Update README.md

README.md

@@ -11,6 +11,34 @@ This model is a finetuned RoBERTa-based model called RobBERT, this model is pre-
 ## Intended use
 The model is finetuned for negation detection on Dutch clinical text. Since it is a domain-specific model trained on medical data, it is meant to be used on medical NLP tasks for Dutch. This particular model is trained on a 32-max token windows surrounding the concept-to-be negated.
 
+## Minimal example
+
+```python
+tokenizer = AutoTokenizer\
+             .from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection")
+model = AutoModelForTokenClassification\
+            .from_pretrained("UMCU/MedRoBERTa.nl_NegationDetection")
+
+some_text = "De patient was niet aanspreekbaar en hij zag er grauw uit. \
+Hij heeft de inspanningstest echter goed doorstaan." 
+inputs = tokenizer(some_text, return_tensors='pt')
+output = model.forward(inputs)
+probas = torch.nn.functional.softmax(output.logits[0]).detach().numpy()
+
+#  koppel aan tokens
+input_tokens = tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])
+target_map = {0: 'B-Negated', 1:'B-NotNegated',2:'I-Negated',3:'I-NotNegated'}
+results = [{'token': input_tokens[idx],
+                 'proba_negated': proba_arr[0]+proba_arr[2],
+                 'proba_not_negated': proba_arr[1]+proba_arr[3]
+                 }  
+                 for idx,proba_arr in enumerate(probas)]
+
+```
+
+It is perhaps good to note that we assume the [Inside-Outside-Beginning](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)) format.
+
+
 ## Data
 The pre-trained model was trained the Dutch section of OSCAR (about 39GB), and is described here: http://dx.doi.org/10.18653/v1/2020.findings-emnlp.292.