Update model card with published paper citation

README.md

@@ -4,6 +4,7 @@ datasets:
 - billingsmoore/tibetan-asr
 language:
 - bo
+license: cc-by-4.0
 tags:
 - asr
 - tibetan
@@ -12,8 +13,15 @@ tags:
 
 # tibetan-asr-whisper-tiny-lora
 
-Fine-tuned on the [NICT-Tib1](https://huggingface.co/datasets/billingsmoore/tibetan-asr) Tibetan speech dataset, one of the
-models evaluated in a benchmarking study of Tibetan ASR architectures.
+Fine-tuned on the [NICT-Tib1](https://huggingface.co/datasets/billingsmoore/tibetan-asr) Tibetan speech dataset,
+one of five architectures (Whisper Tiny/Base/Small, Wav2Vec2, HuBERT) and six adaptation strategies
+(standard fine-tuning, 8-bit/4-bit quantization, LoRA, and 8-bit/4-bit QLoRA) benchmarked in:
+
+> J. Moore, S. Li and P. Lauren, "Evaluating Tibetan ASR With Segmented Word Error Rate: Beyond Character-Level Metrics," in *IEEE Access*, vol. 14, pp. 101790-101805, 2026, doi: 10.1109/ACCESS.2026.3709206.
+
+The paper introduces Segmented Word Error Rate (SWER) — a word-level evaluation framework for
+Tibetan ASR built on automatic segmentation — and reports CER, SER, and SWER results for every
+model/adaptation combination. See the paper for full benchmark tables and metric analysis.
 
 - **Base model:** `openai/whisper-tiny`
 - **Dataset:** `billingsmoore/tibetan-asr`
@@ -21,4 +29,18 @@ models evaluated in a benchmarking study of Tibetan ASR architectures.
 
 This repo contains a LoRA adapter (via [PEFT](https://github.com/huggingface/peft)) fine-tuned on top of the base model above. Load it with `peft`'s `PeftModel.from_pretrained(base_model, this_repo)`.
 
-This model is private, pending publication of the associated paper.
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@ARTICLE{moore2026tibetanasr,
+  author={Moore, Jacob and Li, Sheng and Lauren, Paula},
+  journal={IEEE Access},
+  title={Evaluating Tibetan {ASR} With Segmented Word Error Rate: Beyond Character-Level Metrics},
+  year={2026},
+  volume={14},
+  pages={101790-101805},
+  doi={10.1109/ACCESS.2026.3709206}
+}
+```