README.md

---
license: eupl-1.2
tags:
- assembly
- arm64
- amd64
- risc-v
- i386
---

# Model Description
ASMTransformers is a project to train and use a machine learning model to compare assembly (ARM64, AMD64, RISC-V, i386) functions to a database of known functions, 
to aid in the process of reverse engineering.

# Status
?

# Relevant links
* [Github repo ASMtransformers](https://github.com/NetherlandsForensicInstitute/asmtransformers)

# Version
TODO: calver of new release

# Usage
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, 
nor has it been tested on other downstream tasks.

# Architecture description
TODO: get model architecture like on the model card of the previous model
Don't forget to describe that during pretraining, the --mlm-prob parameter has been set to 0.4, in accordance to [paper](INSERT LINK)
Parameters used: epochs=19, eval_steps=10000, batch_size=512, gradient_accumulation_steps=1, mlm_prob=0.4, bf16=True, tf32=True

add estimated time it has cost to train:
pretraining:
finetuning: 4 hours on 1 NVIDIA H200

# Output
The model outputs embeddings that can be compared using cosine similarity

# Data
TODO: how did we get the data?

# Preprocessing
Several preprocessing steps have been taken, that differ slightly between architectures. 
TODO: update to same as architecture.md in repo

# Performance
Performance was measured in two ways: Mean Reciprocal Rank (MRR) and Accuracy@1

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|model|ARM64 mrr|ARM64 acc|AMD64 mrr|AMD64 acc|RISC-V mrr|RISC-V acc|I386 mrr|I386 acc|crosslingual mrr|crosslingual acc|
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