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transformers/docs/source/en/model_doc/vision-text-dual-encoder.md

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+*This model was released on 2021-11-15 and added to Hugging Face Transformers on 2021-11-30.*
+
+# VisionTextDualEncoder
+
+<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">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
+any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
+to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
+downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
+training and then can be used for zero-shot vision tasks such image-classification or retrieval.
+
+In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://huggingface.co/papers/2111.07991) it is shown how
+leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
+new zero-shot vision tasks such as image classification or retrieval.
+
+## VisionTextDualEncoderConfig
+
+[[autodoc]] VisionTextDualEncoderConfig
+
+## VisionTextDualEncoderProcessor
+
+[[autodoc]] VisionTextDualEncoderProcessor
+
+## VisionTextDualEncoderModel
+
+[[autodoc]] VisionTextDualEncoderModel
+    - forward