Instructions to use Fsoft-AIC/dopamin-java-summary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-java-summary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-java-summary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-java-summary") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-java-summary") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80553172cdf173c46551c206091814824d51a08741c8b526f289ef80c39a0228
- Size of remote file:
- 612 MB
- SHA256:
- 2504cc2e3989e98e5a5a3125a2031803048b7a765179c35e12e1a1bd31858f29
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