Update README.md

README.md

@@ -25,7 +25,19 @@ The feel-it-italian-sentiment model performs sentiment analysis. We fine-tuned t
 
 ## Data
 
-Soon
+Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper.
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+## Performance
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+We evaluate our performance using [SENTIPOLC16 Evalita](http://www.di.unito.it/~tutreeb/sentipolc-evalita16/data.html). This dataset comes with a training set and a testing set and thus we can compare the performance of different training datasets on the SENTIPOLC test set We collapsed the FEEL-IT classes into 2 by mapping joy to the positive class and anger, fear and sadness into the negative class. 
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+We use the fine-tuned UmBERTo model. The results show that FEEL-IT can provide better results on the SENTIPOLC test set than those that can be obtained with the SENTIPOLC training set. 
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+| Training Dataset | Macro-F1 | Accuracy
+| ------ | ------ |------ |
+| SENTIPOLC16 | 0.80 | 0.81 |
+| FEEL-IT | **0.81** | **0.84**  |
+| FEEL-IT+SentiPolc | 0.81 | 0.82
 
 ## Usage