Zero-Shot Classification
Transformers
Safetensors
English
matilda_jev
feature-extraction
decision-model
typed-decisions
jev
maincode
custom_code
Instructions to use Maincode/Matilda-Jev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Maincode/Matilda-Jev-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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## Results
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Evaluated with Decision Index 0.
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| Model | Parameters | Decision Index | Raw | Breadth |
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| Matilda-Jev | 26.1B |
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| Knowledge & Reasoning | Language | Retrieval & Classification | Tools & Automation | Arts & Human Taste |
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Area scores are chance-corrected skill multiplied by 100.
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## Results
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Evaluated with Decision Index 0.3 (public) over 150,317 requests (currently awaiting official submission).
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| Model | Parameters | Decision Index | Raw | Breadth |
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| Matilda-Jev | 26.1B | 60.16 | 70.04 | 59.21 |
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| Knowledge & Reasoning | Language | Retrieval & Classification | Tools & Automation | Arts & Human Taste |
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| 46.84 | 64.80 | 61.07 | 79.07 | 46.22 |
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Area scores are chance-corrected skill multiplied by 100.
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