---
license: apache-2.0
language:
- en
tags:
- legal
- ohio
- custody
- family-law
- parental-alienation
- bert
- modernbert
- legal-agent-council
- multi-agent
- retrieval
- classification
datasets:
- Roderick3rd/OhioCustodyBERT-corpus
pipeline_tag: text-classification
---

# OhioCustodyBERT

**A domain-specific legal AI system for Ohio family law, child custody, and parental alienation.** Built from the ground up with over 13,000 curated training examples from Ohio statutes, appellate decisions, Supreme Court cases, and legal reference materials.

> ⚠️ **This is a legal research tool, not legal advice.** Always consult a licensed attorney for legal matters.

---

## Two Ways to Use This

### 🟢 Simple: One File, One Command
Download a single GGUF file and run it. No setup, no configuration, no Python required.

```bash
# Download the GGUF from the Qwen adapter repo, then:
ollama create ohio-custody -f Modelfile
ollama run ohio-custody
>>> What are the best interest factors for custody under Ohio law?
```

**What you get**: A Qwen3.5-9B model fine-tuned on 13,346 Ohio family law Q&A pairs. It knows Ohio statutes, case law, and court procedures. Ask it anything about Ohio custody law and it responds with citations.

👉 **[Download the GGUF](https://huggingface.co/Roderick3rd/OhioCustodyBERT-Qwen3.5-9B)**

---

### 🔵 Advanced: The Full Legal Agent Council
Deploy a multi-agent system where specialized AI models cross-examine each other — like having a legal team debate your case. Multiple models, each with a different perspective, produce consensus-based legal analysis.

**What you get**: 6 agents analyzing your question from different angles (statute law, case law, procedure, adversary's argument), a synthesis judge that resolves conflicts, and a verifier that fact-checks every citation. When the models agree, you can be confident. When they disagree, you've found where the law is genuinely unclear.

👉 Keep reading below for the full architecture.

---

## Why This Exists

The Ohio family court system processes thousands of custody cases annually. Parents — especially pro se litigants — face a legal system built on decades of case law, statutes, and procedural rules that are nearly impossible to navigate without expensive legal counsel.

**OhioCustodyBERT aims to democratize access to Ohio family law knowledge** by providing AI models trained specifically on:
- Ohio Revised Code (ORC) Chapter 3109 and related family law statutes
- **Ohio Rules of Evidence (Evid.R. 101-1103)** — authentication, hearsay, cross-examination, expert testimony
- Ohio Administrative Code (OAC) custody and child welfare rules
- Appellate court decisions on custody, parental alienation, and visitation
- US Supreme Court precedent on parental rights (Santosky, Stanley, Troxel)
- Ohio Supreme Court reports, bench cards, and practice guides
- **Trial advocacy techniques** — cross-examination methods, evidence foundation, objection handling

---

## The Legal Agent Council

OhioCustodyBERT is not a single model — it's a **multi-agent legal reasoning system** where specialized AI models work together to provide comprehensive legal analysis. Each model is fine-tuned on the same curated Ohio family law corpus, but serves a different role.

### Architecture

```
User Query
    │
    ▼
┌─────────────────────────────┐
│  ① BERT Retriever           │ ← OhioCustodyBERT (this model)
│  Finds relevant statutes,   │    ModernBERT-base, 149.7M params
│  case law, and rules        │
└──────────┬──────────────────┘
           │ Retrieved context
           ▼
┌─────────────────────────────┐
│  ② Statute Agent            │ ← OhioCustodyBERT-Qwen3.5-9B (LoRA)
│  "What does the law say?"   │    Analyzes applicable statutes
├─────────────────────────────┤
│  ③ Case Law Agent           │ ← Same base, different prompt
│  "What have courts held?"   │    Finds binding precedent
├─────────────────────────────┤
│  ④ Procedure Agent          │ ← Qwen3-30B-A3B (MoE, planned)
│  "Was due process followed?"│    Procedural compliance check
├─────────────────────────────┤
│  ⑤ Adversary Agent          │ ← OhioCustodyBERT-Qwen3.5-9B (LoRA)
│  "What's the other side?"   │    Steel-mans the opposition
└──────────┬──────────────────┘
           │ All analyses
           ▼
┌─────────────────────────────┐
│  ⑥ Synthesis Judge          │ ← QwQ-32B (deep reasoning)
│  Weighs all perspectives,   │    Produces final analysis with
│  resolves conflicts         │    confidence scoring
└──────────┬──────────────────┘
           │ Final analysis
           ▼
┌─────────────────────────────┐
│  ⑦ BERT Verifier            │ ← OhioCustodyBERT (this model)
│  Fact-checks citations,     │    Catches hallucinated case law
│  validates statute refs     │    and incorrect statute numbers
└─────────────────────────────┘
```

