Update README.md

README.md

@@ -1,11 +1,11 @@
----
-license: apache-2.0
-language:
-- ar
-tags:
-- Relation Extraction
-- Evidence Extraction
----
+---
+license: cc-by-sa-4.0
+language:
+- ar
+tags:
+- Relation Extraction
+- Evidence Extraction
+---
 # 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.
 
@@ -77,4 +77,6 @@ print('Prediction:', prediction)
     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).",
 }
-```
+```
+### License
+This model is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-sa/4.0/).