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        <identifier>oai:researchdata.se:doi-10-23695-aryw-nh78/0</identifier>
        <datestamp>2020-06-29</datestamp>
        <setSpec>subject:ssif:10208</setSpec>
        <setSpec>subject:ssif:102</setSpec>
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        <setSpec>principal:slug:university-of-gothenburg</setSpec>
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        <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
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          <dc:type>http://purl.org/dc/dcmitype/Dataset</dc:type>
          <dc:identifier>https://doi.org/10.23695/ARYW-NH78</dc:identifier>
          <dc:title xml:lang="en">POS-tagging model: Marmot</dc:title>
          <dc:title xml:lang="sv">Ordklasstaggningsmodell: Marmot</dc:title>
          <dc:creator>https://ror.org/05qhvy459</dc:creator>
          <dc:subject xml:lang="en">Natural Language Processing</dc:subject>
          <dc:subject xml:lang="sv">Språkbehandling och datorlingvistik</dc:subject>
          <dc:description xml:lang="en">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]</dc:description>
          <dc:description xml:lang="sv">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]</dc:description>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
          <dc:publisher xml:lang="en">University of Gothenburg</dc:publisher>
          <dc:publisher xml:lang="sv">Göteborgs universitet</dc:publisher>
          <dc:date>2024-01-01T00:00:00Z</dc:date>
          <dc:language>swe</dc:language>
        </oai_dc:dc>
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