Instructions to use mamiksik/CommitPredictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mamiksik/CommitPredictor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mamiksik/CommitPredictor")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mamiksik/CommitPredictor") model = AutoModelForMaskedLM.from_pretrained("mamiksik/CommitPredictor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9702c3a085d6c57b921682210500dcccc7c3cd2892bcd36ad5befd2d2c9a6f1f
- Size of remote file:
- 3.52 kB
- SHA256:
- e29e30c634f0a127da1316e8fe77d318f4adcb81c89df36c4e83d3f161a211be
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