Instructions to use AbeHou/SemStamp-booksum-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbeHou/SemStamp-booksum-sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AbeHou/SemStamp-booksum-sbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AbeHou/SemStamp-booksum-sbert") model = AutoModel.from_pretrained("AbeHou/SemStamp-booksum-sbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AbeHou/SemStamp-booksum-sbert: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/AbeHou/SemStamp-booksum-sbert/resolve/main/training_args.bin
- Command line
-
hf download hf://AbeHou/SemStamp-booksum-sbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AbeHou/SemStamp-booksum-sbert/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- acbc313aa785a13b6ca7c58e1716863f2a32ba4f5a68c1a41427381f6ae18109
- Size of remote file:
- 4.86 kB
- SHA256:
- 2294fa0ae54957be0fcddbdaa1dbff5555a8d91cee690069991543dd7679008b
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