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:
- 346172a0a69185024c74708aa6a3d2a64546b84834e5e3677d9bac73d787f5bc
- Size of remote file:
- 5.37 kB
- SHA256:
- 7e630b8cac934879cafea7e9194c17609ab1e1d20621646d79a5a55286571ffd
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