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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See [TEST_REPORT.json](TEST_REPORT.json). These checks are not a full benchmark rerun or a full continued-training run. Other accelerator backends are untested for this configuration edition.
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| Model | Parameters | Decision Index | Raw | Breadth |
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See [TEST_REPORT.json](TEST_REPORT.json). These checks are not a full benchmark rerun or a full continued-training run. Other accelerator backends are untested for this configuration edition.
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## Results
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Evaluated with Decision Index 0.2.1 over 150,317 requests (currently awaiting official submission).
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