POS-tagging model: Marmot - Språkbanken Text (University of Gothenburg, 2024)
Model
Marmot is a part-of-speech tagger that performs for Swedish somewhat worse than state-of-the-art neural models, but still yields very good results, works much faster and does not require a GPU. We provide two models for Marmot.
marmot_eval is trained on SUC3 and Talbanken_SBX_dev, using Saldo as dictionary. 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
0.982
0.988

SIC2
0.921
0.934
0.958

Read more about the evaluation here.
marmot_full is trained on SUC3 + Talbanken_SBX_test + Talbanken_SBX_dev + SIC2 (with Saldo as dictionary). We cannot evaluate the performance of this model, but we expect it to perform better than marmot_eval, or at least not worse.
Tagging and training
Download Marmot and the necessary dependencies. Download SALDO (converted to the necessary format) here. Download our scripts from this repository.
The scripts use a tab-separated three-column format: token, POS (without MSD), MSD. Use conllu_to_tab.rb to convert CONLL(U) to the two-column format (install Ruby 1.9+ and run ruby conllu_to_tab 2 n, where n is the number of the column you want to use (if you are converting our CONLLU files, use 4). Run ruby convert_col2_to_marmot.rb to convert the resulting col2 file to Marmot's col3).

Tagging
Use java -cp marmot.jar marmot.morph.cmd.Annotator --model-file model_name.marmot --test-file form-index=0,test_corpus.col1 --pred-file output_name.conll to tag a corpus using a pretrained model. The output corpus will be in a CONLL format with a somewhat unusual order of columns, use convert_marmot_to_conllu.rb to convert it to a usual CONLLU.
Training your own models
Run Marmot: java -Xmx5G -cp marmot.jar marmot.morph.cmd.Trainer -train-file form-index=0,tag-index=1,morph-index=2,corpus.col3 -tag-morph true -model-file model_name.marmot subtag-separator "." -type-dict saldo_marmot.txt,indexes=[2,3]

Model
Marmot is a part-of-speech tagger that performs for Swedish somewhat worse than state-of-the-art neural models, but still yields very good results, works much faster and does not require a GPU. We provide two models for Marmot.
marmot_eval is trained on SUC3 and Talbanken_SBX_dev, using Saldo as dictionary. 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
0.982
0.988

SIC2
0.921
0.934
0.958

Read more about the evaluation here.
marmot_full is trained on SUC3 + Talbanken_SBX_test + Talbanken_SBX_dev + SIC2 (with Saldo as dictionary). We cannot evaluate the performance of this model, but we expect it to perform better than marmot_eval, or at least not worse.
Tagging and training
Download Marmot and the necessary dependencies. Download SALDO (converted to the necessary format) here. Download our scripts from this repository.
The scripts use a tab-separated three-column format: token, POS (without MSD), MSD. Use conllu_to_tab.rb to convert CONLL(U) to the two-column format (install Ruby 1.9+ and run ruby conllu_to_tab 2 n, where n is the number of the column you want to use (if you are converting our CONLLU files, use 4). Run ruby convert_col2_to_marmot.rb to convert the resulting col2 file to Marmot's col3).

Tagging
Use java -cp marmot.jar marmot.morph.cmd.Annotator --model-file model_name.marmot --test-file form-index=0,test_corpus.col1 --pred-file output_name.conll to tag a corpus using a pretrained model. The output corpus will be in a CONLL format with a somewhat unusual order of columns, use convert_marmot_to_conllu.rb to convert it to a usual CONLLU.
Training your own models
Run Marmot: java -Xmx5G -cp marmot.jar marmot.morph.cmd.Trainer -train-file form-index=0,tag-index=1,morph-index=2,corpus.col3 -tag-morph true -model-file model_name.marmot subtag-separator "." -type-dict saldo_marmot.txt,indexes=[2,3]
Ordklasstaggningsmodell: Marmot
Keywords: Language Technology (Computational Linguistics); model; Natural Language Processing; Språkbehandling och datorlingvistik