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New model update

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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ tags:
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+ - vision
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+ - ocr
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+ - trocr
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+ - pytorch
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+ license: apache-2.0
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+ datasets:
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+ - custom-captcha-dataset
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+ metrics:
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+ - cer
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+ model_name: anuashok/ocr-captcha-v3
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+ base_model:
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+ - microsoft/trocr-base-printed
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+ ---
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+
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+ # anuashok/ocr-captcha-v3
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+
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+ This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on Captchas of the type shown below
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6569b4be1bac1166939f86b2/ncjFKGf86bk18ON9B9mYZ.png)
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+
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6569b4be1bac1166939f86b2/bKLXrLjpjpIwPHaURhjN2.png)
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+
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+
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+ ## Training Summary
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+
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+ - **CER (Character Error Rate)**: 0.01394585726004922
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+ - **Hyperparameters**:
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+ - **Learning Rate**: 1.5078922700531405e-05
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+ - **Batch Size**: 16
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+ - **Num Epochs**: 7
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+ - **Warmup Ratio**: 0.14813004670666596
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+ - **Weight Decay**: 0.017176551931326833
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+ - **Num Beams**: 2
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+ - **Length Penalty**: 1.3612823161368288
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import VisionEncoderDecoderModel, TrOCRProcessor
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+ import torch
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+ from PIL import Image
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+
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+ # Load model and processor
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+ processor = TrOCRProcessor.from_pretrained("anuashok/ocr-captcha-v3")
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+ model = VisionEncoderDecoderModel.from_pretrained("anuashok/ocr-captcha-v3")
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+
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+ # Load image
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+ image = Image.open('path_to_your_image.jpg').convert("RGB")
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+ # Load and preprocess image for display
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+ image = Image.open(image_path).convert("RGBA")
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+ # Create white background
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+ background = Image.new("RGBA", image.size, (255, 255, 255))
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+ combined = Image.alpha_composite(background, image).convert("RGB")
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+
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+ # Prepare image
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+ pixel_values = processor(combined, return_tensors="pt").pixel_values
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+
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+ # Generate text
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+ generated_ids = model.generate(pixel_values)
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+ generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ print(generated_text)
config.json ADDED
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+ {
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+ "VisionEncoderDecoderModel"
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+ ],
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