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*This model was released on 2020-06-12 and added to Hugging Face Transformers on 2023-06-20 and contributed by [yjernite](https://huggingface.co/yjernite).*

> [!WARNING]
> 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`.

# RetriBERT

[RetriBERT](https://yjernite.github.io/lfqa.html) is a compact model designed for dense semantic indexing, utilizing either a single or a pair of BERT encoders with a reduced-dimensional projection layer. This architecture enables efficient retrieval of relevant passages by encoding text into dense vectors. It was developed to facilitate open-domain long-form question answering (LFQA) tasks, particularly when training data is limited. By leveraging the ELI5 dataset, RetriBERT demonstrates how dense retrieval systems can be trained without extensive supervision or task-specific pretraining, making such models more accessible.

## RetriBertConfig

[[autodoc]] RetriBertConfig

## RetriBertTokenizer

[[autodoc]] RetriBertTokenizer

## RetriBertTokenizerFast

[[autodoc]] RetriBertTokenizerFast

## RetriBertModel

[[autodoc]] RetriBertModel
    - forward