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---
license: mit
language:
- en
inference: true
base_model:
- microsoft/codebert-base-mlm
- web3se/SmartBERT-v2
pipeline_tag: fill-mask
tags:
- fill-mask
- smart-contract
- web3
- software-engineering
- embedding
- codebert
---
# SmartBERT V3 CodeBERT
## 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) |