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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de5fb760039bc282fc46f6921972f0857d82a6319f468deffc9a2dd4eeb85354
- Size of remote file:
- 627 Bytes
- SHA256:
- ea8db4406f8a1fce1dfb18074452f9812f3aca7a1e937c80a737f30484e5c195
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