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:
- 3f512da10c8cd398fc49e3e551baf482e14e79a77d9537935eada929a7423dc4
- Size of remote file:
- 499 MB
- SHA256:
- 06c4b00d6e9a425d3a0b27d6982705e8cade89cf1e691a5ff1b27355881f912f
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