---
license: apache-2.0
datasets:
- CFPB/consumer-finance-complaints
language:
- en
metrics:
- rouge
- bleu
- f1
- meteor
base_model:
- Qwen/Qwen2.5-7B-Instruct
new_version: aryachakraborty/arya-cfpb-qwen_2.5-7b-lora-V2
library_name: transformers
tags:
- finan
- AMD
- lora
- peft
---

# Intelligent Complaint Triage using Fine-Tuned Qwen2.5-7B

## The Problem

Financial institutions process thousands of customer complaints daily across mobile apps, websites, contact centres, email, and regulatory portals. These complaints arrive as free-form text and must be manually categorized before investigation and resolution can begin.

The result is predictable:

- Complaints are routed to the wrong operational teams
- Manual review effort increases
- Resolution times become longer
- Customer experience deteriorates
- Regulatory complaint handling becomes more expensive

Traditional classifiers typically predict one label at a time and struggle with the nuanced language used in consumer finance complaints.

This project addresses the problem as a structured generation task. A single model call extracts all required complaint taxonomy fields simultaneously.

---

## What the Model Does

Given a customer complaint narrative, the model generates:

```json
{
  "product": "Checking or savings account",
  "sub_product": "Checking account",
  "issue": "Unauthorized transactions or other transaction problem",
  "sub_issue": "Debit card issue"
}
```

These fields map directly to the CFPB complaint taxonomy and can be consumed by routing systems, workflow engines, complaint management platforms, and analytics pipelines.

---

## Model Details

| Property | Value |
|----------|-------|
| Base Model | Qwen/Qwen2.5-7B-Instruct |
| Fine-Tuning Method | LoRA (PEFT) |
| Training Hardware | AMD Instinct MI300X |
| Precision | bfloat16 |
| Task Type | Structured JSON Generation |
| Output Format | CFPB Taxonomy JSON |

---

## Training Configuration

### LoRA Adapter

| Parameter | Value |
|-----------|-------|
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target Modules | q_proj, k_proj, v_proj, o_proj |

Only LoRA adapter weights were updated during training.

### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| Epochs | 5 |
| Batch Size / Device | 8 |
| Gradient Accumulation | 4 |
| Effective Batch Size | 32 |
| Learning Rate | 1e-4 |
| Optimizer | AdamW |
| Scheduler | Linear |
| Precision | bf16 |
| Max Sequence Length | 1024 |

---

## Training Convergence

| Step | Training Loss | Validation Loss |
|--------|--------|--------|
| 100 | 1.7377 | 1.7092 |
| 200 | 1.6316 | 1.6485 |
| 300 | 1.6508 | 1.6295 |
| 400 | 1.6078 | 1.6204 |
| 500 | 1.6090 | 1.6145 |
| 600 | 1.6191 | 1.6101 |
| 700 | 1.5926 | 1.6058 |
| 800 | 1.6128 | 1.6034 |
| 900 | 1.6076 | 1.6012 |
| 1000 | 1.5874 | 1.5997 |
| 1100 | 1.6001 | 1.5984 |

Validation loss steadily decreased from **1.709 → 1.598**, demonstrating successful adaptation of the base model to the CFPB complaint taxonomy.

---

## Dataset

Source: CFPB Consumer Complaint Database

The model was trained to predict four operational complaint fields:

1. Product
2. Sub-Product
3. Issue
4. Sub-Issue

The task is formulated as structured JSON generation rather than independent classification.

---

## Inference with Constrained Decoding

Inference uses a two-stage approach:

### Stage 1

The fine-tuned model generates structured JSON.

### Stage 2

Generated values are aligned to the nearest canonical CFPB label using TF-IDF similarity matching.

This improves robustness when the model generates labels that are semantically correct but differ slightly from official CFPB terminology.

---

## Evaluation Results

Evaluated on 250 held-out CFPB complaints.

Baseline refers to the original Qwen2.5-7B-Instruct model without fine-tuning.

### Product Classification Performance

| Metric | Baseline | Fine-Tuned | Improvement |
|----------|----------|----------|----------|
| Exact Match | 0.0100 | 0.9080 | +0.8980 |
| Precision | 0.5180 | 0.9082 | +0.3902 |
| Recall | 0.0100 | 0.9080 | +0.8980 |
| F1 Score | 0.0196 | 0.9068 | +0.8872 |

### Sub-Product Semantic Similarity

| Metric | Baseline | Fine-Tuned | Improvement |
|----------|----------|----------|----------|
| ROUGE-1 | 0.0041 | 0.7122 | +0.7081 |
| ROUGE-2 | 0.0030 | 0.6452 | +0.6422 |
| ROUGE-L | 0.0041 | 0.7122 | +0.7081 |
| BLEU | 0.0000 | 0.5026 | +0.5026 |

