Create README.md
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README.md
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---
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license: mit
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language:
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- ar
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- kn
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- ar
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- ka
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- af
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- kk
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- am
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- km
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- ar
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- ky
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- ar
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- ko
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- as
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- lo
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- az
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- ml
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- az
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- mr
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- be
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- mk
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- bn
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- my
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- bs
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- nl
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- bg
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- 'no'
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- ca
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- 'no'
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- cs
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- ne
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- ku
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- pl
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- cy
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- pt
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- da
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- ro
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- de
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- ru
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- el
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- sa
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- en
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- si
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- eo
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- sk
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- et
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- sl
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- eu
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- sd
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- fi
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- so
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- fr
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- es
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- gd
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- sr
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- ga
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- su
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- gl
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- sv
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- gu
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- sw
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- ha
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- ta
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- he
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- te
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- hi
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- th
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- hr
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- tr
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- hu
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- ug
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- hy
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- uk
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- id
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- ur
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- is
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- vi
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- it
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- xh
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- jv
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- zh
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- ja
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---
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## Model Summary
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MEXMA-SigLIP is a model that combines the [MEXMA](https://huggingface.co/facebook/MEXMA) multilingual text encoder and an image encoder from the
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[SigLIP](https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384) model. This allows us to get a high-performance CLIP model for 80 languages.
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MEXMA-SigLIP sets state-of-the-art on the [Crossmodal-3600](https://google.github.io/crossmodal-3600/) dataset across commercial use-friendly models.
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## How to use
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```
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from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
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from PIL import Image
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import requests
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import torch
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model = AutoModel.from_pretrained("visheratin/mexma-siglip", torch_dtype=torch.bfloat16, trust_remote_code=True, optimized=True).to("cuda")
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tokenizer = AutoTokenizer.from_pretrained("visheratin/mexma-siglip")
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processor = AutoImageProcessor.from_pretrained("visheratin/mexma-siglip")
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img = Image.open(requests.get("https://static.independent.co.uk/s3fs-public/thumbnails/image/2014/03/25/12/eiffel.jpg", stream=True).raw)
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| 107 |
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img = processor(images=img, return_tensors="pt")["pixel_values"]
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| 108 |
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img = img.to(torch.bfloat16).to("cuda")
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| 109 |
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with torch.inference_mode():
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text = tokenizer(["кошка", "a dog", "एफिल टॉवर"], return_tensors="pt", padding=True).to("cuda")
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| 111 |
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image_logits, text_logits = model.get_logits(text["input_ids"], text["attention_mask"], img)
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| 112 |
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probs = image_logits.softmax(dim=-1)
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| 113 |
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print(probs)
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| 114 |
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```
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## Acknowledgements
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| 117 |
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| 118 |
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I thank [ML Collective](https://mlcollective.org/) and [Lambda](https://lambdalabs.com/) for providing compute resources to train the model.
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