update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 language: nl
 license: mit
+pipeline_tag: text-classification
 inference: false
 ---
 
@@ -21,21 +22,56 @@ Level | Meaning
 The predictions generated by the model might sometimes be outside of the scale (e.g. 4.2); 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.
 
-TBD
+## How to use
+To generate predictions with the model, use the [Simple Transformers](https://simpletransformers.ai/) library:
+```
+from simpletransformers.classification import ClassificationModel
 
-## Training data
+model = ClassificationModel(
+    'roberta',
+    'CLTL/icf-levels-adm',
+    use_cuda=False,
+)
 
-TBD
+example = 'Nu sinds 5-6 dagen progressieve benauwdheidsklachten (bij korte stukken lopen al kortademig), terwijl dit eerder niet zo was.'
+_, raw_outputs = model.predict([example])
+predictions = np.squeeze(raw_outputs)
+```
+The prediction on the example is:
+```
+2.26
+```
+The raw outputs look like this:
+```
+[[2.26074648]]
+```
 
-## Training procedure
+## 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).
 
-TBD
+## 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).
 
-TBD
+| | Sentence-level | Note-level
+|---|---|---
+mean absolute error | 0.48 | 0.37
+mean squared error | 0.55 | 0.34
+root mean squared error | 0.74 | 0.58
 
 ## Authors and references
+### Authors
+Jenia Kim, Piek Vossen
 
-TBD
+### References
+TBD