Instructions to use transZ/M2M_Vi_Ba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use transZ/M2M_Vi_Ba with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="transZ/M2M_Vi_Ba")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("transZ/M2M_Vi_Ba") model = AutoModelForSeq2SeqLM.from_pretrained("transZ/M2M_Vi_Ba") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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# How to run the model
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```python
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from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
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model = M2M100ForConditionalGeneration.from_pretrained("transZ/M2M_Vi_Ba")
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tokenizer = M2M100Tokenizer.from_pretrained("transZ/M2M_Vi_Ba")
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tokenizer.src_lang = "vi"
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vi_text = "Hôm nay ba đi chợ."
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encoded_vi = tokenizer(vi_text, return_tensors="pt")
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generated_tokens = model.generate(**encoded_vi, forced_bos_token_id=tokenizer.get_lang_id("ba"))
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translate = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
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print(translate)
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```
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