> from transformers import AutoTokenizer, LongformerForMaskedLM

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

Let’s try a very long input.

```
>>> TXT = (
...     "My friends are <mask> but they eat too many carbs."
...     + " That's why I decide not to eat with them." * 300
... )
>>> input_ids = tokenizer([TXT], return_tensors="pt")["input_ids"]
>>> logits = model(input_ids).logits

>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
>>> probs = logits[0, masked_index].softmax(dim=0)
>>> values, predictions = probs.topk(5)

>>> tokenizer.decode(predictions).split()
['healthy', 'skinny', 'thin', 'good', 'vegetarian']```

## [](#transformers.LongformerForSequenceClassification)LongformerForSequenceClassification

### class transformers.LongformerForSequenceClassification

[](#transformers.LongformerForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1887)

( config )

Parameters

-   [](#transformers.LongformerForSequenceClassification.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

#### forward

[](#transformers.LongformerForSequenceClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L1901)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerSequenceClassifierOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerSequenceClassifierOutput) or `tuple(torch.FloatTensor)`

The [LongformerForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForSequenceClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example of single-label classification:

```
>>> import torch
>>> from transformers import AutoTokenizer, LongformerForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("jpwahle/longformer-base-plagiarism-detection")
>>> model = LongformerForSequenceClassification.from_pretrained("jpwahle/longformer-base-plagiarism-detection")

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

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'ORIGINAL'

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = LongformerForSequenceClassification.from_pretrained("jpwahle/longformer-base-plagiarism-detection", num_labels=num_labels)

>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
5.44```

Example of multi-label classification:

```
>>> import torch
>>> from transformers import AutoTokenizer, LongformerForSequenceClassification

>>> tokenizer = AutoTokenizer.from_pretrained("jpwahle/longformer-base-plagiarism-detection")
>>> model = LongformerForSequenceClassification.from_pretrained("jpwahle/longformer-base-plagiarism-detection", problem_type="multi_label_classification")

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

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]

>>> 
>>> num_labels = len(model.config.id2label)
>>> model = LongformerForSequenceClassification.from_pretrained(
...     "jpwahle/longformer-base-plagiarism-detection", num_labels=num_labels, problem_type="multi_label_classification"
... )

>>> labels = torch.sum(
...     torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.LongformerForMultipleChoice)LongformerForMultipleChoice

### class transformers.LongformerForMultipleChoice

[](#transformers.LongformerForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2245)

( config )

Parameters

-   [](#transformers.LongformerForMultipleChoice.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

#### forward

[](#transformers.LongformerForMultipleChoice.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2256)

( input\_ids: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerMultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerMultipleChoiceModelOutput) or `tuple(torch.FloatTensor)`

The [LongformerForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForMultipleChoice) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, LongformerForMultipleChoice
>>> import torch

>>> tokenizer = AutoTokenizer.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)  

>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)  

>>> 
>>> loss = outputs.loss
>>> logits = outputs.logits```

## [](#transformers.LongformerForTokenClassification)LongformerForTokenClassification

### class transformers.LongformerForTokenClassification

[](#transformers.LongformerForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2155)

( config )

Parameters

-   [](#transformers.LongformerForTokenClassification.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

#### forward

[](#transformers.LongformerForTokenClassification.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2169)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonelabels: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerTokenClassifierOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerTokenClassifierOutput) or `tuple(torch.FloatTensor)`

The [LongformerForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForTokenClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, LongformerForTokenClassification
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("brad1141/Longformer-finetuned-norm")
>>> model = LongformerForTokenClassification.from_pretrained("brad1141/Longformer-finetuned-norm")

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )

>>> with torch.no_grad():
...     logits = model(**inputs).logits

>>> predicted_token_class_ids = logits.argmax(-1)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence', 'Evidence']

>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.63```

## [](#transformers.LongformerForQuestionAnswering)LongformerForQuestionAnswering

### class transformers.LongformerForQuestionAnswering

[](#transformers.LongformerForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2016)

( config )

Parameters

-   [](#transformers.LongformerForQuestionAnswering.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

#### forward

[](#transformers.LongformerForQuestionAnswering.forward)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_longformer.py#L2029)

( input\_ids: typing.Optional\[torch.Tensor\] = Noneattention\_mask: typing.Optional\[torch.Tensor\] = Noneglobal\_attention\_mask: typing.Optional\[torch.Tensor\] = Nonehead\_mask: typing.Optional\[torch.Tensor\] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor\] = Noneposition\_ids: typing.Optional\[torch.Tensor\] = Noneinputs\_embeds: typing.Optional\[torch.Tensor\] = Nonestart\_positions: typing.Optional\[torch.Tensor\] = Noneend\_positions: typing.Optional\[torch.Tensor\] = Noneoutput\_attentions: typing.Optional\[bool\] = Noneoutput\_hidden\_states: typing.Optional\[bool\] = Nonereturn\_dict: typing.Optional\[bool\] = None ) → [transformers.models.longformer.modeling\_longformer.LongformerQuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_longformer.LongformerQuestionAnsweringModelOutput) or `tuple(torch.FloatTensor)`

The [LongformerForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.LongformerForQuestionAnswering) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

```
>>> from transformers import AutoTokenizer, LongformerForQuestionAnswering
>>> import torch

>>> tokenizer = AutoTokenizer.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"]

>>> 
>>> 
>>> 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)
... )  ```

## [](#transformers.TFLongformerModel)TFLongformerModel

### class transformers.TFLongformerModel

[](#transformers.TFLongformerModel)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L1999)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerModel.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare Longformer Model outputting raw hidden-states without any specific head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

This class copies code from [TFRobertaModel](/docs/transformers/v4.30.0/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.

