Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- ar
+library_name: transformers
 ---
+# AREEj: Arabic Relation Extraction with Evidence
+You can use AREEj to extract relations from Arabic documents. Each document can contain multiple relations, and each relation contains six elements, the source, target, their named entities, relation type between them, and evidence. The evidence is used for two reasons: improving the Relation Extraction task, and explaining the LLM's predictions. You can also use it as an edge between the related entities.
+
+AREEj was introduced in the Proceedings of The Second Arabic Natural Language Processing Conference paper [AREEj: Arabic Relation Extraction with Evidence](https://aclanthology.org/2024.arabicnlp-1.6/).
+
+
+
+```
+@inproceedings{mraikhat-etal-2024-areej,
+    title = "{AREE}j: {A}rabic Relation Extraction with Evidence",
+    author = "Mraikhat, Osama  and
+      Hamoud, Hadi  and
+      Zaraket, Fadi",
+    editor = "Habash, Nizar  and
+      Bouamor, Houda  and
+      Eskander, Ramy  and
+      Tomeh, Nadi  and
+      Abu Farha, Ibrahim  and
+      Abdelali, Ahmed  and
+      Touileb, Samia  and
+      Hamed, Injy  and
+      Onaizan, Yaser  and
+      Alhafni, Bashar  and
+      Antoun, Wissam  and
+      Khalifa, Salam  and
+      Haddad, Hatem  and
+      Zitouni, Imed  and
+      AlKhamissi, Badr  and
+      Almatham, Rawan  and
+      Mrini, Khalil",
+    booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.arabicnlp-1.6",
+    pages = "67--72",
+    abstract = "Relational entity extraction is key in building knowledge graphs. A relational entity has a source, a tail and atype. In this paper, we consider Arabic text and introduce evidence enrichment which intuitivelyinforms models for better predictions. Relational evidence is an expression in the textthat explains how sources and targets relate. {\%}It also provides hints from which models learn. This paper augments the existing relational extraction dataset with evidence annotation to its 2.9-million Arabic relations.We leverage the augmented dataset to build , a relation extraction with evidence model from Arabic documents. The evidence augmentation model we constructed to complete the dataset achieved .82 F1-score (.93 precision, .73 recall). The target outperformed SOTA mREBEL with .72 F1-score (.78 precision, .66 recall).",
+}
+```