Text Classification
Transformers
Safetensors
English
roberta
code
classification
BERT
text-embeddings-inference
Instructions to use LavishKK/graphcodebert-slowcode-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LavishKK/graphcodebert-slowcode-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LavishKK/graphcodebert-slowcode-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LavishKK/graphcodebert-slowcode-detector") model = AutoModelForSequenceClassification.from_pretrained("LavishKK/graphcodebert-slowcode-detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e189f0afe9bc0e6301670e611e3a113eff44336f4a66a0d41f83b5c784c3df5a
- Size of remote file:
- 499 MB
- SHA256:
- a22a38e84e07e7bd925e01f838076b796d52b6dd9c3acdc6f2325b3e109e04a0
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