Instructions to use codeparrot/unixcoder-java-complexity-prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codeparrot/unixcoder-java-complexity-prediction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="codeparrot/unixcoder-java-complexity-prediction")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("codeparrot/unixcoder-java-complexity-prediction") model = AutoModelForSequenceClassification.from_pretrained("codeparrot/unixcoder-java-complexity-prediction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f303e5ff16af2e8c8a953f6485ae42e77a1d309623d563308349abff5ac3c399
- Size of remote file:
- 504 MB
- SHA256:
- 1719622f78746d0fd5eb07d2ba4fbe39a72be55920d39904a712c9fd470b81a0
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