Update README.md

README.md

@@ -1,3 +1,71 @@
----
-license: apache-2.0
----
+---
+language:
+- multilingual
+- pl
+- ru
+- uk
+- bg
+- cs
+- sl
+datasets:
+- SlavicNER
+license: apache-2.0
+library_name: transformers
+pipeline_tag: token-classification
+tags:
+- ner
+- named entity recognition
+---
+
+# Model description
+
+This is a baseline model for named entity **recognition** trained on the cross-topic split of the 
+[SlavicNER corpus](https://github.com/SlavicNLP/SlavicNER).
+
+
+# Resources and Technical Documentation
+
+- Paper: [Cross-lingual Named Entity Corpus for Slavic Languages](https://arxiv.org/pdf/2404.00482), to appear in LREC-COLING 2024.
+- Annotation guidelines: https://arxiv.org/pdf/2404.00482
+- SlavicNER Corpus: https://github.com/SlavicNLP/SlavicNER
+
+
+# Evaluation
+
+*Will appear soon*
+
+
+# Usage
+
+*Will appear soon*
+
+# Citation
+
+```latex
+@inproceedings{piskorski-etal-2024-cross-lingual,
+    title = "Cross-lingual Named Entity Corpus for {S}lavic Languages",
+    author = "Piskorski, Jakub  and
+      Marci{\'n}czuk, Micha{\l}  and
+      Yangarber, Roman",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italy",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.369",
+    pages = "4143--4157",
+    abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian,
+                and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural
+                Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities.
+                Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits
+                {---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture
+                with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization,
+                and mT5-large for named entity lemmatization and linking.",
+}
+```