Instructions to use ashokurlana/mBART-TeSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ashokurlana/mBART-TeSum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ashokurlana/mBART-TeSum") model = AutoModelForSeq2SeqLM.from_pretrained("ashokurlana/mBART-TeSum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ashokurlana/mBART-TeSum: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ashokurlana/mBART-TeSum/resolve/main/tokenizer.json
- Command line
-
hf download hf://ashokurlana/mBART-TeSum/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ashokurlana/mBART-TeSum/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- bf5b6990ce7464a0c5d006a2ae54d4100b85f59bd791607c010bcf9d518228cc
- Size of remote file:
- 17.1 MB
- SHA256:
- 66b66d4ba6405e4a2e7de3014c17574b08264e7abcc2e244e47e0d03610a530d
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