### Why Multiple Models?

**Same data, different architectures = meaningful consensus.** When all models agree on a legal analysis, confidence is high. When they disagree, the disagreement reveals where the law is ambiguous or the facts need closer examination — which is exactly what a good lawyer would identify.

---

## Models in This System

| Model | HuggingFace | Role | Architecture |
|-------|-------------|------|-------------|
| **OhioCustodyBERT** | [This repo](https://huggingface.co/Roderick3rd/OhioCustodyBERT) | Retriever + Verifier | ModernBERT-base (149.7M) |
| **OhioCustodyBERT-Qwen3.5-9B** | [Adapter repo](https://huggingface.co/Roderick3rd/OhioCustodyBERT-Qwen3.5-9B) | Generative Agents | Qwen3.5-9B + LoRA |
| **Training Corpus** | [Dataset](https://huggingface.co/datasets/Roderick3rd/OhioCustodyBERT-corpus) | All training data | 13,346 SFT pairs + raw corpus |

---

## This Model: ModernBERT Retriever + Verifier

### What It Does
- **Semantic retrieval**: Find the most relevant statutes, rules, and case law for a given legal question
- **Classification**: Identify legal issues (custody type, parental alienation indicators, due process violations)
- **NER**: Extract legal entities (judge names, case citations, statute references)
- **Citation verification**: Validate that LLM-generated citations point to real cases and statutes

### Model Details

| | |
|---|---|
| **Base** | ModernBERT-base (149.7M params) |
| **Domain** | Ohio family law, child custody, parental alienation |
| **Training** | Masked Language Modeling on curated Ohio legal corpus |
| **Version** | v0.1 (v0.2 training in progress — full corpus) |
| **License** | Apache 2.0 |

---

## Training Corpus (v24 — March 2026)

All models in the Legal Agent Council are trained on the same curated corpus:

### SFT Training Data — 13,346 unique pairs

| Source | Pairs | Description |
|--------|-------|-------------|
| Ohio Revised Code | 4,145 | R.C. 3109, 2919, 2151, 3111, 3127, 3105 — statutes on custody, DV, juvenile, paternity, UCCJEA, divorce |
| Ohio Administrative Code | 3,849 | OAC rules on child welfare, custody evaluation, court procedures |
| Curated Q&A | 2,288 | Hand-audited pairs from opinion analysis, case strategy, verified datasets |
| CourtListener opinions | 1,374 | Ohio appellate court custody and family law decisions |
| Reference materials | 732 | Ohio SC task force reports, bench cards, GAL guides, custody evaluator toolkit |
| Targeted opinions | 560 | Ohio appellate, Ohio SC, and SCOTUS parental rights decisions |
| Sperm donor cases | 154 | R.C. 3111 paternity and donor rights case law |
| OhioFamilyRights | 113 | Family law reform advocacy, legislative testimony, case analyses |
| Juvenile court | 75 | Juvenile court procedures and delinquency |
| Gemini-generated | 56 | Cloud-generated Q&A from corpus documents |

**Generators**: Gemini 2.5 Flash, qwen3-coder:480b, Ollama local models, manual curation

### Raw Corpus
| Source | Records | Size |
|--------|---------|------|
| Ohio Revised Code (19 chapters) | 1,034 sections | 3.0 MB |
| Ohio Administrative Code (19 divisions) | 1,283 rules | 7.3 MB |
| CourtListener opinions (OH App, SC, SCOTUS) | 259 opinions | 14 MB |
| Reference materials | 941 records | 4.0 MB |
| Parental alienation research | 55 papers | — |
| **Ohio Rules of Evidence** | **89 rules (all 11 articles)** | **170 KB** |

