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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pretty_name: MATILDA-jev by Maincode
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license: apache-2.0
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language:
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library_name: transformers
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# MATILDA-jev by Maincode
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## Licence and attribution
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See [LICENSE](LICENSE). The custom classes reuse the Transformers implementation.
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pretty_name: MATILDA-jev by Maincode
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license: apache-2.0
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language:
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library_name: transformers
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tags:
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- decision-model
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- typed-decisions
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- jev
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- maincode
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- custom_code
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base_model:
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---
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# MATILDA-jev by Maincode
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## Licence and attribution
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See [LICENSE](LICENSE). The custom classes reuse the Transformers implementation.
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