Instructions to use edwinlaw/opus-mt-cantonese-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edwinlaw/opus-mt-cantonese-v2 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("edwinlaw/opus-mt-cantonese-v2") model = AutoModelForSeq2SeqLM.from_pretrained("edwinlaw/opus-mt-cantonese-v2") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# opus-mt-cantonese-v2
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh)
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It achieves the following results on the evaluation set:
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- Loss: 1.8948
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- Bleu: 4.1931
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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# opus-mt-cantonese-v2
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsinki-NLP/opus-mt-en-zh).
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It achieves the following results on the evaluation set:
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- Loss: 1.8948
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- Bleu: 4.1931
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## Model description
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This model translates English into Cantonese.
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## Intended uses & limitations
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Translations produced are for experimental purposes. Correctness is not guaranteed. Use at your own risk.
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## Training and evaluation data
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Trained with Cantonese sentences with English translations:
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- 6280 from Tatoeba.
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- 1232 from CantoDict.
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## Training procedure
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80% training / 20% validation. 20 epochs.
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### Training hyperparameters
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The following hyperparameters were used during training:
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