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Model Details
Model Provider
iamjayeshc
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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.
| 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 |
Training was performed using the same four manually annotated argument-mining datasets used in Version 1:
Training configuration:
q_proj, k_proj, v_proj, o_projAverage held-out Component F1:
Component F1: 0.219
Compared with ArgParser-v1, this represents approximately a 2× improvement.
Notable improvements include:
Microtext Premise F1:
AbstRCT Empty Prediction Rate:
These results demonstrate the benefits of combining a larger base model with parameter-efficient fine-tuning.
The model extracts structured argumentative information from input text, including:
The output is designed for downstream argument mining and fact-checking pipelines.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerimport torchbase_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:
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."
ArgParser-v2 is the second model in the ArgParser development series.
Other versions:
ArgParser-v1-Qwen0.5B
ArgParser-v3-Qwen1.5B
ArgParser-v4-Qwen1.5B (Recommended)
For most applications, ArgParser-v4 is recommended, as it incorporates cross-domain adaptation for PolitiFact/LIARArg-style claims.
Although significantly stronger than Version 1, this model still relies exclusively on academic argument-mining corpora for training.
Consequently, it may struggle with:
These limitations motivated the development of Versions 3 and 4.
This model is part of the ArgParser research project investigating efficient argument-structure extraction using progressively improved lightweight language models.
Released by Jayesh Choudhari.
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.