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:
- bbc5b28a25776abdb9c05017f18a7f641f0c0c40ada4f080276c9f1ba93b9c78
- Size of remote file:
- 499 MB
- SHA256:
- 25f3375d48a8896a3387e9533a36a38a847ae5b220839b5006887d0887d5a044
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