Instructions to use AlpineHealth/alpine-clinical-assertion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlpineHealth/alpine-clinical-assertion with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlpineHealth/alpine-clinical-assertion")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AlpineHealth/alpine-clinical-assertion") model = AutoModelForSequenceClassification.from_pretrained("AlpineHealth/alpine-clinical-assertion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AlpineHealth/alpine-clinical-assertion: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/AlpineHealth/alpine-clinical-assertion/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://AlpineHealth/alpine-clinical-assertion@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AlpineHealth/alpine-clinical-assertion/resolve/refs%2Fpr%2F1/pytorch_model.bin
433 MB
- Xet hash:
- 85c83cfaeaaf2b390cc3d8187c193bb016afa228599efd6ba8370505c3042883
- Size of remote file:
- 433 MB
- SHA256:
- b9ed84fc44e6619e2639933c0deb5bca23b96c2087b4aebc67c444a2e216dda2
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