Update README.md

README.md

@@ -25,17 +25,18 @@ TODO: calver of new release
 TODO: do we refer to the inference file of our repo? 
 
 # Intended use
-The model has been trained and tested to be used for similarity search of assembly code. It has not been trained/tested on any other languages than ARM64, AMD64, RISC-V or I386, 
+The model has been trained and tested to be used for similarity search of assembly code. It has not been trained/tested on any other languages than arm64, amd64, risc-v or i386, 
 nor has it been tested on other downstream tasks.
 
 # Architecture description
 
-This model has been trained similarly to [Wang et al.](https://arxiv.org/pdf/2205.12713). The first step is Masked Language Modelling, where we set the 
+This model has been trained similarly to [Wang et al.](https://arxiv.org/pdf/2205.12713). We did diverge somewhat from Wang et al. their approach, since our BERT model is trained multilingual.
+We also experimented with different training parameters during the Masked Language Modelling (MLM). For example, we set the 
 --mlm-prob parameter to 0.4 in accordance to [Wettig et al., 2023](https://aclanthology.org/2023.eacl-main.217.pdf). We have done some experiments with different mlm-probs 
-(namely, 0.15, 0.3 and 0.4) and found that 0.4 yielded the best results. 
+(namely 0.15, 0.3 and 0.4) and found that 0.4 yielded the best results. 
 
 One remarkable aspect of [Wang et al.](https://arxiv.org/pdf/2205.12713)'s paper, was that they came up with the concept of "Jump target prediction". As part of the 
-Masked Language Modelling procedure, the model also has to predict jump targets: whenever there is a jump in the code, the model shouldn't only predict it to be a 
+MLM procedure, the model also has to predict jump targets: whenever there is a jump in the code, the model shouldn't only predict it to be a 
 jump instruction, but it should also predict to which line of code it's supposed to jump. The idea is that this leads to better understanding of the long-term relations
 in the code, and as Wang et al yielded good results, we have adopted it.