Instructions to use ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification") model = AutoModelForSequenceClassification.from_pretrained("ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ahmed792002/Finetuning_Bert_ClinicalNotes_Diagnosis_Classification/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 2364272c47318637a8f9095723f27369391e85b68f55a527898fbcf91c5bd568
- Size of remote file:
- 5.3 kB
- SHA256:
- 4b4c01342242806277ca9aac9f4bdc049324cb87b130d2273c914cda4f06cdfb
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