sergiopinto/deberta-v3-large-claim-checkworthiness-seed0

DeBERTa-v3-Large for Claim Checkworthiness Detection (Seed 0)

This model is fine-tuned from microsoft/deberta-v3-large for claim checkworthiness detection as part of the ExplainableACD project for IJCAI 2026.

Model Description

• Base Model: microsoft/deberta-v3-large
• Task: Binary classification (checkworthy vs non-checkworthy claims)
• Training Seed: 0 (part of 3-seed ensemble)
• Fine-tuning Dataset: ClaimTruth 2024 Task 1 (CT24)
• Training Framework: PyTorch + Transformers

Training Configuration

Hyperparameters:

• Learning rate: 2e-5
• Batch size: 8 (effective: 32 with gradient accumulation)
• Epochs: 5
• Max sequence length: 128
• Optimizer: AdamW with cosine schedule

Advanced Techniques:

• Focal Loss (γ=2.0): Handles class imbalance
• LLRD (Layer-wise Learning Rate Decay, α=0.9): Different learning rates per layer
• R-Drop (α=1.0): Regularization via dropout consistency
• FGM (Fast Gradient Method, ε=1.0): Adversarial training
• BF16 Precision: Mixed-precision training on NVIDIA A10 (24GB VRAM) GPU

Performance

Development Set

• F1: 96.68%
• Accuracy: 98.45%
• Precision: 95.49%
• Recall: 97.90%
• Optimal Threshold: 0.55

Test Set (Held-out)

• F1: 80.46%
• Accuracy: 90.03%
• Precision: 81.40%
• Recall: 79.55%
• Optimal Threshold: 0.50

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "sergiopinto/deberta-v3-large-claim-checkworthiness-seed0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example claim
claim = "The president announced a new economic policy yesterday."

# Tokenize and predict
inputs = tokenizer(claim, return_tensors="pt", max_length=128, truncation=True)
with torch.no_grad():
    outputs = model(**inputs)
    probs = torch.softmax(outputs.logits, dim=1)

# Get prediction
checkworthy_prob = probs[0][1].item()
is_checkworthy = checkworthy_prob > 0.50

print(f"Checkworthy probability: {checkworthy_prob:.4f}")
print(f"Is checkworthy: {is_checkworthy}")

Ensemble Usage

This model is seed 0 of a 3-seed ensemble. For best performance, combine with:

• sergiopinto/deberta-v3-large-claim-checkworthiness-seed42
• sergiopinto/deberta-v3-large-claim-checkworthiness-seed456

Late fusion ensemble achieves ~83.6% F1 on test set (3% improvement over single seed).

Training Infrastructure

• GPU: NVIDIA A10 (24GB VRAM)
• Training Time: ~1.5 hours
• Framework: PyTorch 2.x + Transformers 4.x
• Precision: BF16 mixed precision

Citation

If you use this model, please cite:

@inproceedings{pinto2026explainableacd,
  title={Explainable Automatic Claim Detection for Real-Time Fact-Checking},
  author={Pinto, Sérgio and [Co-authors]},
  booktitle={Proceedings of the 35th International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2026}
}

License

MIT License

Contact

• Author: Sérgio Pinto
• Project: ExplainableACD (IJCAI 2026)
• Organization: Verefy

Model Card

• Developed by: Sérgio Pinto
• Model type: DeBERTa-v3-Large (Transformer)
• Language: English
• Finetuned from: microsoft/deberta-v3-large
• Task: Claim Checkworthiness Detection