Text Classification
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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")# 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
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README.md
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The model predicts one of two labels:
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- `LABEL_0`:
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It works best on short to medium-length code snippets in supported programming languages and is intended for use with the 🤗 Transformers library.
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The model predicts one of two labels:
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- `LABEL_0`: Slow code (potential performance issues detected)
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- `LABEL_1`: Fast code (no major performance concerns)
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It works best on short to medium-length code snippets in supported programming languages and is intended for use with the 🤗 Transformers library.
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