Instructions to use npark95/finetuned_ClinicalLongformer_CAT_020425 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npark95/finetuned_ClinicalLongformer_CAT_020425 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npark95/finetuned_ClinicalLongformer_CAT_020425")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425") model = AutoModelForSequenceClassification.from_pretrained("npark95/finetuned_ClinicalLongformer_CAT_020425") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9a0c72bc4daf42e9ef9985d0de5c6a68498d5f47bf99d8b8076f20dfa06c499
- Size of remote file:
- 595 MB
- SHA256:
- 1963416a121910f8e47b1e4132f59cc6988b279be4e447ec0243c73e1533e379
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