Update README.md

README.md

@@ -30,10 +30,10 @@ The model was then evaluated on [Mean Reciprocal Rank (MRR)](https://en.wikipedi
 When the model has to pick the positive example out of a pool of 32, it almost always ranks it first. When
 the pool is significantly enlarged to 10.000 functions, it still ranks the positive example highest most of the time. 
 
-| Model   | Pool size | MRR  | Recall@1 |
-|---------|-----------|------|----------|
-| ASMBert | 32        | 0.78 | 0.72     |
-| ASMBert | 10.000    | 0.58 | 0.56     |
+| Model     | Pool size | MRR  | Recall@1 |
+|-----------|-----------|------|----------|
+| ARM64BERT | 32        | 0.78 | 0.72     |
+| ARM64BERT | 10.000    | 0.58 | 0.56     |
 
 ## Purpose and use of the model
 
@@ -62,6 +62,8 @@ either the train or the test set, not both. We have not performed any deduplicat
 | train |  18,083,285 |
 | test  |   3,375,741 |
 
+For our training and evaluation code, see our [GitHub repository](https://github.com/NetherlandsForensicInstitute/asmtransformers).
+
 ### By whom was the dataset collected and annotated?
 The dataset was collected by our team. The annotation of similar/non-similar function comes from the different compilation
 levels, i.e. what we consider "similar functions" is in fact the same function that has been compiled in a different way.