mevsg
/

qwen-for-jawi-v1

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags: [OCR]
 ---
+# Model Card for Model qwen-for-jawi-v1
+## Model Description
+This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct) specialized for Optical Character Recognition (OCR) of historical Malay texts written in Jawi script (Arabic script adapted for Malay language).
+### Model Architecture
+- **Base Model**: Qwen2-VL-2B-Instruct
+- **Model Type**: Vision-Language Model
+- **Parameters**: 2 billion
+- **Language(s)**: Malay (Jawi script)
+## Intended Use
+### Primary Intended Uses
+- OCR for historical Malay manuscripts written in Jawi script
+- Digital preservation of Malay cultural heritage
+- Enabling computational analysis of historical Malay texts
+### Out-of-Scope Uses
+- General Arabic text recognition
+- Modern Malay text processing
+- Real-time OCR applications
+## Training Data
+### Dataset Description
+This was trained and evaluated using
 ### Training Procedure
+- Hardware used: 1 x H100
+- Training time: 6 hours
+## Performance and Limitations
+### Performance Metrics
+- Character Error Rate (CER): 8.66
+- Word Error Rate (WER): 25.50
+### Comparison with Other Models
+We compared this model with https://github.com/VikParuchuri/surya, which reports high accuracy reates for Arabic, but performs poorly oun our Jawi data:
+- Character Error Rate (CER): 70.89%
+- Word Error Rate (WER): 91.73%
+## How to Use
+```python
+# Example code for loading and using the model
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+import torch
+from qwen_vl_utils import process_vision_info
+from PIL import Image
+model_name = 'mevsg/qwen-for-jawi-v1'
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,  # Use the appropriate torch dtype if needed
+    device_map='auto'            # Optional: automatically allocate model layers across devices
+)
+# Load the processor from Hugging Face Hub
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+# Add example usage code
+image_path = 'path/to/image'
+image = Image.open(image_path).convert('RGB')
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": image,
+            },
+            {"type": "text", "text": "Convert this image to text"},
+        ],
+    }
+]
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+## Citation
+```bibtex
+@misc{qwen-for-jawi-v1,
+  title     = {Qwen for Jawi v1: a model for Jawi OCR},
+  author    = {[Miguel Escobar Varela]},
+  year      = {2024},
+  publisher = {HuggingFace},
+  url       = {[https://huggingface.co/mevsg/qwen-for-Jawi-v1]},
+  note      = {Model created at National University of Singapore }
+}
+```
+## Acknowledgements
+Special thanks to [William Mattingly](https://huggingface.co/wjbmattingly), whose finetuning script served as the base for our finetuning approach: https://github.com/wjbmattingly/qwen2-vl-finetune-huggingface