**      | **%**   | |----|---------|--------------------------|------------|---------| | 1  | **PER** | _Person_                 | 1,747      | 3.26    | | 2  | **RR**  | _Judge_                  | 1,519      | 2.83    | | 3  | **AN**  | _Lawyer_                 | 111        | 0.21    | | 4  | **LD**  | _Country_                | 1,429      | 2.66    | | 5  | **ST**  | _City_                   | 705        | 1.31    | | 6  | **STR** | _Street_                 | 136        | 0.25    | | 7  | **LDS** | _Landscape_              | 198        | 0.37    | | 8  | **ORG** | _Organization_           | 1,166      | 2.17    | | 9  | **UN**  | _Company_                | 1,058      | 1.97    | | 10 | **INN** | _Institution_            | 2,196      | 4.09    | | 11 | **GRT** | _Court_                  | 3,212      | 5.99    | | 12 | **MRK** | _Brand_                  | 283        | 0.53    | | 13 | **GS**  | _Law_                    | 18,52      | 34.53   | | 14 | **VO**  | _Ordinance_              | 797        | 1.49    | | 15 | **EUN** | _European legal norm_    | 1,499      | 2.79    | | 16 | **VS**  | _Regulation_             | 607        | 1.13    | | 17 | **VT**  | _Contract_               | 2,863      | 5.34    | | 18 | **RS**  | _Court decision_         | 12,58      | 23.46   | | 19 | **LIT** | _Legal literature_       | 3,006      | 5.60    | |    |         | **Total**                | **53,632** | **100** |  How to fine-tune another model on the German LER Dataset, see [GitHub](https://github.com/elenanereiss/bert-legal-ner).  