Text Classification
Transformers
ONNX
Safetensors
mojev-scorer
feature-extraction
calibration
structured-output
multiple-choice
preference-learning
multimodal
mojev
custom_code
Eval Results (legacy)
Instructions to use MoLeMo-Lab/mojev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoLeMo-Lab/mojev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MoLeMo-Lab/mojev", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MoLeMo-Lab/mojev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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