Update README.md

README.md

@@ -14,28 +14,39 @@ tags:
 
 ## Model description
 
-The model is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
-](https://aclanthology.org/2021.nlpmc-1.5/). 
-It is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
+The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
+](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
+
+The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
 
 
 #### How to use the model
 
 You can load the model via the transformers library:
 ```
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
-model = AutoModel.from_pretrained("bvanaken/clinical-assertion-negation-bert")
+model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
+
 ```
-The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT`, `ABSENT` or `POSSIBLE`. The entity in question is identified with the special token `[entity]` surrounding it.
 
-Example input:
+The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it.
+
+Example input and inference:
 ```
-The patient recovered during the night and now denies any [entity] shortness of breath [entity].
+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)
+
+import numpy as np
+predicted_label = np.argmax(output.logits.detach().numpy())  ## 1 == ABSENT
 ``` 
 
 ### Cite
 
+When working with the model, please cite our paper as follows:
+
 ```bibtex
 @inproceedings{van-aken-2021-assertion,
     title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",