Instructions to use microsoft/unixcoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/unixcoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/unixcoder-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base") model = AutoModel.from_pretrained("microsoft/unixcoder-base") - Inference
- Notebooks
- Google Colab
- Kaggle
add supported languages
Browse files
README.md
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## Model description
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UniXcoder is a unified cross-modal pre-trained model for programming languages to support both code-related understanding and generation tasks.
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[UniXcoder: Unified Cross-Modal Pre-training for Code Representation.](https://arxiv.org/abs/2203.03850) Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin.
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[GitHub](hhttps://github.com/microsoft/CodeBERT/tree/master/UniXcoder#unixcoder)
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## Model description
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UniXcoder is a unified cross-modal pre-trained model for programming languages to support both code-related understanding and generation tasks. The model can support six languages: java, ruby, python, php, javascript, and go.
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[UniXcoder: Unified Cross-Modal Pre-training for Code Representation.](https://arxiv.org/abs/2203.03850) Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, Jian Yin.
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[GitHub](hhttps://github.com/microsoft/CodeBERT/tree/master/UniXcoder#unixcoder)
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