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")# 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
File size: 285 Bytes
26f7f25 32255d4 26f7f25 32255d4 26f7f25 32255d4 48771eb | 1 2 3 4 5 6 7 8 9 10 11 | 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)
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