Text Classification
Transformers
Safetensors
English
tiny_jev
feature-extraction
jev
system-one
open-jev
decision-model
calibrated
routing
classification
guardrails
non-autoregressive
custom_code
Instructions to use lostargon/Tiny-Jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lostargon/Tiny-Jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lostargon/Tiny-Jev", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lostargon/Tiny-Jev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94e9aa04053d887f4376f879208b80ddd1f9dc5f30865b62a321d7190b95718f
- Size of remote file:
- 11.4 MB
- SHA256:
- e3fccc77adda606e2a461ffd17ca5a55a5a75ac40a5aa0a91c5307fb31b1efac
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