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
| """ | |
| Configuration class for ScorePredictorModel. | |
| Compatible with Hugging Face's AutoConfig. | |
| """ | |
| from transformers import PretrainedConfig | |
| class ScorePredictorConfig(PretrainedConfig): | |
| """ | |
| Configuration class for ScorePredictorModel (Encoder-only). | |
| Args: | |
| backbone_model_name: Name or path of the backbone encoder model (e.g., 'bert-base-uncased') | |
| num_scores: Number of regression outputs (default: 4) | |
| score_names: Names of the scores being predicted | |
| hidden_size: Hidden size of the backbone model (auto-detected) | |
| attn_implementation: Attention implementation ('eager', 'sdpa', 'flash_attention_2') | |
| use_attention_pooling: Whether to use attention pooling in addition to CLS+Mean | |
| use_shared_encoder: Whether to use shared MLP before task-specific heads | |
| hidden_dropout_prob: Dropout probability for hidden layers | |
| head_hidden_size: Hidden size for task-specific score heads | |
| shared_hidden_size: Hidden size for shared encoder layers | |
| **kwargs: Additional arguments passed to PretrainedConfig | |
| """ | |
| model_type = "score_predictor" | |
| def __init__( | |
| self, | |
| backbone_model_name: str = "bert-base-uncased", | |
| num_scores: int = 4, | |
| score_names: list = None, | |
| hidden_size: int = None, | |
| attn_implementation: str = None, | |
| use_attention_pooling: bool = True, | |
| max_position_embeddings: int = 512, | |
| use_shared_encoder: bool = True, | |
| hidden_dropout_prob: float = 0.1, | |
| head_hidden_size: int = 256, | |
| shared_hidden_size: int = 512, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.backbone_model_name = backbone_model_name | |
| self.num_scores = num_scores | |
| self.score_names = score_names or [ | |
| "informativeness", | |
| "clarity", | |
| "plausibility", | |
| "faithfulness" | |
| ] | |
| self.hidden_size = hidden_size | |
| self.attn_implementation = attn_implementation | |
| self.max_position_embeddings = kwargs.get("max_position_embeddings", 512) | |
| # Architecture configuration | |
| self.use_attention_pooling = use_attention_pooling | |
| self.use_shared_encoder = use_shared_encoder | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.head_hidden_size = head_hidden_size | |
| self.shared_hidden_size = shared_hidden_size |