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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base") model = AutoModel.from_pretrained("microsoft/unixcoder-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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add supported languages
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Update README.md
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add README
I added a `code` tag to find the model easily among other code models, and a small description of the model.7185fe0
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Upload vocab.json
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Upload pytorch_model.bin with git-lfs
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initial commit
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