Update README.md

README.md

@@ -1,12 +1,42 @@
 ---
 datasets:
 - ipipan/polqa
+- ipipan/maupqa
 language:
 - pl
 pipeline_tag: sentence-similarity
 ---
 # HerBERT-base Retrieval (v1)
 
-HerBERT Retrieval model encodes the Polish sentences or paragraphs into a 768-dimensional dense vector space and can be used for tasks like document retrieval or semantic search.
+HerBERT Retrieval model encodes the Polish sentences or paragraphs into a 768-dimensional dense vector space and can be used for tasks like document retrieval or semantic search. See [the paper](https://arxiv.org/abs/2305.05486) for more details.
 
-This model is deprecated. Please consider using the [HerBERT-base Retrieval (v2)](https://huggingface.co/ipipan/herbert-base-retrieval-v2) for much better performance.
+This model is deprecated. Please consider using the [HerBERT-base Retrieval (v2)](https://huggingface.co/ipipan/herbert-base-retrieval-v2) for much better performance.
+
+## Additional Information
+
+### Model Creators
+
+The was created by Piotr Rybak from the [Institute of Computer Science, Polish Academy of Sciences](http://zil.ipipan.waw.pl/).
+
+This work was supported by the European Regional Development Fund as a part of 2014–2020 Smart Growth Operational Programme, CLARIN — Common Language Resources and Technology Infrastructure, project no. POIR.04.02.00-00C002/19.
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+```
+@inproceedings{rybak-2023-maupqa,
+    title = "{MAUPQA}: Massive Automatically-created {P}olish Question Answering Dataset",
+    author = "Rybak, Piotr",
+    booktitle = "Proceedings of the 9th Workshop on Slavic Natural Language Processing 2023 (SlavicNLP 2023)",
+    month = may,
+    year = "2023",
+    address = "Dubrovnik, Croatia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.bsnlp-1.2",
+    pages = "11--16",
+    abstract = "Recently, open-domain question answering systems have begun to rely heavily on annotated datasets to train neural passage retrievers. However, manually annotating such datasets is both difficult and time-consuming, which limits their availability for less popular languages. In this work, we experiment with several methods for automatically collecting weakly labeled datasets and show how they affect the performance of the neural passage retrieval models. As a result of our work, we publish the MAUPQA dataset, consisting of nearly 400,000 question-passage pairs for Polish, as well as the HerBERT-QA neural retriever.",
+}
+```