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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 without predicting their location types.
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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-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/AR/gold-random-bilou/). All Location types in the data were normalized to the "LOC" tag. More details are available [here](https://github.com/rsuwaileh/IDRISI/tree/main/models). 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 without predicting their location types.
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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-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/AR/gold-random-bilou/). All Location types in the data were normalized to the `LOC` tag. More details are available [here](https://github.com/rsuwaileh/IDRISI/tree/main/models). 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: