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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f0b9d8b8fe8b720caa0ccfbca7de4d37fe18f9c8d3e2832afe4886a31f2c67c
- Size of remote file:
- 14.5 kB
- SHA256:
- 0bdde48c6a64301fcab27f50a232a40d13e6db332d5ac0d8dce3ec7052ae4709
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