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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.)
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The model is trained using the training splits of all events from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the `Type-based` LMR mode and using the `Time-based` version of the data. You can download this data in BILOU format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/AR/gold-timebased-bilou/). More details about the models are available [here](https://github.com/rsuwaileh/IDRISI/tree/main/models).
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* Different variants of the model are available through HuggingFace:
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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.)
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The model is trained using the training splits of all events from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the `Type-based` LMR mode and using the `Time-based` version of the data. You can download this data in `BILOU` format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/AR/gold-timebased-bilou/). More details about the models are available [here](https://github.com/rsuwaileh/IDRISI/tree/main/models).
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* Different variants of the model are available through HuggingFace:
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