Add model card

README.md

@@ -1,56 +1,91 @@
 ---
-library_name: transformers
 license: apache-2.0
-base_model: answerdotai/ModernBERT-base
+language:
+- en
 tags:
-- generated_from_trainer
-model-index:
-- name: OhioCustodyBERT
-  results: []
+- legal
+- custody
+- family-law
+- ohio
+- modernbert
+- mlm
+base_model: answerdotai/ModernBERT-base
+datasets:
+- Roderick3rd/OhioCustodyBERT-corpus
+pipeline_tag: fill-mask
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # OhioCustodyBERT
 
-This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
+A domain-adapted language model for **Ohio family law and child custody** cases, fine-tuned from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base).
+
+## Overview
+
+| | |
+|---|---|
+| **Base Model** | answerdotai/ModernBERT-base (149.7M params) |
+| **Domain** | Ohio family law, child custody, domestic relations |
+| **Training** | Masked Language Modeling (MLM) on custody case corpus |
+| **Hardware** | 2× NVIDIA Tesla T4 (Kaggle) |
+| **Corpus** | 864 curated custody case analyses |
+| **Developer** | [Phoenix Intelligence LLC](https://github.com/rodneymullins) |
+
+## Training Data
+
+Fine-tuned on the [OhioCustodyBERT-corpus](https://huggingface.co/datasets/Roderick3rd/OhioCustodyBERT-corpus), which contains structured analyses of Ohio custody cases including:
+
+- **Case excerpts** from Ohio appellate decisions (e.g., *In re M.J.C.*, *Davis v. Davis*)
+- **Best-interest factor analysis** under Ohio Revised Code § 3109.04
+- **Guardian ad litem recommendations**
+- **Custody modification standards**
+- **Shared parenting plan evaluations**
 
-## Model description
+## Usage
 
-More information needed
+```python
+from transformers import pipeline
 
-## Intended uses & limitations
+fill = pipeline("fill-mask", model="Roderick3rd/OhioCustodyBERT")
 
-More information needed
+# Predict custody terms
+result = fill("The court granted [MASK] custody to the mother.")
+for r in result[:3]:
+    print(f"{r['token_str']:15s} ({r['score']:.3f})")
+```
 
-## Training and evaluation data
+## Use Cases
 
-More information needed
+- **Legal research**: Auto-complete and suggest custody-relevant legal terminology
+- **Document analysis**: Enhanced understanding of family law documents
+- **Case prediction**: Feature extraction for custody outcome modeling
+- **RAG pipelines**: Better embeddings for custody-related retrieval
 
-## Training procedure
+## Training Details
 
-### Training hyperparameters
+- **Epochs**: 3
+- **Batch size**: 4 per device × 32 gradient accumulation = 128 effective
+- **Learning rate**: 5e-5 with cosine schedule
+- **Precision**: bf16 mixed precision
+- **Gradient checkpointing**: Enabled
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 4
-- eval_batch_size: 16
-- seed: 42
-- gradient_accumulation_steps: 32
-- total_train_batch_size: 128
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 10
-- num_epochs: 3
+## Limitations
 
-### Training results
+- Trained on Ohio-specific case law; may not generalize to other jurisdictions
+- Small corpus (864 examples) — best used as a starting point for further fine-tuning
+- Not a substitute for legal advice
 
+## License
 
+Apache 2.0
 
-### Framework versions
+## Citation
 
-- Transformers 5.2.0
-- Pytorch 2.9.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.2
+```bibtex
+@misc{ohiocustodybert2026,
+  title={OhioCustodyBERT: Domain-Adapted Language Model for Ohio Family Law},
+  author={Phoenix Intelligence LLC},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Roderick3rd/OhioCustodyBERT}
+}
+```