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")# pip install -U transformers accelerate # 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 training_args.bin from mathury/Bio_ClinicalBERT-finetuned-mediQA: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/mathury/Bio_ClinicalBERT-finetuned-mediQA/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://mathury/Bio_ClinicalBERT-finetuned-mediQA@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mathury/Bio_ClinicalBERT-finetuned-mediQA/resolve/refs%2Fpr%2F1/training_args.bin
3.58 kB
- Xet hash:
- 0c0980f41355ecc7e8bc0eba70a81b0ec2278c693e826e20e22fccac3f9de1fb
- Size of remote file:
- 3.58 kB
- SHA256:
- e1602e985b1a318938564671b5c0131aba4a91ebef266f7725efd2ac6cb9a8d8
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