This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
The model is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-based 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/hurricane_dorian_2019).

* Different variants of the model are available through HuggingFace:
  - [rsuwaileh/IDRISI-LMR-HD-TB-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB-partition/)
  - [rsuwaileh/IDRISI-LMR-HD-TL](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL)
  - [rsuwaileh/IDRISI-LMR-HD-TL-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL-partition/)

* Larger models are available at [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).

* Models trained on the entire IDRISI-R dataset:
  - [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/)
  - [rsuwaileh/IDRISI-LMR-EN-timebased-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typebased/)
  

To cite this model:

```
@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={...}
  }
```

{}
transformers pytorch bert token-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

This model is a BERT-based Location Mention Recognition model that is adopted from the TLLMR4CM GitHub.
The model is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from IDRISI-R dataset under the Type-based LMR mode and using the random version of the data.
You can download this data in BILOU format from here.

* Different variants of the model are available through HuggingFace:
  - rsuwaileh/IDRISI-LMR-HD-TB-partition
  - rsuwaileh/IDRISI-LMR-HD-TL
  - rsuwaileh/IDRISI-LMR-HD-TL-partition

* Larger models are available at TLLMR4CM GitHub.

* Models trained on the entire IDRISI-R dataset:
  - rsuwaileh/IDRISI-LMR-EN-random-typeless
  - rsuwaileh/IDRISI-LMR-EN-random-typebased
  - rsuwaileh/IDRISI-LMR-EN-timebased-typeless
  - rsuwaileh/IDRISI-LMR-EN-timebased-typebased
  

To cite this model:



To cite the IDRISI-R dataset:


TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us 
