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README.md
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base_model:
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- google/vit-base-patch16-224
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- ytu-ce-cosmos/turkish-gpt2
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base_model:
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- google/vit-base-patch16-224
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- ytu-ce-cosmos/turkish-gpt2
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---
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# vit-base-patch16-224-turkish-gpt2
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This vision encoder-decoder model utilizes the [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) as the encoder and [ytu-ce-cosmos/turkish-gpt2](https://huggingface.co/ytu-ce-cosmos/turkish-gpt2) as the decoder, and it has been fine-tuned on the [flickr8k-turkish](https://huggingface.co/datasets/atasoglu/flickr8k-turkish) dataset to generate image captions in Turkish.
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## Usage
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```py
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import torch
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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
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from PIL import Image
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_id = "atasoglu/vit-base-patch16-224-turkish-gpt2"
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img = Image.open("example.jpg")
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feature_extractor = ViTImageProcessor.from_pretrained(model_id)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = VisionEncoderDecoderModel.from_pretrained(model_id)
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model.to(device)
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features = feature_extractor(images=[img], return_tensors="pt")
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pixel_values = features.pixel_values.to(device)
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generated_captions = tokenizer.batch_decode(
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model.generate(pixel_values, max_new_tokens=128),
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skip_special_tokens=True,
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)
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print(generated_captions)
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```
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