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language: sv |
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<p align="center"> |
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<h1 align="center">Swe-CLIP 500k</h1> |
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<p align="center"> |
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<a href="https://github.com/FreddeFrallan/Multilingual-CLIP/tree/main/Model%20Cards/Swe-CLIP%20500k">Github Model Card</a> |
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</p> |
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## Usage |
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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). |
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Once this is done, you can load and use the model with the following code |
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```python |
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from src import multilingual_clip |
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model = multilingual_clip.load_model('Swe-CLIP-500k') |
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embeddings = model(['Älgen är skogens konung!', 'Alla isbjörnar är vänsterhänta']) |
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print(embeddings.shape) |
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# Yields: torch.Size([2, 640]) |
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``` |
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<!-- ABOUT THE PROJECT --> |
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## About |
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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. <br> |
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Training data pairs was generated by sampling 500k 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. |
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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/). |
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