Instructions to use Fujitsu/AugCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fujitsu/AugCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fujitsu/AugCode")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fujitsu/AugCode") model = AutoModelForSequenceClassification.from_pretrained("Fujitsu/AugCode") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b6ccf1d54eeb28edf70a756352ec7c559f039f60ef62670f8aa627c67279cb5
- Size of remote file:
- 499 MB
- SHA256:
- 513e5b44417cbb3e7e3290fe719e813000212a71b2e502cb4c1c6cfe86b95dd2
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