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*This model was released on 2020-06-12 and added to Hugging Face Transformers on 2023-06-20.*

# RetriBERT

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

<Tip warning={true}>

This model is in maintenance mode only, so we won't accept any new PRs changing its code.

If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.

</Tip>

## Overview

The [RetriBERT](https://huggingface.co/yjernite/retribert-base-uncased/tree/main) model was proposed in the blog post [Explain Anything Like I'm Five: A Model for Open Domain Long Form
Question Answering](https://yjernite.github.io/lfqa.html). RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.

This model was contributed by [yjernite](https://huggingface.co/yjernite). Code to train and use the model can be
found [here](https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation).

## RetriBertConfig

[[autodoc]] RetriBertConfig

## RetriBertTokenizer

[[autodoc]] RetriBertTokenizer

## RetriBertTokenizerFast

[[autodoc]] RetriBertTokenizerFast

## RetriBertModel

[[autodoc]] RetriBertModel
    - forward
