Update README.md

README.md

@@ -39,7 +39,7 @@ FaBERT is a Persian BERT-base model trained on the diverse HmBlogs corpus, encom
 
 ## Useful Links
 - **Repository:** [FaBERT on Github](https://github.com/SBU-NLP-LAB/FaBERT)
-- **Paper:** [arXiv preprint](https://arxiv.org/abs/2402.06617)
+- **Paper:** [ACL Anthology](https://aclanthology.org/2025.wnut-1.10/)
 
 ## Usage
 
@@ -118,10 +118,28 @@ For a more detailed performance analysis refer to the paper.
 If you use FaBERT in your research or projects, please cite it using the following BibTeX:
 
 ```bibtex
-@article{masumi2024fabert,
-  title={FaBERT: Pre-training BERT on Persian Blogs},
-  author={Masumi, Mostafa and Majd, Seyed Soroush and Shamsfard, Mehrnoush and Beigy, Hamid},
-  journal={arXiv preprint arXiv:2402.06617},
-  year={2024}
+@inproceedings{masumi-etal-2025-fabert,
+    title = "{F}a{BERT}: Pre-training {BERT} on {P}ersian Blogs",
+    author = "Masumi, Mostafa  and
+      Majd, Seyed Soroush  and
+      Shamsfard, Mehrnoush  and
+      Beigy, Hamid",
+    editor = "Bak, JinYeong  and
+      Goot, Rob van der  and
+      Jang, Hyeju  and
+      Buaphet, Weerayut  and
+      Ramponi, Alan  and
+      Xu, Wei  and
+      Ritter, Alan",
+    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
+    month = may,
+    year = "2025",
+    address = "Albuquerque, New Mexico, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.wnut-1.10/",
+    doi = "10.18653/v1/2025.wnut-1.10",
+    pages = "85--96",
+    ISBN = "979-8-89176-232-9",
+    abstract = "We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. FaBERT is designed to excel in traditional Natural Language Understanding (NLU) tasks, addressing the intricacies of diverse sentence structures and linguistic styles prevalent in the Persian language. In our comprehensive evaluation of FaBERT on 12 datasets in various downstream tasks, encompassing Sentiment Analysis (SA), Named Entity Recognition (NER), Natural Language Inference (NLI), Question Answering (QA), and Question Paraphrasing (QP), it consistently demonstrated improved performance, all achieved within a compact model size. The findings highlight the importance of utilizing diverse corpora, such as HmBlogs, to enhance the performance of language models like BERT in Persian Natural Language Processing (NLP) applications."
 }
 ```