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
Adding `safetensors` variant of this model
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by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:cf3ad03820e4e95acb7d4a28de321d38e756e39e41840d379e4b7631a84d433d
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size 498818156
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