ArgParser-v1-Qwen0.5B
Version 1 of the ArgParser model series — the initial baseline developed for structured argument extraction using a compact Qwen model.
ArgParser-v1-Qwen0.5B is the first model in the ArgParser research series. It serves as the baseline for evaluating lightweight argument-structure extraction models and demonstrates the feasibility of extracting structured argumentative information using a small language model.
This version performs a full fine-tuning of Qwen2.5-0.5B-Instruct on four academic argument-mining datasets and establishes the foundation for the later LoRA-based models in the series.
Model Overview
| Property | Value |
|---|---|
| Model | ArgParser-v1-Qwen0.5B |
| Base Model | Qwen/Qwen2.5-0.5B-Instruct |
| Task | Argument Structure Extraction |
| Training Method | Full Fine-tuning |
| Author | Jayesh Choudhari |
| License | Apache-2.0 |
Training Configuration
This model was trained using four manually annotated academic argument-mining corpora:
- AbstRCT
- Microtext
- CDCP
- PERSPECTRUM
Training configuration:
- Training Samples: 1,494
- Epochs: 3
- Optimizer: Adafactor
- Precision: FP16
- Hardware: NVIDIA GTX 1080 Ti
- Training Time: ~1.5 hours
Performance
Average held-out Component F1 across the four datasets:
Component F1: 0.108
Highlights:
- Best performance:
- CDCP Claim Extraction → 0.501 F1
- Weakest performance:
- PERSPECTRUM
- Approximately 91% empty predictions
As expected, this early baseline struggles to generalize beyond academic argument-mining datasets.
Intended Task
The model extracts structured argumentative information from input text, including:
- Claims
- Premises
- Citations
- Argument relations
The generated output follows a structured JSON format suitable for downstream NLP pipelines.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "iamjayeshc/ArgParser-v1-Qwen0.5B"
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."
Model Series
ArgParser-v1 is the first model in the development series.
If you're looking for improved performance, consider one of the later versions:
ArgParser-v2-Qwen1.5B
ArgParser-v3-Qwen1.5B
ArgParser-v4-Qwen1.5B (Recommended)
ArgParser-v4 is the final distilled model in this series and provides substantially better downstream performance on the target fact-checking task.
Limitations
This model represents the initial baseline and has several limitations:
- Trained only on academic argument-mining corpora.
- Limited transfer to informal language such as social media or political fact-checking claims.
- Lower extraction accuracy compared with later versions.
It is primarily provided for research reproducibility and comparison with subsequent iterations.
Acknowledgements
This model is part of the ArgParser research project investigating lightweight argument-structure extraction through progressively improved model distillation and domain adaptation.
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 will be released in the future.
- Downloads last month
- 19