Instructions to use CLTL/icf-domains with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLTL/icf-domains with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CLTL/icf-domains")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RobertaForMultiLabelSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CLTL/icf-domains") model = RobertaForMultiLabelSequenceClassification.from_pretrained("CLTL/icf-domains", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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language: nl
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license: mit
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tag: text-classification
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# A-PROOF ICF-domains Classification
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## Description
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A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model [link to be added](), a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC.
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## ICF domains
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The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19:
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language: nl
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license: mit
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# A-PROOF ICF-domains Classification
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## Description
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A fine-tuned multi-label classification model that detects 9 [WHO-ICF](https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health) domains in clinical text in Dutch. The model is based on a pre-trained Dutch medical language model ([link to be added]()), a RoBERTa model, trained from scratch on clinical notes of the Amsterdam UMC.
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## ICF domains
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The model can detect 9 domains, which were chosen due to their relevance to recovery from COVID-19:
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