Instructions to use vaibhavagg303/bart_for_summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vaibhavagg303/bart_for_summarization with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("vaibhavagg303/bart_for_summarization") model = AutoModelForSeq2SeqLM.from_pretrained("vaibhavagg303/bart_for_summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vaibhavagg303/bart_for_summarization: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/vaibhavagg303/bart_for_summarization/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vaibhavagg303/bart_for_summarization/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vaibhavagg303/bart_for_summarization/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- b96ced8da120815cec2863ed71f24574c712f916309fde3b53bfadc7a0397fb9
- Size of remote file:
- 1.63 GB
- SHA256:
- b0326c302e0b694b4a5fb3d1ec4b5b1aea8ccba5ef26bbe1a017f960da5a9214
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