Reward-EmoPair

Pairwise reward model trained on the EmoPair dataset for scoring text along three emotional dimensions: Valence, Arousal, and Dominance.

Model description

A roberta-large encoder with three scalar output heads:

• Valence head: regression against gold 1–5 EmoBank scores (MSE loss)
• Arousal head: pairwise ranking trained with BCE loss on paired comparisons
• Dominance head: pairwise ranking trained with BCE loss on paired comparisons

The model outputs continuous scores for each dimension; Arousal and Dominance scores are correlated with Bradley-Terry scores derived from pairwise annotations.

Base model: roberta-large

Training details

Parameter	Value
Epochs (max)	20
Mini-batch size	16
Effective batch size	64
Learning rate	2e-06
Weight decay	0.05
Dropout	0.2
Max sequence length	256
Early-stopping patience	7
Seed	42

Evaluation results

Pairwise test-set accuracy

Metric	Valence	Arousal	Dominance	Overall
Accuracy	0.8313	0.8537	0.7703	0.8184

Pearson correlation with Bradley-Terry scores (A/D) and gold V

Split	Valence r	Arousal BT r	Dominance BT r
Dev	0.8015	0.8254	0.7094
Test	0.8051	0.8706	0.7263

Files

File	Description
pytorch_model.pth	Full model checkpoint (weights + optimizer + scheduler state)
training_args.json	Hyperparameters used during training
test_results.csv	Per-split test-set metrics
regression_metrics.json	Dev/test Pearson correlations vs BT and gold V scores
training_history.csv	Per-epoch training and eval losses

Usage

The example below assumes you have cloned this repository and downloaded the model files from the Hugging Face repo into the current working directory.

from huggingface_hub import snapshot_download
from pathlib import Path
import importlib.util

repo_id = "edsi-umd/Reward-EmoPair"
local_dir = snapshot_download(repo_id=repo_id, repo_type="model")

# Dynamically load the RewardModel class from the downloaded repository
reward_model_path = Path(local_dir) / "RewardModel.py"
spec = importlib.util.spec_from_file_location("reward_model", str(reward_model_path))
reward_mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(reward_mod)
RewardModel = reward_mod.RewardModel

model = RewardModel(model_name="roberta-large", max_length=256)
checkpoint = Path(local_dir) / "pytorch_model.pth"
model.load(str(checkpoint))
model.eval()

scores = model.score_text("I can't believe how amazing this feels!")
print(scores)  # {'valence': ..., 'arousal': ..., 'dominance': ...}

If you prefer to keep the class definition in a separate file, the same snippet works as long as the repository checkout is on sys.path and the checkpoint file is available next to your current working directory.

Citation

If you use this model, please cite the EmoPair paper and dataset using the most recent citation on the GitHub repository, https://github.com/EDSI-UMD-College-Park/EMOPAIR.