Instructions to use Vailla-Rohit/bart-base-finetuned-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vailla-Rohit/bart-base-finetuned-samsum with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vailla-Rohit/bart-base-finetuned-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("Vailla-Rohit/bart-base-finetuned-samsum", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Vailla-Rohit/bart-base-finetuned-samsum: direct link, hf CLI and curl.
- Browser
- Download file 558 MB
-
https://huggingface.co/Vailla-Rohit/bart-base-finetuned-samsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Vailla-Rohit/bart-base-finetuned-samsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Vailla-Rohit/bart-base-finetuned-samsum/resolve/main/pytorch_model.bin
558 MB
- Xet hash:
- 349eadb3339b322193c26c4a21d88a327788b274c269d582faf29ead04bd1700
- Size of remote file:
- 558 MB
- SHA256:
- 206c1c487010b2a63f2b2f5a07e0df573990d613ebe6ca77efd2ec8bd4d4b2ab
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