### Issue Semantic Similarity

| Metric | Baseline | Fine-Tuned | Improvement |
|----------|----------|----------|----------|
| ROUGE-1 | 0.0018 | 0.4018 | +0.4000 |
| ROUGE-2 | 0.0000 | 0.3463 | +0.3463 |
| ROUGE-L | 0.0018 | 0.4013 | +0.3995 |
| BLEU | 0.0000 | 0.3368 | +0.3368 |

### Sub-Issue Semantic Similarity

| Metric | Baseline | Fine-Tuned | Improvement |
|----------|----------|----------|----------|
| ROUGE-1 | 0.0004 | 0.5215 | +0.5211 |
| ROUGE-2 | 0.0000 | 0.4895 | +0.4895 |
| ROUGE-L | 0.0004 | 0.5207 | +0.5203 |
| BLEU | 0.0000 | 0.2283 | +0.2283 |

---

## Final Results Summary

| Category | Base Qwen2.5-7B | Fine-Tuned CFPB Model |
|-----------|----------------|----------------------|
| Product Classification (Exact Match) | 1.0% | 90.8% |
| Product F1 Score | 1.96% | 90.7% |
| Sub-Product ROUGE-L | 0.004 | 0.712 |
| Issue ROUGE-L | 0.002 | 0.401 |
| Sub-Issue ROUGE-L | 0.000 | 0.521 |
| Output Structure | Inconsistent | Reliable CFPB JSON |
| Taxonomy Alignment | Poor | High |
| Training Time | ~45 Minutes | ~45 Minutes |
| Inference Latency | Baseline | Near Identical |
| Additional GPU Memory | Baseline | ~50 MB Adapter |

### Run inference

```python
def categorise_complaint(complaint_text: str, model, tokenizer) -> dict:
    messages = [
        {
            "role": "system",
            "content": (
                "You are a banking complaint classification assistant. "
                "Given a consumer complaint narrative, extract the CFPB ticket fields "
                "as a JSON object with keys: product, sub_product, issue, sub_issue."
            ),
        },
        {
            "role": "user",
            "content": complaint_text,
        },
    ]

    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=128,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
        )

    prompt_len = inputs["input_ids"].shape[1]
    generated  = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
    return generated


complaint = """
I reported fraudulent transactions on my debit card and the bank reversed
my provisional credit without explaining the investigation outcome.
"""

result = categorise_complaint(complaint, model, tokenizer)
print(result)
# {"product": "Checking or savings account", "sub_product": "Checking account",
#  "issue": "Unauthorized transactions or other transaction problem",
#  "sub_issue": "Debit card issue"}
```

---

## Dependencies

```
transformers==4.44.0
peft==0.12.0
accelerate==0.34.0
datasets==2.21.0
torch (ROCm-compatible build for AMD, or standard CUDA build)
scikit-learn
rouge-score
sacrebleu
nltk
```

---

## Limitations

- **CFPB taxonomy only.** The model is trained on and constrained to CFPB Consumer Complaint Database labels. It is not a general-purpose complaint classifier and should not be used with complaint taxonomies from other regulatory bodies or internal systems without retraining.
- **Issue field accuracy.** The `issue` field (33.6% accuracy) is the weakest link. The CFPB issue taxonomy contains 80+ canonical strings with overlapping phrasing. Expanding training data and further tuning the constrained decoder are the most direct paths to improvement.
- **English language only.** All training data is in English. Performance on non-English complaints is untested and likely poor.
- **Context length.** Complaints longer than 1024 tokens will be truncated. Most CFPB complaints are well within this limit, but very long narratives may lose relevant context.

---

## Intended Use

This model is intended for use by:
- Banking operations teams automating first-touch complaint categorisation
- Compliance teams processing regulatory complaint filings
- Contact centre platforms routing incoming complaints before agent assignment
- Research teams studying LLM adaptation for financial NLP tasks

It is not intended for consumer-facing deployment without human review of outputs, or for use in jurisdictions where automated complaint classification decisions have legal or regulatory implications without appropriate oversight.

---

## Training Infrastructure

Trained on an AMD Instinct MI300X GPU (192 GB HBM3 VRAM) running ROCm 7.2.4. The training stack is fully ROCm-native — `bitsandbytes` (CUDA-only) is not used. Model precision is bfloat16, which is the native compute type for the CDNA3 architecture.

---

## Citation

If you use this model in research or production, please cite the CFPB Consumer Complaint Database as the data source:

```
Consumer Financial Protection Bureau (CFPB)
Consumer Complaint Database
https://www.consumerfinance.gov/data-research/consumer-complaints/
```