Update README.md

README.md

@@ -14,7 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 # best_model
 
-This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.2833
 - Accuracy: 0.8942
@@ -25,7 +25,32 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
+The model can be used for text classification tasks of Randomized Controlled Trials that does not have any structure. The text can be classified as one of the following:
+* BACKGROUND
+* CONCLUSIONS
+* METHODS
+* OBJECTIVE
+* RESULTS
+
+The model can be directly used like this:
+
+```python
+from transformers import TextClassificationPipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+model = AutoModelForSequenceClassification.from_pretrained("pritamdeka/PubMedBert-PubMed200kRCT")
+tokenizer = AutoTokenizer.from_pretrained("pritamdeka/PubMedBert-PubMed200kRCT")
+pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
+pipe("Treatment of 12 healthy female subjects with CDCA for 2 days resulted in increased BAT activity.")
+```
+Results will be shown as follows:
+
+```python
+[[{'label': 'BACKGROUND', 'score': 0.0026365036610513926},
+  {'label': 'CONCLUSIONS', 'score': 0.052317846566438675},
+  {'label': 'METHODS', 'score': 0.007398751098662615},
+  {'label': 'OBJECTIVE', 'score': 0.0008019638480618596},
+  {'label': 'RESULTS', 'score': 0.9368449449539185}]]
+```
 
 ## Training and evaluation data