Text Classification
Transformers
Safetensors
English
multilingual
gemma_decision
feature-extraction
typed-decisions
structured-decisions
calibration
embeddinggemma
custom_code
decision-model
Eval Results
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
Add Typed Decisions benchmark eval results
#3
by SaylorTwift HF Staff - opened
accuracy, kl_from_gold, brier, ece on LocalLLaMA/typed-decisions test split (400 cases, 2000 decisions, one request per case). Independent run 2026-10-16; details: https://huggingface.co/buckets/SaylorTwift/typed-decisions-evals