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
| { | |
| "architectures": [ | |
| "ScorePredictorModel" | |
| ], | |
| "attn_implementation": null, | |
| "backbone_model_name": "microsoft/deberta-v3-base", | |
| "dtype": "float32", | |
| "head_hidden_size": 256, | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "max_position_embeddings": 512, | |
| "model_type": "score_predictor", | |
| "num_scores": 4, | |
| "score_names": [ | |
| "informativeness", | |
| "clarity", | |
| "plausibility", | |
| "faithfulness" | |
| ], | |
| "shared_hidden_size": 512, | |
| "transformers_version": "4.57.3", | |
| "use_attention_pooling": true, | |
| "use_shared_encoder": true, | |
| "auto_map": { | |
| "AutoConfig": "configuration_score_predictor.ScorePredictorConfig", | |
| "AutoModel": "modeling_score_predictor.ScorePredictorModel" | |
| } | |
| } |