Feature Extraction
Transformers
Safetensors
matilda_jev
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("feature-extraction", 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
Model card: Decision Index 0.3 public suite scores vs Jev and v1
Browse files
README.md
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@@ -79,16 +79,20 @@ prediction, is in [Maincode/matilda-jev-decision-index](https://huggingface.co/d
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under `runs/matilda-jev-v1.5/`. [`scores.json`](scores.json) in this repository is the earlier 0.2.1 run
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(62.76), kept for reference.
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| Decision Index | 63.47 |
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| Raw | 72.62 |
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| Breadth | 62.45 |
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| Knowledge & Reasoning | 49.83 |
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| Language Understanding | 69.91 |
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| Retrieval & Classification | 65.96 |
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| Tools & Automation | 80.84 |
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| Arts & Human Taste | 45.41 |
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Area scores are chance-corrected skill multiplied by 100. These results are
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diagnostic: earlier training in this model's lineage used benchmark-related
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under `runs/matilda-jev-v1.5/`. [`scores.json`](scores.json) in this repository is the earlier 0.2.1 run
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(62.76), kept for reference.
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| Metric | Jev | MATILDA JEV v1 | **MATILDA JEV v1.5** |
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| Decision Index (public) | 57.96 | 60.16 | **63.47** |
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| Raw | 68.55 | 70.04 | **72.62** |
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| Breadth | 57.27 | 59.21 | **62.45** |
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| Knowledge & Reasoning | **53.87** | 46.84 | 49.83 |
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| Language Understanding | 59.23 | 64.80 | **69.91** |
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| Retrieval & Classification | 55.42 | 61.07 | **65.96** |
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| Tools & Automation | 75.09 | 79.07 | **80.84** |
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| Arts & Human Taste | 39.08 | **46.22** | 45.41 |
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Jev is the public-suite part of its Decision Index 0.3 board entry (per-benchmark public results, as published in the
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kit's `tests/fixtures/board-0.3.json`), not its Full score. MATILDA JEV v1 is
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[Maincode/matilda-jev-v1](https://huggingface.co/Maincode/matilda-jev-v1) (`runs/matilda-jev-v1.3/` in the same dataset).
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Area scores are chance-corrected skill multiplied by 100. These results are
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diagnostic: earlier training in this model's lineage used benchmark-related
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