Instructions to use razent/SciFive-large-Pubmed_PMC-MedNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razent/SciFive-large-Pubmed_PMC-MedNLI with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-Pubmed_PMC-MedNLI") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-Pubmed_PMC-MedNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from razent/SciFive-large-Pubmed_PMC-MedNLI: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/razent/SciFive-large-Pubmed_PMC-MedNLI/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://razent/SciFive-large-Pubmed_PMC-MedNLI/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/razent/SciFive-large-Pubmed_PMC-MedNLI/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- 6ae8505e4bcffbfadd0da40e109c8dfcf43a58a285a387e7485adf23a255db4f
- Size of remote file:
- 2.95 GB
- SHA256:
- 9a348b5c18206449576ce89e97932fa62777db440dd1194407e90fe85ca78eae
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