#### call

[](#transformers.TFLongformerModel.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2021)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonetraining: Optional\[bool\] = False )

The [TFLongformerModel](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

## [](#transformers.TFLongformerForMaskedLM)TFLongformerForMaskedLM

### class transformers.TFLongformerForMaskedLM

[](#transformers.TFLongformerForMaskedLM)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2058)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerForMaskedLM.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

Longformer Model with a `language modeling` head on top.

This model inherits from [TFPreTrainedModel](/docs/transformers/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFLongformerForMaskedLM.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2075)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMaskedLMOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMaskedLMOutput) or `tuple(tf.Tensor)`

The [TFLongformerForMaskedLM](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerForMaskedLM) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFLongformerForMaskedLM
>>> import tensorflow as tf

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

>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="tf")
>>> logits = model(**inputs).logits

>>> 
>>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
>>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)

>>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'```

```
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>> 
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)

>>> outputs = model(**inputs, labels=labels)
>>> round(float(outputs.loss), 2)
0.44```

## [](#transformers.TFLongformerForQuestionAnswering)TFLongformerForQuestionAnswering

### class transformers.TFLongformerForQuestionAnswering

[](#transformers.TFLongformerForQuestionAnswering)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2145)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerForQuestionAnswering.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFLongformerForQuestionAnswering.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2160)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonestart\_positions: np.ndarray | tf.Tensor | None = Noneend\_positions: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.models.longformer.modeling\_tf\_longformer.TFLongformerQuestionAnsweringModelOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerQuestionAnsweringModelOutput) or `tuple(tf.Tensor)`

The [TFLongformerForQuestionAnswering](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFLongformerForQuestionAnswering
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.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"

>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)

>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])

>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
' puppet'```

```
>>> 
>>> target_start_index = tf.constant([14])
>>> target_end_index = tf.constant([15])

>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = tf.math.reduce_mean(outputs.loss)
>>> round(float(loss), 2)
0.96```

## [](#transformers.TFLongformerForSequenceClassification)TFLongformerForSequenceClassification

### class transformers.TFLongformerForSequenceClassification

[](#transformers.TFLongformerForSequenceClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2300)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerForSequenceClassification.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFLongformerForSequenceClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2312)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.models.longformer.modeling\_tf\_longformer.TFLongformerSequenceClassifierOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerSequenceClassifierOutput) or `tuple(tf.Tensor)`

The [TFLongformerForSequenceClassification](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFLongformerForSequenceClassification
>>> import tensorflow as tf

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

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

>>> logits = model(**inputs).logits

>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])```

```
>>> 
>>> num_labels = len(model.config.id2label)
>>> model = TFLongformerForSequenceClassification.from_pretrained("allenai/longformer-base-4096", num_labels=num_labels)

>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss```

## [](#transformers.TFLongformerForTokenClassification)TFLongformerForTokenClassification

### class transformers.TFLongformerForTokenClassification

[](#transformers.TFLongformerForTokenClassification)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2517)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerForTokenClassification.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFLongformerForTokenClassification.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2532)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: Optional\[Union\[np.array, tf.Tensor\]\] = Nonetraining: Optional\[bool\] = False ) → [transformers.models.longformer.modeling\_tf\_longformer.TFLongformerTokenClassifierOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerTokenClassifierOutput) or `tuple(tf.Tensor)`

The [TFLongformerForTokenClassification](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerForTokenClassification) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFLongformerForTokenClassification
>>> import tensorflow as tf

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

>>> inputs = tokenizer(
...     "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )

>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)

>>> 
>>> 
>>> 
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]```

```
>>> labels = predicted_token_class_ids
>>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)```

## [](#transformers.TFLongformerForMultipleChoice)TFLongformerForMultipleChoice

### class transformers.TFLongformerForMultipleChoice

[](#transformers.TFLongformerForMultipleChoice)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2403)

( \*args\*\*kwargs )

Parameters

-   [](#transformers.TFLongformerForMultipleChoice.config)**config** ([LongformerConfig](/docs/transformers/v4.30.0/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/v4.30.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

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/v4.30.0/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.

TensorFlow models and layers in `transformers` accept two formats as input:

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

The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should “just work” for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:

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

Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don’t need to worry about any of this, as you can just pass inputs like you would to any other Python function!

#### call

[](#transformers.TFLongformerForMultipleChoice.call)[< source \>](https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/longformer/modeling_tf_longformer.py#L2424)

( input\_ids: TFModelInputType | None = Noneattention\_mask: np.ndarray | tf.Tensor | None = Nonehead\_mask: np.ndarray | tf.Tensor | None = Nonetoken\_type\_ids: np.ndarray | tf.Tensor | None = Noneposition\_ids: np.ndarray | tf.Tensor | None = Noneglobal\_attention\_mask: np.ndarray | tf.Tensor | None = Noneinputs\_embeds: np.ndarray | tf.Tensor | None = Noneoutput\_attentions: Optional\[bool\] = Noneoutput\_hidden\_states: Optional\[bool\] = Nonereturn\_dict: Optional\[bool\] = Nonelabels: np.ndarray | tf.Tensor | None = Nonetraining: Optional\[bool\] = False ) → [transformers.models.longformer.modeling\_tf\_longformer.TFLongformerMultipleChoiceModelOutput](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.models.longformer.modeling_tf_longformer.TFLongformerMultipleChoiceModelOutput) or `tuple(tf.Tensor)`

The [TFLongformerForMultipleChoice](/docs/transformers/v4.30.0/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

```
>>> from transformers import AutoTokenizer, TFLongformerForMultipleChoice
>>> import tensorflow as tf

>>> tokenizer = AutoTokenizer.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 = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)  

>>> 
>>> logits = outputs.logits```