Feature Extraction
Transformers
Safetensors
English
bert
text-classification
icd10
healthcare
medical
custom_code
text-embeddings-inference
Instructions to use UMCU/ICD10_classifier_base_English with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UMCU/ICD10_classifier_base_English with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UMCU/ICD10_classifier_base_English", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UMCU/ICD10_classifier_base_English", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("UMCU/ICD10_classifier_base_English", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from UMCU/ICD10_classifier_base_English: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/UMCU/ICD10_classifier_base_English/resolve/main/training_args.bin
- Command line
-
hf download hf://UMCU/ICD10_classifier_base_English/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/UMCU/ICD10_classifier_base_English/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- fc29a5a3f91a48a84f6a4acec1c15ce9b7229f883542346af2d18bbdb2f45686
- Size of remote file:
- 5.37 kB
- SHA256:
- 31382314b72a26bf2d38949136f28d27fe342b93062985e195ac1d0a23f64441
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