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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from codeparrot/unixcoder-java-complexity-prediction: direct link, hf CLI and curl.
- Browser
- Download file 537 Bytes
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https://huggingface.co/codeparrot/unixcoder-java-complexity-prediction/resolve/main/README.md
- Command line
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hf download hf://codeparrot/unixcoder-java-complexity-prediction/README.md
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curl -L -o README.md https://huggingface.co/codeparrot/unixcoder-java-complexity-prediction/resolve/main/README.md
537 Bytes
metadata
license: apache-2.0
language: code
datasets:
- codeparrot/codecomplex
This is a fine-tuned version of UniXcoder, a unified cross-modal pre-trained model for programming languages, on CodeComplex, a dataset for complexity prediction of Java code. You can also find the code for the fine-tuning in this repository