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
Download readout.safetensors from Maincode/Matilda-Jev-v1: direct link, hf CLI and curl.
- Browser
- Download file 2.61 MB
-
https://huggingface.co/Maincode/Matilda-Jev-v1/resolve/main/readout.safetensors
- Command line
-
hf download hf://Maincode/Matilda-Jev-v1/readout.safetensors
-
curl -L -o readout.safetensors https://huggingface.co/Maincode/Matilda-Jev-v1/resolve/main/readout.safetensors
2.61 MB
- Xet hash:
- 062139dadf51c3277a41980705beb0883db92fa33c2263ab6152719919522378
- Size of remote file:
- 2.61 MB
- SHA256:
- 262b432eea8c8ca7439e48c6be565d9d6f0383f10e1c37ae2363a0aefffa571f
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