Summary Evaluator (ViDeBERTa base + 2-layer MLP)

• Backbone: Fsoft-AIC/videberta-base
• Head: 2-layer MLP for 3 regression scores:
  • faithfulness, coherence, relevance (range depends on training data, commonly 1–5)

Files

• config.json, model.safetensors, tokenizer files: backbone encoder
• regressor.pt: regression head weights
• head_config.json: head meta
• modeling_summary_evaluator.py: convenience loader (from_pretrained_custom)
• training_args.json: training-time hyperparameters

Usage (inference)