Instructions to use ayanami-kitasan/code-pruner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayanami-kitasan/code-pruner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ayanami-kitasan/code-pruner")# Load model directly from transformers import SwePrunerForCodeCompression model = SwePrunerForCodeCompression.from_pretrained("ayanami-kitasan/code-pruner", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e1ccbf8fa2c13de7febc9c5838adf0c2c749d304f7bcc9739783ae5401c6788
- Size of remote file:
- 1.35 GB
- SHA256:
- 373b77f5262c3298b803303d49ea38949d3e92f08dc3bbb90b03f490413adae9
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