Ordklasstaggningsmodell: Stanza POS-tagging model: Stanza doi-10-23695-ygw3-gf17-0 https://doi.org/10.23695/YGW3-GF17 Swedish National Data Service Svensk nationell datatjänst Landing page Ordklasstaggningsmodell: Stanza POS-tagging model: Stanza doi-10-23695-ygw3-gf17-0 https://doi.org/10.23695/YGW3-GF17 Språkbanken Text Swedish National Data Service Svensk nationell datatjänst Landing page Models Stanza is currently the default annotation tool used by Sparv. We provide two Stanza POS-tagging models. stanza_eval is trained on SUC3 with Talbanken_SBX_dev as dev set. The advantage of this model is that it can be evaluated, using Talbanken_SBX_test or SIC2. The evaluation results are reported in the table below. Test set Exact match POS MSD Talbanken_SBX_test 0.973 0.983 0.988 SIC2 0.918 0.932 0.957 Read more about the evaluation here. stanza_full is trained on SUC3 + Talbanken_SBX_test + SIC2 with Talbanken_SBX_dev as dev set. We cannot evaluate the performance of this model, but we expect it to perform better than stanza_eval, or at least not worse. This is the model used by Sparv. We updated the "pretrain" file in spring 2025. This was a minor format change. Using the models on your own Unzip the model you want to use and the "pretrain" file (which contains word2vec embeddings encoded in a format required by Stanza). Follow the instructions provided by Stanza Models Stanza is currently the default annotation tool used by Sparv. We provide two Stanza POS-tagging models. stanza_eval is trained on SUC3 with Talbanken_SBX_dev as dev set. The advantage of this model is that it can be evaluated, using Talbanken_SBX_test or SIC2. The evaluation results are reported in the table below. Test set Exact match POS MSD Talbanken_SBX_test 0.973 0.983 0.988 SIC2 0.918 0.932 0.957 Read more about the evaluation here. stanza_full is trained on SUC3 + Talbanken_SBX_test + SIC2 with Talbanken_SBX_dev as dev set. We cannot evaluate the performance of this model, but we expect it to perform better than stanza_eval, or at least not worse. This is the model used by Sparv. We updated the "pretrain" file in spring 2025. This was a minor format change. Using the models on your own Unzip the model you want to use and the "pretrain" file (which contains word2vec embeddings encoded in a format required by Stanza). Follow the instructions provided by Stanza Access to data through an external actor. Åtkomst till data via extern aktör.