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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("web3se/SmartBERT-v2") model = AutoModelForMaskedLM.from_pretrained("web3se/SmartBERT-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from web3se/SmartBERT-v2: direct link, hf CLI and curl.
- Browser
- Download file 4.02 kB
-
https://huggingface.co/web3se/SmartBERT-v2/resolve/a364f07a696b8ce4ccf4e66f09dbabe58487d963/training_args.bin
- Command line
-
hf download hf://web3se/SmartBERT-v2@a364f07a696b8ce4ccf4e66f09dbabe58487d963/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/web3se/SmartBERT-v2/resolve/a364f07a696b8ce4ccf4e66f09dbabe58487d963/training_args.bin
4.02 kB
- Xet hash:
- aff2d2fc9d38d3395f163e5eceef583d3f588f94e2cdd25fe45f2ee2493e2f96
- Size of remote file:
- 4.02 kB
- SHA256:
- a9496fbbf40d87baa68a3e2cf4cd54780c45a7da8e8c5700c1bfeb67753e379b
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