Fill-Mask
Transformers
PyTorch
English
roberta
smart-contract
web3
software-engineering
embedding
codebert
Instructions to use web3se/SmartBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use web3se/SmartBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="web3se/SmartBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("web3se/SmartBERT") model = AutoModelForMaskedLM.from_pretrained("web3se/SmartBERT") - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
language:
- en
inference: true
base_model:
- FacebookAI/roberta-base
pipeline_tag: fill-mask
tags:
- fill-mask
- smart-contract
- web3
- software-engineering
- embedding
- codebert
library_name: transformers
SmartBERT V1 RoBERTa (2022)
Overview
This smart contract pre-trained model is used to transfer smart contract function-level code to embeddings.
It is trained by Sen Fang in 2022 on over 40,000 smart contracts.
Initialized with RoBERTa
Please update to SmartBERT V2
Citations
@article{huang2025smart,
title={Smart Contract Intent Detection with Pre-trained Programming Language Model},
author={Huang, Youwei and Li, Jianwen and Fang, Sen and Li, Yao and Yang, Peng and Hu, Bin and Zhang, Tao},
journal={arXiv preprint arXiv:2508.20086},
year={2025}
}