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
Link to fine-tuning dataset
Browse files
README.md
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license: apache-2.0
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language: code
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This is a fine-tuned version of [UniXcoder](https://huggingface.co/microsoft/unixcoder-base-nine), a unified cross-modal pre-trained model for programming languages, on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code.
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license: apache-2.0
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language: code
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datasets:
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- codeparrot/codecomplex
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This is a fine-tuned version of [UniXcoder](https://huggingface.co/microsoft/unixcoder-base-nine), a unified cross-modal pre-trained model for programming languages, on [CodeComplex](https://huggingface.co/datasets/codeparrot/codecomplex), a dataset for complexity prediction of Java code.
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