Instructions to use kaan84/sinhala-print-trocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kaan84/sinhala-print-trocr with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="kaan84/sinhala-print-trocr")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kaan84/sinhala-print-trocr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Scripts | |
| Add version-controlled programs here during Phase 1: | |
| - `make_splits.py` β deterministic synthetic train/validation indices | |
| - `prepare_real_lines.py` β page-to-line benchmark construction | |
| - `train.py` β three-seed fine-tuning entry point | |
| - `evaluate.py` β raw and normalized CER/WER | |
| - `bootstrap.py` β paired bootstrap confidence intervals | |
| - `run_tesseract.py` β Tesseract baseline | |
| - `run_surya.py` β optional Surya baseline | |
| - `validate_release.py` β verify required files, revisions, metrics, and license gate | |
| Each script must expose `--help`, accept revisions and seeds explicitly, and write a machine-readable manifest. | |