Update README.md

README.md

@@ -25,8 +25,8 @@ Not actively maintained after publishing
 To use this model, use [inference.py in our repository](https://github.com/NetherlandsForensicInstitute/asmtransformers/blob/main/asmtransformers/README.md#inference).
 
 # 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, 
-nor has it been tested on other downstream tasks.
+The model is trained and tested to be used for similarity search of assembly code. It is not trained/tested on any other languages than ARM, AMD64, RISC-V or i386, 
+nor is it tested on other downstream tasks.
 
 # Architecture description
 
@@ -35,7 +35,7 @@ The model is a Bert-base architecture with 12 layers, 12 attention heads and a h
 The model is trained in two phases - a masked language modeling (MLM) pretraining phase and a fine-tuning phase for function similarity.
 
 The MLM pre-training phase uses a loss function that includes jump target prediction (see [Wang et al.](https://arxiv.org/pdf/2205.12713)). 
-This pretraining was perfomed using a masking probability of 0.4 that was determined through ablation experiments. This is in line with the findings of [Wettig et al., 2023](https://aclanthology.org/2023.eacl-main.217.pdf).
+This pretraining is perfomed using a masking probability of 0.4 that was determined through ablation experiments. This is in line with the findings of [Wettig et al., 2023](https://aclanthology.org/2023.eacl-main.217.pdf).
 
 We perform the finetuning phase using [Batched Semi Hard Triplet Loss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) on batches of size 1024. 
 The training set for this phase includes triplets that are formed using versions of the same function compiled for different instruction set architectures. 
@@ -115,7 +115,7 @@ cross-compile source packages for multiple ISAs and optimisation levels. We used
 We also added an optimization level that includes vectorization and cryptographic instructions that we refer to as Optimized-custom, or Oc.
 The binary files from each source package are disassembled using Ghidra and the resulting control-flow graphs (CFGs) are exported.
 
-This results in a maximum of 24 (6 optimisation levels and 4 ISAs) potentially CFGs which are semantically identical, i.e. they represent the same functionality.
+This results in a maximum of 24 (6 optimisation levels and 4 ISAs) potential CFGs which are semantically identical, i.e. they represent the same functionality.
 However, because not all source packages can be built for all ISAs and because some optimisation levels result in identical code, the actual number is often lower than that.
 
 The dataset is split into a train, test and an evaluation set. This in done on source package basis, so all binaries and functions belonging to one source package are part of
@@ -162,9 +162,9 @@ either the training set or the test set, not both.
 ----------------------------
 
 # Preprocessing
-Several preprocessing steps have been taken. CFGs are processed into a flat token list using ISA aware tokenizers.
-Immediate operands and addresses have been normalized to the nearest power-of-two reduce token explosion caused by raw numeric values.
-Finally, target addresses of jumps, conditional branches or call instructions are normalised to fucntion-relative `JUMP_ADDR_*` tokens. 
+Several preprocessing steps were taken. The CFGs are processed into a flat token list using ISA aware tokenizers.
+Immediate operands and addresses have been normalized to the nearest power-of-two to reduce token explosion caused by raw numeric values.
+Finally, target addresses of jumps, conditional branches or call instructions are normalised to function-relative `JUMP_ADDR_*` tokens. 
 
 # Performance
 We report two performance metrics: Mean Reciprocal Rank (MRR) and Accuracy@1.