Instructions to use UGARIT/grc-alignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UGARIT/grc-alignment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UGARIT/grc-alignment")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UGARIT/grc-alignment") model = AutoModelForMaskedLM.from_pretrained("UGARIT/grc-alignment") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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| Languages | Alignment Error Rate |
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| GRC-ENG | 19.73% (IterMax) |
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The gold standard datasets are available on [Github](https://github.com/UgaritAlignment/Alignment-Gold-Standards).
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| Languages | Alignment Error Rate |
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| GRC-ENG | 19.73% (IterMax) |
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| GRC-POR | 23.91% (IterMax) |
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| GRC-LAT | 10.60% (ArgMax) |
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The gold standard datasets are available on [Github](https://github.com/UgaritAlignment/Alignment-Gold-Standards).
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