Instructions to use npark95/finetuned_BioClinicalBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npark95/finetuned_BioClinicalBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="npark95/finetuned_BioClinicalBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("npark95/finetuned_BioClinicalBERT") model = AutoModelForSequenceClassification.from_pretrained("npark95/finetuned_BioClinicalBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e725ccd59db3083e2721e42ec151b3cccb54f45cb0b86116e3c8c2fd31eda1b
- Size of remote file:
- 5.37 kB
- SHA256:
- aa3cb025705c0061e90536f7774b5fbcf9e0d24b6df1ba3e5906cb70160b1f89
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