---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
language:
- en
pipeline_tag: text-generation
tags:
- argument-mining
- fact-checking
- information-extraction
- lora
- qwen2
- peft
- research
---

# ArgParser-v3-Qwen1.5B

> **Version 3 of the ArgParser model series** — continual fine-tuning with an additional argument-mining corpus to investigate the limits of domain transfer.

ArgParser-v3-Qwen1.5B extends **ArgParser-v2** through continual training after introducing an additional academic argument-mining corpus (AAEC). The objective was to determine whether increasing the diversity of gold-standard argument annotations would improve generalization to real-world fact-checking data.

While this version achieved modest improvements on in-domain benchmarks, experiments demonstrated that academic argument-mining datasets alone were insufficient for effective transfer to PolitiFact-style claims. This finding directly motivated the development of **ArgParser-v4**.

---

# Model Overview

| Property | Value |
|----------|-------|
| Model | ArgParser-v3-Qwen1.5B |
| Base Model | Qwen/Qwen2.5-1.5B-Instruct |
| Task | Argument Structure Extraction |
| Training Method | Continual LoRA Fine-tuning |
| LoRA Rank | 16 |
| Author | **Jayesh Choudhari** |
| License | Apache-2.0 |

---

# Training Configuration

ArgParser-v3 continues training from Version 2 after incorporating an additional argument-mining dataset.

Training datasets:

- AbstRCT
- Microtext
- CDCP
- PERSPECTRUM
- AAEC (402 persuasive essays)

Training configuration:

- **Training Samples:** 1,823
- **Continual Training:** 1 additional epoch
- **Total Effective Learning:** ~4 epochs including Version 2
- **LoRA Rank:** 16
- **Hardware:** NVIDIA GTX 1080 Ti
- **Training Time:** ~5.5 hours

---

# Performance

Average held-out Component F1:

**Component F1:** **0.229**

Compared with Version 2:

- Small overall improvement (**0.219 → 0.229**)
- Improved performance on:
  - Microtext
  - AbstRCT
- Slight regression on:
  - PERSPECTRUM (**0.056 → 0.034**)

These results suggest diminishing returns when simply increasing the amount of academic argument-mining data.

---

# Cross-Domain Evaluation

A key experiment evaluated ArgParser-v3 on the target **LIARArg** dataset containing PolitiFact-style political claims.

Results showed:

- Approximately **83% empty predictions** on the initial evaluation subset.
- Remaining outputs were often incomplete or fragmented.
- The experiment was terminated early after confirming poor cross-domain transfer.

This demonstrated that academic argument-mining corpora alone do not adequately prepare small language models for real-world political fact-checking tasks.

These findings motivated the development of **ArgParser-v4**, which introduces target-domain silver annotations generated by a larger teacher model.

---

# Intended Task

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

- Claims
- Premises
- Citations
- Supporting relations
- Attacking relations

Outputs follow a structured JSON format suitable for downstream argument mining pipelines.

---

# Usage

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

base_id = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_id = "iamjayeshc/ArgParser-v3-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:

```python
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."
```

---

# Model Series

ArgParser-v3 is the third model in the ArgParser development series.

Other versions:

- **ArgParser-v1-Qwen0.5B**
  - Initial full fine-tuning baseline
  - https://huggingface.co/iamjayeshc/ArgParser-v1-Qwen0.5B

- **ArgParser-v2-Qwen1.5B**
  - Larger base model with LoRA fine-tuning
  - https://huggingface.co/iamjayeshc/ArgParser-v2-Qwen1.5B

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

ArgParser-v4 incorporates silver annotations generated by a large teacher model and substantially improves transfer to PolitiFact-style argument extraction.

---

# Limitations

This version demonstrates that expanding academic training data alone is insufficient for robust cross-domain argument extraction.

It may struggle with:

- Political claims
- Informal social media language
- Long-form real-world arguments

The model is primarily released for **research reproducibility** and comparison with subsequent versions.

---

# Acknowledgements

This model is part of the **ArgParser** research project investigating lightweight argument-structure extraction through continual training, domain adaptation, and knowledge distillation.

Released by **Jayesh Choudhari**.

---

# Citation

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

A formal technical report / preprint is planned.