Feature Extraction
Transformers
PyTorch
English
roberta
fill-mask
smart-contract
web3
software-engineering
embedding
codebert
solidity
code-understanding
Instructions to use web3se/SmartBERT-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use web3se/SmartBERT-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="web3se/SmartBERT-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("web3se/SmartBERT-v2") model = AutoModelForMaskedLM.from_pretrained("web3se/SmartBERT-v2") - Notebooks
- Google Colab
- Kaggle
add citation
Browse files
README.md
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- [Youwei Huang](https://www.devil.ren)
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- [Sen Fang](https://github.com/TomasAndersonFang)
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## Sponsors
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- [Institute of Intelligent Computing Technology, Suzhou, CAS](http://iict.ac.cn/)
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- [Youwei Huang](https://www.devil.ren)
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- [Sen Fang](https://github.com/TomasAndersonFang)
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## Citations
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```tex
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@article{huang2025smart,
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title={Smart Contract Intent Detection with Pre-trained Programming Language Model},
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author={Huang, Youwei and Li, Jianwen and Fang, Sen and Li, Yao and Yang, Peng and Hu, Bin and Zhang, Tao},
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journal={arXiv preprint arXiv:2508.20086},
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year={2025}
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}
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```
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## Sponsors
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- [Institute of Intelligent Computing Technology, Suzhou, CAS](http://iict.ac.cn/)
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