Clarify model licensing and cite the AspectBench article

CITATION.cff

@@ -0,0 +1,28 @@
+cff-version: 1.2.0
+message: "If you use AspectBench, please cite the accompanying article."
+title: "AspectBench: Document-Level Aspect-Based Sentiment Analysis"
+type: software
+authors:
+  - family-names: Chatterjee
+    given-names: Nishan
+repository-code: "https://github.com/nishan-chatterjee/aspect-based-sentiment-analysis"
+url: "https://github.com/nishan-chatterjee/aspect-based-sentiment-analysis"
+preferred-citation:
+  type: article
+  authors:
+    - family-names: Chatterjee
+      given-names: Nishan
+    - family-names: Koloski
+      given-names: Boshko
+    - family-names: Doucet
+      given-names: Antoine
+    - family-names: Pollak
+      given-names: Senja
+    - family-names: Purver
+      given-names: Matthew
+  journal: "Frontiers in Artificial Intelligence"
+  volume: "9"
+  year: 2026
+  title: "Evaluating Fine-Tuned, Embedding-Based, and Zero-Shot Models for Aspect-Based Sentiment Analysis in South Slavic News"
+  doi: "10.3389/frai.2026.1844418"
+  url: "https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1844418/abstract"

LICENSE

@@ -0,0 +1,22 @@
+## License
+
+Copyright (c) 2026 the AspectBench model contributors.
+
+The AspectBench fine-tuned checkpoint contributions / trained heads are licensed under
+[Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).
+The [official legal code](https://creativecommons.org/licenses/by-nc/4.0/legalcode.en) is incorporated by reference.
+Noncommercial research, evaluation, adaptation and redistribution are allowed
+subject to attribution and the license terms. Commercial use is not granted
+under this license; contact the project for separate permission.
+
+Noncommercial describes the purpose of a use, not whether its user is a
+university or a company. Academic affiliation does not automatically make a
+commercial project noncommercial. The legal code controls, including its
+exceptions and limitations. The material is provided as-is without warranties.
+
+This notice does not relicense third-party base weights, tokenizers, code or
+configuration assets, and does not revoke any rights previously granted.
+AspectBench adapted the listed base models for aspect-based sentiment analysis;
+retain their original attribution and license notices when redistributing.
+
+- HBS and Slovenian: [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) — upstream MIT.

README.md

@@ -4,7 +4,7 @@ tags:
 - aspect-based-sentiment-analysis
 - south-slavic
 - text-classification
-license: other
+license: cc-by-nc-4.0
 ---
 
 # AspectBench HAN-XLM-R
@@ -14,6 +14,29 @@ sentiment analysis. This repository contains 4/4 language-mode
 checkpoint slots. It is used with the shared inference toolkit in
 [`nishan-chatterjee/aspect-based-sentiment-analysis`](https://huggingface.co/nishan-chatterjee/aspect-based-sentiment-analysis).
 
+## License
+
+Copyright (c) 2026 the AspectBench model contributors.
+
+The AspectBench fine-tuned checkpoint contributions / trained heads are licensed under
+[Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).
+The [official legal code](https://creativecommons.org/licenses/by-nc/4.0/legalcode.en) is incorporated by reference.
+Noncommercial research, evaluation, adaptation and redistribution are allowed
+subject to attribution and the license terms. Commercial use is not granted
+under this license; contact the project for separate permission.
+
+Noncommercial describes the purpose of a use, not whether its user is a
+university or a company. Academic affiliation does not automatically make a
+commercial project noncommercial. The legal code controls, including its
+exceptions and limitations. The material is provided as-is without warranties.
+
+This notice does not relicense third-party base weights, tokenizers, code or
+configuration assets, and does not revoke any rights previously granted.
+AspectBench adapted the listed base models for aspect-based sentiment analysis;
+retain their original attribution and license notices when redistributing.
+
+- HBS and Slovenian: [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) — upstream MIT.
+
 ## What this repository contains
 
 Each `.pt` file is a tensor-only state dictionary containing the complete
@@ -186,3 +209,21 @@ python scripts/predict.py \
 
 The `.pt` files contain model tensors only. Optimizer, scheduler, and
 gradient-scaler state is excluded.
+
+## Citation
+
+Please cite the accompanying article when using AspectBench:
+
+```bibtex
+@article{chatterjee2026aspectbench,
+  title   = {Evaluating Fine-Tuned, Embedding-Based, and Zero-Shot Models for
+             Aspect-Based Sentiment Analysis in South Slavic News},
+  author  = {Chatterjee, Nishan and Koloski, Boshko and Doucet, Antoine and
+             Pollak, Senja and Purver, Matthew},
+  journal = {Frontiers in Artificial Intelligence},
+  volume  = {9},
+  year    = {2026},
+  doi     = {10.3389/frai.2026.1844418},
+  url     = {https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1844418/abstract}
+}
+```