Add IndicBERT methodology credit and acknowledgments

README.md

@@ -238,6 +238,42 @@ Family law involves sensitive matters affecting children and families. Any appli
 
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+## Acknowledgments & Methodology Credits
+
+Our domain-adaptive pre-training approach draws inspiration from several key works:
+
+### AI4Bharat / IndicBERT
+
+Our training methodology follows the **domain-adaptive MLM fine-tuning** paradigm demonstrated by [AI4Bharat's IndicBERT](https://huggingface.co/ai4bharat/indic-bert) project. Key parallels:
+
+| IndicBERT | OhioCustodyBERT |
+|-----------|-----------------|
+| Pre-trained ALBERT on 8.9B tokens across 12 Indian languages | Fine-tuned ModernBERT on Ohio custody case corpus |
+| Masked Language Modeling (MLM) objective | Masked Language Modeling (MLM) objective |
+| Domain-specific corpus curation | Domain-specific corpus curation (legal/custody) |
+| Curriculum learning phases | Planned multi-phase training (v0.2+) |
+| Efficient training with limited compute | Trained on free-tier Kaggle 2×T4 GPUs |
+
+IndicBERT demonstrated that **focused domain adaptation on a well-curated corpus** can outperform larger general-purpose models — a principle we apply to the legal domain. Their curriculum learning approach (foundation → high-resource → low-resource) will inform our planned training phases:
+
+1. **Phase 1** (current): Broad legal language modeling on custody cases
+2. **Phase 2** (planned): Expanded corpus with CourtListener + GovInfo federal law
+3. **Phase 3** (planned): Ohio-specific statutes and recent slip opinions
+
+```bibtex
+@inproceedings{kakwani2020indicnlpsuite,
+    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
+    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satber Gopi and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
+    year={2020},
+    booktitle={Findings of EMNLP},
+}
+```
+
+### ModernBERT (Answer.AI)
+
+Our base model, [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), provides the architectural foundation with modern improvements including RoPE, Flash Attention, and 8,192-token context. See citation in the [Citation](#citation) section.
+
+
 ## License
 
 Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base ModernBERT model.