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
File size: 714 Bytes
fed9f16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"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"
}
} |