Instructions to use Fsoft-AIC/dopamin-java-usage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-java-usage with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-java-usage")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-java-usage") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-java-usage", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a3f023d688fd6de0a829c275cbd33b43d2fc28c63fdcda2b18ac373576c1294
- Size of remote file:
- 627 Bytes
- SHA256:
- 2f5a137553b8c97f063793599d60398ae9f6ce3f640b5930779c7508bd95df53
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.