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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from web3se/SmartBERT-v2: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/web3se/SmartBERT-v2/resolve/a364f07a696b8ce4ccf4e66f09dbabe58487d963/pytorch_model.bin
- Command line
-
hf download hf://web3se/SmartBERT-v2@a364f07a696b8ce4ccf4e66f09dbabe58487d963/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/web3se/SmartBERT-v2/resolve/a364f07a696b8ce4ccf4e66f09dbabe58487d963/pytorch_model.bin
499 MB
- Xet hash:
- 3936b306ca6822d1ee84b3cab0fb7693119ebe530b24300c6dbbf84e371ec81a
- Size of remote file:
- 499 MB
- SHA256:
- d35a09a3646a4de5b8da57531aa01ec7a5a529b1307aa0e068f6504d26f01a5c
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