Instructions to use jkefeli/PrimaryGleasonBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jkefeli/PrimaryGleasonBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jkefeli/PrimaryGleasonBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jkefeli/PrimaryGleasonBERT") model = AutoModelForSequenceClassification.from_pretrained("jkefeli/PrimaryGleasonBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jkefeli/PrimaryGleasonBERT: direct link, hf CLI and curl.
- Browser
- Download file 285 Bytes
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https://huggingface.co/jkefeli/PrimaryGleasonBERT/resolve/main/README.md
- Command line
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hf download hf://jkefeli/PrimaryGleasonBERT/README.md
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curl -L -o README.md https://huggingface.co/jkefeli/PrimaryGleasonBERT/resolve/main/README.md
285 Bytes
To use the model, add the following from the transformers package:
(1) ClinicalBERT tokenizer:
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
(2) Model type:
model = BertForSequenceClassification.from_pretrained(checkpoint_directory, num_labels=3)