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license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
language:
- en
pipeline_tag: text-generation
tags:
- argument-mining
- fact-checking
- information-extraction
- qwen2
- peft
- lora
- research
---
# 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
```python
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:
```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-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**
- https://huggingface.co/iamjayeshc/ArgParser-v2-Qwen1.5B
- **ArgParser-v3-Qwen1.5B**
- https://huggingface.co/iamjayeshc/ArgParser-v3-Qwen1.5B
- **ArgParser-v4-Qwen1.5B (Recommended)**
- https://huggingface.co/iamjayeshc/ArgParser-v4-Qwen1.5B
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. |