Update README.md

README.md

@@ -17,6 +17,16 @@ This is the model presented in the paper "Detecting Text Formality: A Study of T
 The original model is [DeBERTa (large)](https://huggingface.co/microsoft/deberta-v3-large). Then, it was fine-tuned on the English corpus for fomality classiication [GYAFC](https://arxiv.org/abs/1803.06535). 
 In our experiments, the model showed the best results within Transformer-based models for the task. More details, code and data can be found [here](https://github.com/s-nlp/paradetox).
 
+**Evaluation Results**
+
+Here, we provide several metrics of the best models from each category participated in the comparison to understand the ranks of values.
+|                  | acc  | f1-formal | f1-informal |
+|------------------|------|-----------|-------------|
+| bag-of-words     | 79.1 |    81.8   |     75.6    |
+| CharBiLSTM       | 87.0 |    89.0   |     84.0    |
+| DistilBERT-cased | 80.1 |    83.0   |     75.6    |
+| DeBERTa-large    | 87.8 |    89.0   |     86.1    |
+
 **How to use**
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer