Update README.md

README.md

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 <!-- 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 Res50x4 vision encoder. <br>
+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.