Instructions to use mathury/Bio_ClinicalBERT-finetuned-mediQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mathury/Bio_ClinicalBERT-finetuned-mediQA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mathury/Bio_ClinicalBERT-finetuned-mediQA")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mathury/Bio_ClinicalBERT-finetuned-mediQA") model = AutoModelForSequenceClassification.from_pretrained("mathury/Bio_ClinicalBERT-finetuned-mediQA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mathury/Bio_ClinicalBERT-finetuned-mediQA: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/mathury/Bio_ClinicalBERT-finetuned-mediQA/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://mathury/Bio_ClinicalBERT-finetuned-mediQA@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mathury/Bio_ClinicalBERT-finetuned-mediQA/resolve/refs%2Fpr%2F1/pytorch_model.bin
433 MB
- Xet hash:
- addb5e252172429afc43c79ffda3d4b4f6f2373f255e8b4b687c165230dc5615
- Size of remote file:
- 433 MB
- SHA256:
- 88c0ed66d45505b0d9c4cae71cfec85834cac0b183dbaab464e2371fc34841a2
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