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:
- e6115f86e4eeb2ff7dc397fb0b8a0402656b19f6e0e4803db5bc6b5a5ba0f9ad
- Size of remote file:
- 612 MB
- SHA256:
- 6dd1f7a4be761078371383e9e78b2104b25cb9e53764054e7fcfaed879a7f38a
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