### Key Cases Covered
- **SCOTUS**: Santosky v. Kramer, Stanley v. Illinois, Troxel v. Granville, Michael H. v. Gerald D., Lehr v. Robertson
- **Ohio SC**: In re Murray, In re Bonfield, Shearer v. Shearer, Felton, Davis v. Flickinger
- **Topics**: Best interest standard (R.C. 3109.04), shared parenting, parental alienation, GAL standards, custody evaluation, DV protective orders, contempt, UCCJEA jurisdiction, **evidence authentication, hearsay exceptions, cross-examination**

---

## Quick Start

### Using This Model (ModernBERT — Retrieval/Classification)

```python
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Roderick3rd/OhioCustodyBERT")
tokenizer = AutoTokenizer.from_pretrained("Roderick3rd/OhioCustodyBERT")

# Encode legal text for semantic search
text = "What factors does R.C. 3109.04 consider for shared parenting?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1)
```

### Using the Generative Model (Qwen3.5-9B — Legal Analysis)

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base, "Roderick3rd/OhioCustodyBERT-Qwen3.5-9B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
```

### Using via Ollama (Easiest)

```bash
ollama run ohio-custody
>>> What are the best interest factors under R.C. 3109.04?
```

---

## Limitations

- **Ohio-specific**: Trained primarily on Ohio law — may not generalize to other states
- **Not legal advice**: This is a research tool, not a substitute for a licensed attorney
- **v0.1**: Current model trained on limited examples; v0.2 will substantially improve quality
- **Case law currency**: Training data has a cutoff; recent decisions may not be reflected
- **Classification bias**: Reflects the Ohio statutory framework (R.C. 3109.04 best interest factors)

---

## Intended Use

| Use Case | ✅ Appropriate | ❌ Not Appropriate |
|----------|---------------|-------------------|
| Legal research | Identifying relevant statutes and cases | Generating court filings |
| Case preparation | Understanding legal standards and precedent | Making custody recommendations |
| **Trial preparation** | **Practicing cross-examination and evidence presentation** | **Replacing legal counsel** |
| Education | Learning Ohio family law framework | Autonomous legal decision-making |
| Citation checking | Verifying case law references | — |
| **Evidence rules** | **Understanding authentication and hearsay** | **— ** |

---

## Project Status

| Component | Status | Notes |
|-----------|--------|-------|
| ModernBERT v0.1 | ✅ Live | 149.7M params, 864 examples |
| ModernBERT v0.2 | 🔄 Training | Full corpus MLM retraining |
| Qwen3.5-9B LoRA | 🔄 Training | 13,346 pairs, MLX on M4 Pro 48GB |
| v24 Corpus | ✅ Complete | 13,346 pairs from 19 sources |
| Legal Agent Council | 🔄 In Development | 6-agent pipeline, sequential model swap |
| Kaggle Datasets | ✅ Published | `ohio-legal-corpus`, `pa-references`, `ohio-custody-sft-v21` |

---

## Citation

```bibtex
@misc{ohiocustodybert2026,
  title={OhioCustodyBERT: Domain-Specific Legal AI System for Ohio Family Law},
  author={Roderick Mullins},
  year={2026},
  howpublished={HuggingFace: Roderick3rd/OhioCustodyBERT}
}
```

## Related Resources

- **Generative Model**: [OhioCustodyBERT-Qwen3.5-9B](https://huggingface.co/Roderick3rd/OhioCustodyBERT-Qwen3.5-9B) — LoRA adapter for legal analysis generation
- **Training Corpus**: [OhioCustodyBERT-corpus](https://huggingface.co/datasets/Roderick3rd/OhioCustodyBERT-corpus) — Full dataset with SFT pairs
- **Kaggle**: [ohio-legal-corpus](https://www.kaggle.com/datasets/phoenixintelligence/ohio-legal-corpus), [pa-references](https://www.kaggle.com/datasets/phoenixintelligence/pa-references)