Zero-Shot Classification
Transformers
Safetensors
matilda_jev
feature-extraction
matilda
jev
decision-model
classification
maincode
custom_code
Instructions to use Maincode/Matilda-Jev-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Jev-v1.5", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1.5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
17 days ago
-
Update README.md
Verified signature9a732d3
21 days ago
-
Delete scores.json
Verified signature931662e -
Update README.md
Verified signaturec5f1f16 -
Model card: Decision Index 0.3 public suite scores vs Jev and v1
Verified signaturec47a445 -
Model card: Decision Index 0.3 public suite scores (63.47)
Verified signature574d4eb -
Update README.md
Verified signature83eb5c6 -
runtime: use the public base model id; drop cluster paths
Verified signature72b8267 -
Release MATILDA JEV v1.5: verified BF16 checkpoint, native runtime and DI 62.76
Verified signature1b88863 -
initial commit
Verified signaturefdfa3d2