ArgParser-v2-Qwen1.5B

Version 2 of the ArgParser model series — introducing LoRA fine-tuning and a larger Qwen model, resulting in a substantial improvement over the initial baseline.

ArgParser-v2-Qwen1.5B builds upon the foundation established by ArgParser-v1 by replacing the 0.5B base model with Qwen2.5-1.5B-Instruct and adopting LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning.

While trained on the same four academic argument-mining datasets, this version demonstrates that scaling the base model and using LoRA significantly improves structured argument extraction performance.

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Model Overview

Property	Value
Model	ArgParser-v2-Qwen1.5B
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Task	Argument Structure Extraction
Training Method	LoRA Fine-tuning
LoRA Rank	16
Author	Jayesh Choudhari
License	Apache-2.0

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Training Configuration

Training was performed using the same four manually annotated argument-mining datasets used in Version 1:

• AbstRCT
• Microtext
• CDCP
• PERSPECTRUM

Training configuration:

• Training Samples: 1,494
• Epochs: 3
• LoRA Rank: 16
• LoRA Alpha: 32
• Dropout: 0.05
• Target Modules: q_proj, k_proj, v_proj, o_proj
• Trainable Parameters: ~4.4 Million
• Hardware: NVIDIA GTX 1080 Ti
• Training Time: ~13.5 hours

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Performance

Average held-out Component F1:

Component F1: 0.219

Compared with ArgParser-v1, this represents approximately a 2× improvement.

Notable improvements include:

• Microtext Premise F1:

  • 0.000 → 0.680

• AbstRCT Empty Prediction Rate:

  • 75% → 50%

These results demonstrate the benefits of combining a larger base model with parameter-efficient fine-tuning.

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Intended Task

The model extracts structured argumentative information from input text, including:

• Claims
• Premises
• Citations
• Supporting relations
• Attacking relations

The output is designed for downstream argument mining and fact-checking pipelines.

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Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

base_id = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_id = "iamjayeshc/ArgParser-v2-Qwen1.5B"

tokenizer = AutoTokenizer.from_pretrained(base_id)

base = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

model = PeftModel.from_pretrained(base, adapter_id)

Example prompt:

instruction = (
    "Extract all argument components and relations from the text. "
    "Return strict JSON containing "
    "claim_components, premise_components, citation_components and relations."
)

text = "The Obama administration is putting Border Patrol agents in a chokehold."

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Model Series

ArgParser-v2 is the second model in the ArgParser development series.

Other versions:

• ArgParser-v1-Qwen0.5B

  • Baseline full fine-tuning
  • https://huggingface.co/iamjayeshc/ArgParser-v1-Qwen0.5B

• ArgParser-v3-Qwen1.5B

  • Continual training with an additional argument-mining corpus
  • https://huggingface.co/iamjayeshc/ArgParser-v3-Qwen1.5B

• ArgParser-v4-Qwen1.5B (Recommended)

  • Final distilled model with target-domain adaptation
  • https://huggingface.co/iamjayeshc/ArgParser-v4-Qwen1.5B

For most applications, ArgParser-v4 is recommended, as it incorporates cross-domain adaptation for PolitiFact/LIARArg-style claims.

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Limitations

Although significantly stronger than Version 1, this model still relies exclusively on academic argument-mining corpora for training.

Consequently, it may struggle with:

• Informal social media language
• Political claims
• Domain-specific argument structures

These limitations motivated the development of Versions 3 and 4.

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Acknowledgements

This model is part of the ArgParser research project investigating efficient argument-structure extraction using progressively improved lightweight language models.

Released by Jayesh Choudhari.

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Citation

If you use this model in your research, please cite the associated project when available.

A formal technical report / preprint will be released in the future.