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SambhavSBU/
argument-quality-roberta-v2

Name: argument-quality-roberta-v2
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Argument Quality Ranking - RoBERTa v2

Fine-tuned RoBERTa-base for pairwise argument quality ranking. Given two arguments on the same debate topic, the model predicts which is higher quality.

Model Details

Base model: roberta-base
Task: Pairwise argument quality classification (A wins / B wins)
Training data: IBM ArgQ corpus (3,587 pairs, 60 topics)
Input format: [CLS] topic [SEP] arg_a [SEP] arg_b

Improvements over v1

Topic prepended to input
Pair-flipping data augmentation (doubles training data)
Label smoothing (0.1)
Layerwise learning rate decay (factor 0.9)
Bottom 6 layers frozen

Performance

Split	Accuracy	F1	Precision	Recall
In-domain	63.7%	0.587	0.684	0.514
Cross-topic	62.8%	0.594	0.688	0.522

Usage

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("SambhavSBU/argument-quality-roberta-v2")
model = AutoModelForSequenceClassification.from_pretrained("SambhavSBU/argument-quality-roberta-v2")

topic = "We should ban social media"
arg_a = "Social media spreads misinformation at an unprecedented scale."
arg_b = "Social media connects people across the world."

inputs = tokenizer(
    topic + " [SEP] " + arg_a, arg_b,
    return_tensors="pt", truncation=True, max_length=256
)
with torch.no_grad():
    logits = model(**inputs).logits
winner = "A" if logits.argmax() == 0 else "B"
print(f"Higher quality argument: {winner}")

Citation

Code and full experiments: https://github.com/Sambhav101/Argument-Quality-Ranking