Multilingual-CLIP

OpenAI CLIP text encoders for any language

Colab Notebook · Pre-trained Models · Report Bug

Overview

OpenAI recently released the paper Learning Transferable Visual Models From Natural Language Supervision in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representations using a contrastive learning objective. CLIP consists of two separate models, a visual encoder and a text encoder. These were trained on a wooping 400 Million images and corresponding captions. OpenAI has since released a set of their smaller CLIP models, which can be found on the official CLIP Github.

We propose a fine-tuning to replace the original English text encoder with a pre-trained text model in any language. This method makes it possible to adapt the powerful CLIP model to any language in roughly 24 GPU hours.

This repository contains

Pytorch inference code
Tensorflow training code
Pre-trained CLIP-Text encoders for multiple languages
Training data and pre-computed CLIP text encodings for a large porton of the the image captions of GCC + MSCOCO + VizWiz

Requirements

While it is possible that other versions works equally fine, we have worked with the following:

Python = 3.6.9
Transformers = 4.1.1
Model Weights

Usage

Download CLIP Model

$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ pip install git+https://github.com/openai/CLIP.git

Replace cudatoolkit=11.0 above with the appropriate CUDA version on your machine or cpuonly when installing on a machine without a GPU. For more information please see the official CLIP repostitory.

Download Linear Weights

# Linear Model Weights
$ bash get-weights.sh

Inference

from src import multilingual_clip

print(multilingual_clip.AVAILABLE_MODELS.keys())

model = multilingual_clip.load_model('M-BERT-Distil-40')

embeddings = model(['Älgen är skogens konung!', 'Wie leben Eisbären in der Antarktis?', 'Вы знали, что все белые медведи левши?'])
print(embeddings.shape)
# Yields: torch.Size([3, 640])

For a more elaborate example, comparing the textual embeddings to the CLIP image embeddings see this colab notebook.

Pre-trained Models

Every text encoder is a Huggingface available transformer, with an additional linear layer on top. Neither of the models have been extensively tested, but for more information and qualitative test results for a specific model, click the Model Name to see its model card.

*** Make sure to update to the most recent version of the repostitory when downloading a new model, and re-run the shell script to download the Linear Weights. ***

Name	Model Base	Vision Model	Pre-trained Languages	Target Languages	#Parameters
Multilingual
M-BERT Distil 40	M-BERT Distil	RN50x4	101 Languages	40 Languages	66 M
M-BERT Base 69	M-BERT Base	RN50x4	101 Languages	68 Languages	110 M
M-BERT Base ViT-B	M-BERT Base	ViT-B/32	101 Languages	68 Languages	110 M
Monolingual
Swe-CLIP 500k	KB-BERT	RN50x4	Swedish	Swedish	110 M
Swe-CLIP 2M	KB-BERT	RN50x4	Swedish	Swedish	110 M

Training a new model

This folder contains the code used for training the above models. If you wsh to train your own model you must do the following things:

Prepare a set of translated sentence pairs from English -> Your Language(s)
Compute regular CLIP-Text embeddings for the English sentences.
Edit Training.py to load your data.
Train a new CLIP-Text encoder via Teacher Learning

Pre-computed CLIP Embeddings & Translaton Data

This Google Drive folder contains both pre-computed CLIP-Text Embeddings for a large porton of the the image captions of GCC + MSCOCO + VizWiz.

The Google Drive folder also contains the translation data used to train the currently available models. Good Luck

Contribution

If you have trained a CLIP Text encoder specific to your language, or another model covering a language not supported here, Please feel free to contact us and we will either upload your model and credit you, or simply link to your already uploaded model.

Contact

If you have questions regarding the code or otherwise related to this Github page, please open an issue.

For other purposes, feel free to contact me directly at: [email protected]

Acknowledgements

License

Distributed under the MIT License. See LICENSE for more information.

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