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
Update README.md
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README.md
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We used class weighting with log smoothing.
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For the
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TLDR;
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* ICD10 trimmed after dot, A10.42 -> A10
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* minimum_token_count 16
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We used class weighting with log smoothing.
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TLDR;
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* ICD10 trimmed after dot, A10.42 -> A10
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* minimum_token_count 16
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