Create README.md

README.md

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+---
+language: nl
+license: mit
+pipeline_tag: text-classification
+inference: false
+---
+
+# Regression Model for Exercise Tolerance Functioning Levels (ICF b455)
+
+## Description
+A fine-tuned regression model that assigns a functioning level to Dutch sentences describing exercise tolerance functions. 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. To detect sentences about exercise tolerance functions in clinical text in Dutch, use the [icf-domains](https://huggingface.co/CLTL/icf-domains) classification model.
+
+## Functioning levels
+Level | Meaning
+---|---
+5 | MET>6. Can tolerate jogging, hard exercises, running, climbing stairs fast, sports.
+4 | 4≤MET≤6. Can tolerate walking / cycling at a brisk pace, considerable effort (e.g. cycling from 16 km/h), heavy housework.
+3 | 3≤MET<4. Can tolerate walking / cycling at a normal pace, gardening, exercises without equipment.
+2 | 2≤MET<3. Can tolerate walking at a slow to moderate pace, grocery shopping, light housework.
+1 | 1≤MET<2. Can tolerate sitting activities.
+0 | 0≤MET<1. Can physically tolerate only recumbent activities.
+
+The predictions generated by the model might sometimes be outside of the scale (e.g. 5.2); this is normal in a regression model.
+
+## Intended uses and limitations
+- The model was fine-tuned (trained, validated and tested) on medical records from the Amsterdam UMC (the two academic medical centers of Amsterdam). It might perform differently on text from a different hospital or text from non-hospital sources (e.g. GP records).
+- The model was fine-tuned with the [Simple Transformers](https://simpletransformers.ai/) library. This library is based on Transformers but the model cannot be used directly with Transformers `pipeline` and classes; doing so would generate incorrect outputs. For this reason, the API on this page is disabled.
+
+## How to use
+To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
+```
+from simpletransformers.classification import ClassificationModel
+
+model = ClassificationModel(
+    'roberta',
+    'CLTL/icf-levels-ins',
+    use_cuda=False,
+)
+
+example = 'kan nog goed traplopen, maar flink ingeleverd aan conditie na Corona'
+_, raw_outputs = model.predict([example])
+predictions = np.squeeze(raw_outputs)
+```
+The prediction on the example is:
+```
+3.13
+```
+The raw outputs look like this:
+```
+[[3.1300993]]
+```
+
+## Training data
+- The training data consists of clinical notes from medical records (in Dutch) of the Amsterdam UMC. Due to privacy constraints, the data cannot be released.
+- The annotation guidelines used for the project can be found [here](https://github.com/cltl/a-proof-zonmw/tree/main/resources/annotation_guidelines).
+
+## Training procedure
+The default training parameters of Simple Transformers were used, including:
+- Optimizer: AdamW
+- Learning rate: 4e-5
+- Num train epochs: 1
+- Train batch size: 8
+
+## Evaluation results
+The evaluation is done on a sentence-level (the classification unit) and on a note-level (the aggregated unit which is meaningful for the healthcare professionals).
+
+| | Sentence-level | Note-level
+|---|---|---
+mean absolute error | 0.69 | 0.61
+mean squared error | 0.80 | 0.64
+root mean squared error | 0.89 | 0.80
+
+## Authors and references
+### Authors
+Jenia Kim, Piek Vossen
+
+### References
+TBD