GLM OCR Samaritan

--- library_name: transformers license: mit base_model: zai-org/GLM-OCR tags: - ocr - samaritan - ancient-languages - vision - image-to-text - glm-ocr pipeline_tag: image-to-text language: - smp - sam datasets: - private --- # GLM-OCR <div align="center"> <img src=https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/logo.svg width="40%"/> </div> <p align="center"> 👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/wechat.jpg" target="_blank">WeChat</a> and <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community <br> 📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a> </p> This model is a fine-tuned version of [GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) on the sam_44_mss_pango dataset. It achieves the following results on the evaluation set: - Loss: 0.2622 ## Model description The model was finetuned on 44 medieval Samaritan Hebrew/Aramaic manuscripts. ## Training and evaluation data Trained on a private dataset ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:-----:|:---------------:| | 1.5390 | 0.7594 | 2000 | 1.4977 | | 0.5723 | 1.5187 | 4000 | 0.5357 | | 0.3869 | 2.2779 | 6000 | 0.3955 | | 0.3180 | 3.0372 | 8000 | 0.3480 | | 0.2683 | 3.7966 | 10000 | 0.3113 | | 0.2507 | 4.5559 | 12000 | 0.2898 | | 0.1941 | 5.3151 | 14000 | 0.2783 | | 0.1738 | 6.0744 | 16000 | 0.2722 | | 0.1792 | 6.8338 | 18000 | 0.2622 | | 0.1260 | 7.5931 | 20000 | 0.2662 | | 0.0956 | 8.3523 | 22000 | 0.2667 | | 0.1271 | 9.1116 | 24000 | 0.2672 | ### Framework versions - PEFT 0.18.1 - Transformers 5.1.0 - Pytorch 2.10.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2