argument_quality_ranking_regressor

BERT Base (uncased) fine-tuned on Argument Quality Ranking

This model is a fine-tuned version of bert-base-uncased on the IBM Argument Quality Ranking dataset. It predicts the ratings of arguments as a integer between 1 and 5.

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Model Details

• Model type: BERT (base, uncased)
• Fine-tuned on: IBM Argument Quality Ranking (~30k arguments)
• Task: Regression (argument quality score)
• Output: Integer between 1-5
• Training framework: 🤗 Transformers

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Training

• Epochs: 3
• Batch size: 16
• Learning rate: 2e-5
• Optimizer: AdamW
• Evaluation metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE)

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Evaluation Results

On the test set:

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How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np

model_name = "ByteMeHarder-404/bert-base-uncased-finetuned-arg-quality"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

def predict_quality(arguments):
    inputs = tokenizer(arguments, truncation=True, padding=True, return_tensors="pt")
    device = next(model.parameters()).device
    inputs = {k: v.to(device) for k, v in inputs.items()}
    model.eval()
    with torch.no_grad():
        outputs = model(**inputs)
        preds = round(outputs.logits.squeeze().cpu().numpy()*4+1)
    return preds

# Example
args = [
    "School uniforms reduce individuality.",
    "World Peace is great",
    "Homework improves student learning outcomes."
]

print("Ratings:", predict_rating(args))  # Output: 1–5 ratings