Text Classification
Transformers
English
roberta
medical
drug-recommendation
continual-learning
mixture-of-experts
lora
healthcare
pharmacology
Eval Results (legacy)
Instructions to use nickagge/paladim-1b-medical with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nickagge/paladim-1b-medical with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nickagge/paladim-1b-medical")# Load model directly from transformers import AutoTokenizer, PALADIM tokenizer = AutoTokenizer.from_pretrained("nickagge/paladim-1b-medical") model = PALADIM.from_pretrained("nickagge/paladim-1b-medical", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 882b2e5274446f86c6cf212f7412aa6b44d9dbbe7dc6c80c703f6e873331d47e
- Size of remote file:
- 1.18 GB
- SHA256:
- feafdeb420b78d1802c182a0cfb05d8e030abdce617ef2a577409217e7d14c0f
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