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
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README.md
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license: apache-2.0
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---
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license: apache-2.0
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datasets:
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- sinhala-nlp/NSINA
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language:
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- si
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metrics:
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- accuracy
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base_model:
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- google/byt5-small
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pipeline_tag: token-classification
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library_name: transformers
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tags:
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- legal
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- finance
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---
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