Update README.md

README.md

@@ -24,7 +24,29 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
+The model can be used for NLI tasks related to biomedical data and even be adapted to fact-checking tasks. It can be used from the Huggingface pipeline method as follows:
+
+
+```
+from transformers import TextClassificationPipeline, AutoModel, AutoTokenizer, AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI", num_labels=3, id2label = {1: 'entailment', 0: 'contradiction',2:'neutral'})
+tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBERT-MNLI-MedNLI")
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True, device=0, batch_size=128)
+
+pipe(['ALDH1 expression is associated with better breast cancer outcomes',
+      'In a series of 577 breast carcinomas, expression of ALDH1 detected by immunostaining correlated with poor prognosis.'])
+```
+
+The output for the above will be:
+
+```
+[[{'label': 'contradiction', 'score': 0.10193759202957153},
+  {'label': 'entailment', 'score': 0.2933262586593628},
+  {'label': 'neutral', 'score': 0.6047361493110657}],
+ [{'label': 'contradiction', 'score': 0.21726925671100616},
+  {'label': 'entailment', 'score': 0.24485822021961212},
+  {'label': 'neutral', 'score': 0.5378724932670593}]]
+```
 
 ## Training and evaluation data