Instructions to use usakha/Prophetnet_MedPaper_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usakha/Prophetnet_MedPaper_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="usakha/Prophetnet_MedPaper_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("usakha/Prophetnet_MedPaper_model") model = AutoModelForSeq2SeqLM.from_pretrained("usakha/Prophetnet_MedPaper_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b5195de9400efe5e9d02bdedc66cb1d58937026f18bd06cfd3396453ed1de9f9
- Size of remote file:
- 1.57 GB
- SHA256:
- fe43019918b2431122422cfb683ee7f84a07a9b0f3c31cd38996da5d6fc5359f
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