Instructions to use Ransaka/mBart-en-sin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ransaka/mBart-en-sin with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ransaka/mBart-en-sin") model = AutoModelForSeq2SeqLM.from_pretrained("Ransaka/mBart-en-sin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Ransaka/mBart-en-sin: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Ransaka/mBart-en-sin/resolve/main/tokenizer.json
- Command line
-
hf download hf://Ransaka/mBart-en-sin/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Ransaka/mBart-en-sin/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- af1848dee3e6b7b47ff17b789ec3c26b9cf45d693198cdb39e1eba87230e168e
- Size of remote file:
- 17.1 MB
- SHA256:
- 059114413b7a26b6cdafc34e0edbcf136dce2b08e0b7251a97fdc743e3ed40fb
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