Instructions to use Fsoft-AIC/dopamin-java-pointer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-java-pointer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-java-pointer")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-java-pointer") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-java-pointer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2e8a5b62d16eb3a478f82d455ab114c2a42a85bcaf399067e4646255be1ab06
- Size of remote file:
- 4.54 kB
- SHA256:
- 25497cf735f507fecfb9228f19afa143d6c45a877bc4e9e1b3a187c67421b2e0
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