Instructions to use Fsoft-AIC/dopamin-java-rational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fsoft-AIC/dopamin-java-rational with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fsoft-AIC/dopamin-java-rational")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/dopamin-java-rational") model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/dopamin-java-rational") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7743ed85f81d50085472721a24700ce94034e755a1b247d474a96507ea8d964e
- Size of remote file:
- 627 Bytes
- SHA256:
- 1224706a188bfeff22831b0f93d77fa81205d6a1813a08a90b6152f892574be1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.