transformers pytorch roberta fill-mask smart-contract web3 software-engineering embedding codebert en base_model:microsoft/codebert-base-mlm base_model:finetune:microsoft/codebert-base-mlm license:mit autotrain_compatible endpoints_compatible region:us
---
base_model:
- microsoft/codebert-base-mlm
- web3se/SmartBERT-v2
language:
- en
library_name: transformers
license: mit
pipeline_tag: fill-mask
tags:
- fill-mask
- smart-contract
- web3
- software-engineering
- embedding
- codebert
inference: true
---

# SmartBERT V3 CodeBERT

![SmartBERT](https://huggingface.co/web3se/SmartBERT-v2/resolve/main/framework.png)

## Overview

**SmartBERT V3** is a pre-trained programming language model, initialized with **[CodeBERT-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm)**. It has been further trained on [SmartBERT V2](https://huggingface.co/web3se/SmartBERT-v2) with an additional **64,000** smart contracts, to enhance its robustness in representing smart contract code at the _function_ level.

- **Training Data:** Trained on a total of **80,000** smart contracts, including **16,000** from **[SmartBERT V2](https://huggingface.co/web3se/SmartBERT-v2)** and **64,000** (starts from 30001) new contracts.
- **Hardware:** Utilized 2 Nvidia A100 80G GPUs.
- **Training Duration:** Over 30 hours.
- **Evaluation Data:** Evaluated on **1,500** (starts from 96425) smart contracts.

## Usage

```python
from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline

model = RobertaForMaskedLM.from_pretrained('web3se/SmartBERT-v3')
tokenizer = RobertaTokenizer.from_pretrained('web3se/SmartBERT-v3')

code_example = "function totalSupply() external view <mask> (uint256);"
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

outputs = fill_mask(code_example)
print(outputs)
```

## Preprocessing

All newline (`\n`) and tab (`\t`) characters in the _function_ code were replaced with a single space to ensure consistency in the input data format.

## Base Model

- **Original Model**: [CodeBERT-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm)

## Training Setup

```python
training_args = TrainingArguments(
  output_dir=OUTPUT_DIR,
  overwrite_output_dir=True,
  num_train_epochs=20,
  per_device_train_batch_size=64,
  save_steps=10000,
  save_total_limit=2,
  evaluation_strategy="steps",
  eval_steps=10000,
  resume_from_checkpoint=checkpoint
)
```

## How to Use

To train and deploy the SmartBERT V3 model for Web API services, please refer to our GitHub repository: [web3se-lab/SmartBERT](https://github.com/web3se-lab/SmartBERT).

## Contributors

- [Youwei Huang](https://www.devil.ren)
- [Sen Fang](https://github.com/TomasAndersonFang)

## Sponsors

- [Institute of Intelligent Computing Technology, Suzhou, CAS](http://iict.ac.cn/)
- CAS Mino (中科劢诺)