Text Classification
Transformers
Safetensors
English
multilingual
gemma_decision
feature-extraction
typed-decisions
structured-decisions
calibration
embeddinggemma
custom_code
decision-model
Instructions to use mrm8488/embeddinggemma-2-ft-typed-decision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/embeddinggemma-2-ft-typed-decision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/embeddinggemma-2-ft-typed-decision", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrm8488/embeddinggemma-2-ft-typed-decision", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Demo for this model on Spaces
2
#1 opened 4 days ago
by
multimodalart