Fill-Mask
Transformers
PyTorch
English
roberta
smart-contract
web3
software-engineering
embedding
codebert
Instructions to use web3se/SmartBERT-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use web3se/SmartBERT-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="web3se/SmartBERT-v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("web3se/SmartBERT-v3") model = AutoModelForMaskedLM.from_pretrained("web3se/SmartBERT-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from web3se/SmartBERT-v3: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/web3se/SmartBERT-v3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://web3se/SmartBERT-v3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/web3se/SmartBERT-v3/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 1f699a4ae3816742787f04084bef6b984103521e4355a9893563ad61fb65fd59
- Size of remote file:
- 499 MB
- SHA256:
- ee606e09af427f20e264da959fb9162a8fbce455fc921296294cfefd88bfe46a
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