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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vector2003/sinhala-ocr-postcorrection-byt5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from vector2003/sinhala-ocr-postcorrection-byt5: direct link, hf CLI and curl.
- Browser
- Download file 210 Bytes
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https://huggingface.co/vector2003/sinhala-ocr-postcorrection-byt5/resolve/main/README.md
- Command line
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hf download hf://vector2003/sinhala-ocr-postcorrection-byt5/README.md
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curl -L -o README.md https://huggingface.co/vector2003/sinhala-ocr-postcorrection-byt5/resolve/main/README.md
210 Bytes
| 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 | |