Instructions to use ArashF/ClinicalBERT_captainA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArashF/ClinicalBERT_captainA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ArashF/ClinicalBERT_captainA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ArashF/ClinicalBERT_captainA") model = AutoModelForSequenceClassification.from_pretrained("ArashF/ClinicalBERT_captainA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ArashF/ClinicalBERT_captainA: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/ArashF/ClinicalBERT_captainA/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ArashF/ClinicalBERT_captainA/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ArashF/ClinicalBERT_captainA/resolve/main/pytorch_model.bin
541 MB
- Xet hash:
- 75c0389638c1fd9480d8866035ec6965a4132211d9e32ce282c014c586032ed7
- Size of remote file:
- 541 MB
- SHA256:
- e4e9167371852367a1780bb5aa38a12c8dabebcb771829c0c59380d2476cc0b1
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