Update README.md

README.md

@@ -34,19 +34,40 @@ It achieves the following results on the test set:
 - Loss: 2.0552
 - Accuracy: 0.8329
 
+## How to use?
+
+You can use this model with the Transformers pipeline for text classification.
+
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstBERT128_Rubric")
+model = AutoModelForSequenceClassification.from_pretrained("tartuNLP/EstBERT128_Rubric")
+
+nlp = pipeline("ner", model=bertner, tokenizer=tokenizer)
+text = "Kaia Kanepi (WTA 57.) langes USA-s Charlestonis toimuval WTA 500 kategooria tenniseturniiril konkurentsist kaheksandikfinaalis, kaotades poolatarile Magda Linette'ile (WTA 64.) 3 : 6, 6 : 4, 2 : 6."
+result = nlp(text)
+
+print(result)
+```
+
+```
+[{'label': 'SPORT', 'score': 0.9999998807907104}]
+```
+
+
 ## Model description
 
-A single linear layer classifier is fit on top of the last layer [CLS] token representation. The model is fully fine-tuned during training.
+A single linear layer classifier is fit on top of the last layer [CLS] token representation of the EstBERT model. The model is fully fine-tuned during training.
 
 ## Intended uses & limitations
 
-This model is intended to be used as it is. It can be used to predict nine rubric categories of Estonian texts. 
-
-We do not guarantee that the model is useful for anything or that the predictions are accurate on new data.
+This model is intended to be used as it is. We hope that it can prove to be useful to somebody but we do not guarantee that the model is useful for anything or that the predictions are accurate on new data.
 
 
 ## Citation information
-If you use this model, please cite
+If you use this model, please cite:
 
 ```
 @inproceedings{tanvir2021estbert,