Instructions to use dtorber/BioNLP-conditional-prompting-decoder-PLOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/BioNLP-conditional-prompting-decoder-PLOS with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("dtorber/BioNLP-conditional-prompting-decoder-PLOS") model = AutoModelForSeq2SeqLM.from_pretrained("dtorber/BioNLP-conditional-prompting-decoder-PLOS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df571c4472ceb799f7f0f30e111fabca2a76ac5a05ad537c09bd2fc089c8aaa4
- Size of remote file:
- 648 MB
- SHA256:
- 3e2903f27fea08428625b2be98110dc6375be89a515c332d11ae49e03f9a085b
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