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bert-base-uncased-danish

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@@ -49,20 +49,34 @@ Initially, only the word token embeddings are trained using 1.000.000 samples. F
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  The performance of the pretrained model was evaluated using [ScandEval](https://github.com/ScandEval/ScandEval).
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- | task | dataset | summary |
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- |:-------------------------|:-------------|:-------------------------------------------------------------------------------------------|
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- | sentiment-classification | swerec | mcc = 63.02, mcc_se = 2.16, macro_f1 = 62.2, macro_f1_se = 3.61 |
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- | sentiment-classification | angry-tweets | mcc = 47.21, mcc_se = 0.53, macro_f1 = 64.21, macro_f1_se = 0.53 |
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- | sentiment-classification | norec | mcc = 42.23, mcc_se = 8.69, macro_f1 = 57.24, macro_f1_se = 7.67 |
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- | named-entity-recognition | suc3 | micro_f1 = 50.03, micro_f1_se = 4.16, micro_f1_no_misc = 53.55, micro_f1_no_misc_se = 4.57 |
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- | named-entity-recognition | dane | micro_f1 = 76.44, micro_f1_se = 1.36, micro_f1_no_misc = 80.61, micro_f1_no_misc_se = 1.11 |
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- | named-entity-recognition | norne-nb | micro_f1 = 68.38, micro_f1_se = 1.72, micro_f1_no_misc = 73.08, micro_f1_no_misc_se = 1.66 |
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- | named-entity-recognition | norne-nn | micro_f1 = 60.45, micro_f1_se = 1.71, micro_f1_no_misc = 64.39, micro_f1_no_misc_se = 1.8 |
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- | linguistic-acceptability | scala-sv | mcc = 5.01, mcc_se = 5.41, macro_f1 = 49.46, macro_f1_se = 3.67 |
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- | linguistic-acceptability | scala-da | mcc = 54.74, mcc_se = 12.22, macro_f1 = 76.25, macro_f1_se = 6.09 |
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- | linguistic-acceptability | scala-nb | mcc = 19.18, mcc_se = 14.01, macro_f1 = 55.3, macro_f1_se = 8.85 |
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- | linguistic-acceptability | scala-nn | mcc = 5.72, mcc_se = 5.91, macro_f1 = 49.56, macro_f1_se = 3.73 |
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- | question-answering | scandiqa-da | em = 26.36, em_se = 1.17, f1 = 32.41, f1_se = 1.1 |
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- | question-answering | scandiqa-no | em = 26.14, em_se = 1.59, f1 = 32.02, f1_se = 1.59 |
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- | question-answering | scandiqa-sv | em = 26.38, em_se = 1.1, f1 = 32.33, f1_se = 1.05 |
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- | speed | speed | speed = 4.55, speed_se = 0.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  The performance of the pretrained model was evaluated using [ScandEval](https://github.com/ScandEval/ScandEval).
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+ | Task | Dataset | Score (±SE) |
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+ |:-------------------------|:-------------|:---------------------------------|
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+ | sentiment-classification | swerec | mcc = 63.02 (±2.16) |
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+ | | | macro_f1 = 62.2 (±3.61) |
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+ | sentiment-classification | angry-tweets | mcc = 47.21 (±0.53) |
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+ | | | macro_f1 = 64.21 (±0.53) |
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+ | sentiment-classification | norec | mcc = 42.23 (±8.69) |
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+ | | | macro_f1 = 57.24 (±7.67) |
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+ | named-entity-recognition | suc3 | micro_f1 = 50.03 (±4.16) |
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+ | | | micro_f1_no_misc = 53.55 (±4.57) |
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+ | named-entity-recognition | dane | micro_f1 = 76.44 (±1.36) |
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+ | | | micro_f1_no_misc = 80.61 (±1.11) |
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+ | named-entity-recognition | norne-nb | micro_f1 = 68.38 (±1.72) |
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+ | | | micro_f1_no_misc = 73.08 (±1.66) |
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+ | named-entity-recognition | norne-nn | micro_f1 = 60.45 (±1.71) |
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+ | | | micro_f1_no_misc = 64.39 (±1.8) |
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+ | linguistic-acceptability | scala-sv | mcc = 5.01 (±5.41) |
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+ | | | macro_f1 = 49.46 (±3.67) |
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+ | linguistic-acceptability | scala-da | mcc = 54.74 (±12.22) |
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+ | | | macro_f1 = 76.25 (±6.09) |
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+ | linguistic-acceptability | scala-nb | mcc = 19.18 (±14.01) |
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+ | | | macro_f1 = 55.3 (±8.85) |
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+ | linguistic-acceptability | scala-nn | mcc = 5.72 (±5.91) |
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+ | | | macro_f1 = 49.56 (±3.73) |
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+ | question-answering | scandiqa-da | em = 26.36 (±1.17) |
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+ | | | f1 = 32.41 (±1.1) |
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+ | question-answering | scandiqa-no | em = 26.14 (±1.59) |
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+ | | | f1 = 32.02 (±1.59) |
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+ | question-answering | scandiqa-sv | em = 26.38 (±1.1) |
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+ | | | f1 = 32.33 (±1.05) |
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+ | speed | speed | speed = 4.55 (±0.0) |