Instructions to use vector2003/sinhala-ocr-postcorrection-byt5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vector2003/sinhala-ocr-postcorrection-byt5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vector2003/sinhala-ocr-postcorrection-byt5")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vector2003/sinhala-ocr-postcorrection-byt5", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - sinhala-nlp/NSINA | |
| language: | |
| - si | |
| metrics: | |
| - accuracy | |
| base_model: | |
| - google/byt5-small | |
| pipeline_tag: token-classification | |
| library_name: transformers | |
| tags: | |
| - legal | |
| - finance | |