| --- |
| license: cc-by-4.0 |
| widget: |
| - text: This house was let out in tiny tenements and was inhabited by working people of all kinds--tailors, locksmiths, cooks, Germans ofsorts, girls picking up a living as best they could, petty clerks, etc. |
| example_title: "Crime and Punishment" |
| - text: Quixote having got on his back and the duke mounted a fine horse, they placed the duchess in the middle and set out for the castle. |
| example_title: "Don Quixote" |
| - text: The noble carriage of this gentleman, for whom he believed himself to be engaged, had won Planchet—that was the name of the Picard. |
| example_title: "The Three Musketeers" |
| --- |
| ### Description |
| A `roberta-base` model which has been fine tuned for token classification on the [LitBank](https://github.com/dbamman/litbank) dataset. |
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| ### Intended Use |
| This model is ready to be used for entity recognition. It is capable of tagging the 6 entity types from [ACE 2005](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) |
| - Person (PER) |
| - ORG |
| - GPE |
| - LOC |
| - VEH |
| - FAC |
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| Due to the fine-tuning domain, it is expected to work best with literary sentences. |
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