Instructions to use atom92/medical2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use atom92/medical2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="atom92/medical2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("atom92/medical2") model = AutoModelForSequenceClassification.from_pretrained("atom92/medical2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from atom92/medical2: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/atom92/medical2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://atom92/medical2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/atom92/medical2/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 6101d1aa7ba75adfa79968b1cc809cc3d5ba9a01a9e93402448a5652fef91813
- Size of remote file:
- 268 MB
- SHA256:
- ac527629271d86d2f2918f2b886ac075c71c0996cfecf287b1c4a2ec538103c3
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