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
Update runtime/maincode_jev_serve/engine.py
Browse files
runtime/maincode_jev_serve/engine.py
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Optional: needs the `decision-index` package installed alongside. Same scoring as the server.
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Example: python -m decision_index run --engine maincode_jev_serve.engine:MaincodeJevEngine \
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--option checkpoint=/
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"""
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import hashlib
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Optional: needs the `decision-index` package installed alongside. Same scoring as the server.
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Example: python -m decision_index run --engine maincode_jev_serve.engine:MaincodeJevEngine \
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--option checkpoint=./matilda-jev-v1 --rows sample.jsonl.gz --out runs/x
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"""
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import hashlib
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