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# Funnel Transformer

## Overview

The Funnel Transformer model was proposed in the paper [Funnel-Transformer: Filtering out Sequential Redundancy for
Efficient Language Processing](https://arxiv.org/abs/2006.03236). It is a bidirectional transformer model, like
BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
(CNN) in computer vision.

The abstract from the paper is the following:

*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
comprehension.*

Tips:

- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers.
  The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
  directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
  tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
  sequence length as the input.
- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should be
  used for [`FunnelModel`], [`FunnelForPreTraining`],
  [`FunnelForMaskedLM`], [`FunnelForTokenClassification`] and
  class:*~transformers.FunnelForQuestionAnswering*. The second ones should be used for
  [`FunnelBaseModel`], [`FunnelForSequenceClassification`] and
  [`FunnelForMultipleChoice`].

This model was contributed by [sgugger](https://huggingface.co/sgugger). The original code can be found [here](https://github.com/laiguokun/Funnel-Transformer).


## FunnelConfig

[[autodoc]] FunnelConfig

## FunnelTokenizer

[[autodoc]] FunnelTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## FunnelTokenizerFast

[[autodoc]] FunnelTokenizerFast

## Funnel specific outputs

[[autodoc]] models.funnel.modeling_funnel.FunnelForPreTrainingOutput

[[autodoc]] models.funnel.modeling_tf_funnel.TFFunnelForPreTrainingOutput

## FunnelBaseModel

[[autodoc]] FunnelBaseModel
    - forward

## FunnelModel

[[autodoc]] FunnelModel
    - forward

## FunnelModelForPreTraining

[[autodoc]] FunnelForPreTraining
    - forward

## FunnelForMaskedLM

[[autodoc]] FunnelForMaskedLM
    - forward

## FunnelForSequenceClassification

[[autodoc]] FunnelForSequenceClassification
    - forward

## FunnelForMultipleChoice

[[autodoc]] FunnelForMultipleChoice
    - forward

## FunnelForTokenClassification

[[autodoc]] FunnelForTokenClassification
    - forward

## FunnelForQuestionAnswering

[[autodoc]] FunnelForQuestionAnswering
    - forward

## TFFunnelBaseModel

[[autodoc]] TFFunnelBaseModel
    - call

## TFFunnelModel

[[autodoc]] TFFunnelModel
    - call

## TFFunnelModelForPreTraining

[[autodoc]] TFFunnelForPreTraining
    - call

## TFFunnelForMaskedLM

[[autodoc]] TFFunnelForMaskedLM
    - call

## TFFunnelForSequenceClassification

[[autodoc]] TFFunnelForSequenceClassification
    - call

## TFFunnelForMultipleChoice

[[autodoc]] TFFunnelForMultipleChoice
    - call

## TFFunnelForTokenClassification

[[autodoc]] TFFunnelForTokenClassification
    - call

## TFFunnelForQuestionAnswering

[[autodoc]] TFFunnelForQuestionAnswering
    - call
