update model inference with pipeline

README.md

@@ -26,7 +26,7 @@ The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](ht
 
 You can load the model via the transformers library:
 ```
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
 tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
 model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
 
@@ -38,11 +38,10 @@ Example input and inference:
 ```
 input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
 
-tokenized_input = tokenizer(input, return_tensors="pt")
-output = model(**tokenized_input)
+classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
 
-import numpy as np
-predicted_label = np.argmax(output.logits.detach().numpy())  ## 1 == ABSENT
+classification = classifier(input)
+# [{'label': 'ABSENT', 'score': 0.9842607378959656}]
 ``` 
 
 ### Cite