Update README.md

README.md

@@ -1,3 +1,44 @@
 ---
 license: apache-2.0
 ---
+
+This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/). The model identifies the toponyms' spans in the text and predicts their location types. The location type can be coarse-grained (e.g., country, city, etc.) and fine-grained (e.g., street, POI, etc.)
+
+The model is trained using the training splits of all events from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-less LMR mode and using the random version of the data. You can download this data in BILOU format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/). 
+
+* Different variants of the model are available through HuggingFace:
+  - [rsuwaileh/IDRISI-LMR-EN-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typeless/)
+  - [rsuwaileh/IDRISI-LMR-EN-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typebased/)
+  - [rsuwaileh/IDRISI-LMR-EN-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typeless/)
+
+To cite the models:
+
+```
+@article{suwaileh2022tlLMR4disaster,
+    title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets},
+    author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan},
+    journal={International Journal of Disaster Risk Reduction},
+    year={2022}
+}
+
+@inproceedings{suwaileh2020tlLMR4disaster,
+  title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets},
+  author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan},
+  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
+  pages={6252--6263},
+  year={2020}
+}
+```
+
+To cite the IDRISI-R dataset:
+```
+  @article{rsuwaileh2022Idrisi-r,
+    title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter},
+    author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad},
+    journal={...},
+    volume={...},
+    pages={...},
+    year={2022},
+    publisher={...}
+  }
+```