Instructions to use deliciouscat/leobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deliciouscat/leobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="deliciouscat/leobert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deliciouscat/leobert") model = AutoModel.from_pretrained("deliciouscat/leobert") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model is designed for the Leo language and is based on microsoft/codebert-base.
With the code data written in the Leo language crawled from GitHub, the model is trained using self-supervised learning with triplet loss.
Anchor and positive code chunks are from the same code, while negatives are not.
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