Instructions to use VTaPo/mBart_en_vi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VTaPo/mBart_en_vi with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("VTaPo/mBart_en_vi") model = AutoModelForSeq2SeqLM.from_pretrained("VTaPo/mBart_en_vi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from VTaPo/mBart_en_vi: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/VTaPo/mBart_en_vi/resolve/main/tokenizer.json
- Command line
-
hf download hf://VTaPo/mBart_en_vi/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/VTaPo/mBart_en_vi/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- d10efc508868860a37107fb69d55c6794f119957a8b16c22ad1c1c673b4a9b33
- Size of remote file:
- 17.1 MB
- SHA256:
- e1e1107d01eaf32d6a7ea2c10460e44df074aa12d2babf09f6ff04b834989b60
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