Update README.md

README.md

@@ -19,4 +19,11 @@ The model was trained as cross-encoder classification model with the objective t
 ## Data
 
 The data from SQuAD 2.0 was sentence-split. The question + the sentence containing the answer was a positive example. 
-The question + the remaining sentence from the same Wikipedia passege were treated as hard negative examples.
+The question + the remaining sentence from the same Wikipedia passege were treated as hard negative examples.
+
+The table balow reports the classification results on the validation set.
+
+
+|                | accuracy | F1       |
+|----------------|----------|----------|
+|ALBERT-xxlarge  | 97.05    | 84.14    |