Feature Extraction
Transformers
Safetensors
score_predictor
deberta-v3
regression
text-evaluation
multilingual
trust-remote-code
custom_code
Instructions to use QCRI/OmniScore-deberta-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/OmniScore-deberta-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="QCRI/OmniScore-deberta-v3", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QCRI/OmniScore-deberta-v3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """ | |
| ScorePredictorModel - Multi-output regression for conversation scoring. | |
| """ | |
| from .configuration_score_predictor import ScorePredictorConfig | |
| from .modeling_score_predictor import ScorePredictorModel, ScorePredictorOutput | |
| __all__ = ["ScorePredictorConfig", "ScorePredictorModel", "ScorePredictorOutput"] | |