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
File size: 220 Bytes
d10ad42 | 1 2 3 4 5 6 7 8 9 10 | transformers==5.17.0 flash-linear-attention==0.5.2 safetensors==0.8.0 pillow==12.3.0 numpy==2.5.3 fastapi==0.141.1 uvicorn==0.52.4 httpx==0.28.1 # Also install torch==2.14.0 and torchvision==0.29.0 for your accelerator. |