<br />
<p align="center">
  <h1 align="center">M-BERT Base ViT-B</h1>
  
  <p align="center">  
    <a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/M-BERT%20Base%20ViT-B">Github Model Card</a>
  </p>
</p>

## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the [Multilingual-CLIP Github](https://github.com/FreddeFrallan/Multilingual-CLIP).

Once this is done, you can load and use the model with the following code
```python
from src import multilingual_clip

model = multilingual_clip.load_model('M-BERT-Base-ViT')
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])
```

<!-- ABOUT THE PROJECT -->
## About
A [BERT-base-multilingual](https://huggingface.co/bert-base-multilingual-cased) tuned to match the embedding space for [69 languages](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/Model%20Cards/M-BERT%20Base%2069/Fine-Tune-Languages.md), to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br>
A full list of the 100 languages used during pre-training can be found [here](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages), and a list of the 4069languages used during fine-tuning can be found in [SupportedLanguages.md](https://github.com/FreddeFrallan/Multilingual-CLIP/blob/main/Model%20Cards/M-BERT%20Base%2069/Fine-Tune-Languages.md).

Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of [GCC](https://ai.google.com/research/ConceptualCaptions/) + [MSCOCO](https://cocodataset.org/#home) + [VizWiz](https://vizwiz.org/tasks-and-datasets/image-captioning/), and translating them into the corresponding language.
All translation was done using the [AWS translate service](https://aws.amazon.com/translate/), the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.

{}
transformers pytorch tf jax bert feature-extraction endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #region-us 

<br />
<p align="center">
  <h1 align="center">M-BERT Base ViT-B</h1>
  
  <p align="center">  
    <a href="URL Model Card</a>
  </p>
</p>

## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.

Once this is done, you can load and use the model with the following code



## About
A BERT-base-multilingual tuned to match the embedding space for 69 languages, to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br>
A full list of the 100 languages used during pre-training can be found here, and a list of the 4069languages used during fine-tuning can be found in URL.

Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of GCC + MSCOCO + VizWiz, and translating them into the corresponding language.
All translation was done using the AWS translate service, the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.

## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.

Once this is done, you can load and use the model with the following code ## About
A BERT-base-multilingual tuned to match the embedding space for 69 languages, to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br>
A full list of the 100 languages used during pre-training can be found here, and a list of the 4069languages used during fine-tuning can be found in URL.

Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of GCC + MSCOCO + VizWiz, and translating them into the corresponding language.
All translation was done using the AWS translate service, the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #region-us 
 ## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.

Once this is done, you can load and use the model with the following code ## About
A BERT-base-multilingual tuned to match the embedding space for 69 languages, to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br>
A full list of the 100 languages used during pre-training can be found here, and a list of the 4069languages used during fine-tuning can be found in URL.

Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of GCC + MSCOCO + VizWiz, and translating them into the corresponding language.
All translation was done using the AWS translate service, the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.
TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #endpoints_compatible #region-us 
## Usage
To use this model along with the original CLIP vision encoder you need to download the code and additional linear weights from the Multilingual-CLIP Github.

Once this is done, you can load and use the model with the following code## About
A BERT-base-multilingual tuned to match the embedding space for 69 languages, to the embedding space of the CLIP text encoder which accompanies the ViT-B/32 vision encoder. <br>
A full list of the 100 languages used during pre-training can be found here, and a list of the 4069languages used during fine-tuning can be found in URL.

Training data pairs was generated by sampling 40k sentences for each language from the combined descriptions of GCC + MSCOCO + VizWiz, and translating them into the corresponding language.
All translation was done using the AWS translate service, the quality of these translations have currently not been analyzed, but one can assume the quality varies between the 69 languages.