Swe-CLIP 2M

Github Model Card

## 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('Swe-CLIP-500k') embeddings = model(['Älgen är skogens konung!', 'Alla isbjörnar är vänsterhänta']) print(embeddings.shape) # Yields: torch.Size([2, 640]) ``` ## About A [KB/Bert-Swedish-Cased](https://huggingface.co./KB/bert-base-swedish-cased) tuned to match the embedding space of the CLIP text encoder which accompanies the Res50x4 vision encoder.
Training data pairs was generated by sampling 2 Million sentences 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 Swedish. All translation was done using the [Huggingface Opus Model](https://huggingface.co./Helsinki-NLP/opus-mt-en-sv), which seemingly procudes higher quality translations than relying on the [AWS translate service](https://aws.amazon.com/translate/).