Text Classification
Transformers
Safetensors
English
roberta
code
classification
BERT
Python
Java
JavaScript
text-embeddings-inference
Instructions to use LavishKK/graphcodebert-slowcode-detector-unbalanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LavishKK/graphcodebert-slowcode-detector-unbalanced with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LavishKK/graphcodebert-slowcode-detector-unbalanced")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LavishKK/graphcodebert-slowcode-detector-unbalanced") model = AutoModelForSequenceClassification.from_pretrained("LavishKK/graphcodebert-slowcode-detector-unbalanced", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from LavishKK/graphcodebert-slowcode-detector-unbalanced: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/LavishKK/graphcodebert-slowcode-detector-unbalanced/resolve/main/model.safetensors
- Command line
-
hf download hf://LavishKK/graphcodebert-slowcode-detector-unbalanced/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/LavishKK/graphcodebert-slowcode-detector-unbalanced/resolve/main/model.safetensors
499 MB
- Xet hash:
- 33a101396e09981c7ea33dfa88d1b1742c8751424db8a3eda945badc8f2ef1da
- Size of remote file:
- 499 MB
- SHA256:
- 348b3c8ca1b0f6453d94b26a69edfd58fff16ffe5d47c7854c5d34cc0b79eaa3
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