Update README.md

README.md

@@ -17,14 +17,13 @@ Another key feature of our model is that it also avoids lookback bias. In partic
 
 Our model is trained on 1 billion words (1-2 billion tokens) from Parliament Q&As, TV show conversations,  music lyrics, patents, FOMC documents, public access books, newspapers, election campaign documents, and research papers. The model is based on the base-size DeBERTa model architecture and a custom ByteLevelBPETokenizer trained using the same training data.
 
-Our model achieves nearly state-of-the-art performance with less than 1% of training data and the smallest model size.
+Our model achieves state-of-the-art performance with less than 10% of training data.
 
 | Model                  | Vocabulary (K) | Backbone #Params (M) | COLA | SST2 | QQP|MNLI|QNLI
 |------------------------|:--------------:|:--------------------:|:-----------------:|:---------------:|:--------------------:|:-----------------:|:---------------:|
-| ChronoBERT_1999          | 50             | 149                   | 0.57|0.92|0.89|0.86|0.91|
 | FinBERT             | 30             | 110                   | 0.29|0.89|0.87|0.79|0.86|
-| StoriesLM           | 30             | 110                   | 0.47|0.90|0.87|0.80|0.87|
-| NolBERT           | 30             | 109                  | 0.43|0.91|0.91|0.82|0.89
+| StoriesLM           | 30             | 110                   | **0.47**|0.90|0.87|0.80|0.87|
+| NolBERT           | 30             | 109                  | 0.43|**0.91**|**0.91**|**0.82**|**0.89**
 
 ## Usage Examples