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
metadata
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