Instructions to use ParamDev/clinicalbert-medical-doc-oversampled-headtail with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ParamDev/clinicalbert-medical-doc-oversampled-headtail with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ParamDev/clinicalbert-medical-doc-oversampled-headtail")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ParamDev/clinicalbert-medical-doc-oversampled-headtail") model = AutoModelForSequenceClassification.from_pretrained("ParamDev/clinicalbert-medical-doc-oversampled-headtail", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edbb4ef36eb6a3ce7384cf1e003f945ff50ff9b324b675177cf113b6d9d29c51
- Size of remote file:
- 5.27 kB
- SHA256:
- c7f389eaca0ce0c7fe262f9b8e28fa42160caed637202f7c93e9bb00be5380f3
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