Instructions to use CWrecker/ClincalBert-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CWrecker/ClincalBert-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CWrecker/ClincalBert-Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CWrecker/ClincalBert-Classification") model = AutoModelForSequenceClassification.from_pretrained("CWrecker/ClincalBert-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ee879bbb55eac7f9f55da0c4c8675475217e02db197e214e4c977b7a95e6ece
- Size of remote file:
- 433 MB
- SHA256:
- 2965e27eb80a61ebf6706328353466c9ddd8fb60c297db54c2a2f0fe800d5bcd
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