Update README.md

README.md

@@ -61,10 +61,10 @@ training_args = TrainingArguments(
 ### Eval results
 The model was evaluated using the binary test sets for MultiNLI, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
 
-mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
----------|----------|---------|----------|----------|------
-x | x | x | x | x |  x
-
+dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
+--------|---------|----------|---------|----------|----------|------
+accuracy | 0.925 | 0.922 | 0.892 | 0.676 | 0.665 | 0.888
+speed (text/sec, GPU, 128 batch) | 6.0 | 6.3 | 3.0 | 5.8 | 5.0 | 7.6
 
 ## Limitations and bias
 Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases.