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rubert-tiny2-srl

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bert
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@@ -14,43 +14,47 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2182
 
 
 
 
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  - Benefactive Precision: 0.0
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  - Benefactive Recall: 0.0
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  - Benefactive F1: 0.0
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- - Benefactive Number: 2
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- - Causator Precision: 0.7333
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- - Causator Recall: 0.9167
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- - Causator F1: 0.8148
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- - Causator Number: 12
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- - Cause Precision: 0.5
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- - Cause Recall: 0.2
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- - Cause F1: 0.2857
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- - Cause Number: 5
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- - Contrsubject Precision: 1.0
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- - Contrsubject Recall: 0.2222
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- - Contrsubject F1: 0.3636
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- - Contrsubject Number: 9
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  - Deliberative Precision: 1.0
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- - Deliberative Recall: 0.25
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- - Deliberative F1: 0.4
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- - Deliberative Number: 4
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- - Experiencer Precision: 0.7108
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- - Experiencer Recall: 0.7468
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- - Experiencer F1: 0.7284
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- - Experiencer Number: 79
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- - Object Precision: 0.6966
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- - Object Recall: 0.6779
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- - Object F1: 0.6871
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- - Object Number: 149
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- - Predicate Precision: 0.9847
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- - Predicate Recall: 0.9923
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- - Predicate F1: 0.9885
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- - Predicate Number: 260
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- - Overall Precision: 0.8490
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- - Overall Recall: 0.8327
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- - Overall F1: 0.8408
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- - Overall Accuracy: 0.9274
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  ## Model description
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@@ -83,10 +87,10 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 0.2886 | 1.0 | 152 | 0.2507 | 0.0 | 0.0 | 0.0 | 2 | 0.6667 | 0.8333 | 0.7407 | 12 | 1.0 | 0.2 | 0.3333 | 5 | 1.0 | 0.1111 | 0.2000 | 9 | 0.0 | 0.0 | 0.0 | 4 | 0.6596 | 0.7848 | 0.7168 | 79 | 0.7232 | 0.5436 | 0.6207 | 149 | 0.9847 | 0.9885 | 0.9866 | 260 | 0.8512 | 0.7923 | 0.8207 | 0.9203 |
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- | 0.1911 | 2.0 | 304 | 0.2182 | 0.0 | 0.0 | 0.0 | 2 | 0.7333 | 0.9167 | 0.8148 | 12 | 0.5 | 0.2 | 0.2857 | 5 | 1.0 | 0.2222 | 0.3636 | 9 | 1.0 | 0.25 | 0.4 | 4 | 0.7108 | 0.7468 | 0.7284 | 79 | 0.6966 | 0.6779 | 0.6871 | 149 | 0.9847 | 0.9923 | 0.9885 | 260 | 0.8490 | 0.8327 | 0.8408 | 0.9274 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2041
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+ - Addressee Precision: 0.7273
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+ - Addressee Recall: 0.8
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+ - Addressee F1: 0.7619
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+ - Addressee Number: 10
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  - Benefactive Precision: 0.0
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  - Benefactive Recall: 0.0
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  - Benefactive F1: 0.0
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+ - Benefactive Number: 1
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+ - Causator Precision: 0.8824
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+ - Causator Recall: 0.8333
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+ - Causator F1: 0.8571
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+ - Causator Number: 18
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+ - Cause Precision: 0.6667
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+ - Cause Recall: 0.1538
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+ - Cause F1: 0.25
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+ - Cause Number: 13
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+ - Contrsubject Precision: 0.6667
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+ - Contrsubject Recall: 0.3333
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+ - Contrsubject F1: 0.4444
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+ - Contrsubject Number: 6
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  - Deliberative Precision: 1.0
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+ - Deliberative Recall: 0.4
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+ - Deliberative F1: 0.5714
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+ - Deliberative Number: 5
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+ - Experiencer Precision: 0.7660
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+ - Experiencer Recall: 0.8
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+ - Experiencer F1: 0.7826
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+ - Experiencer Number: 90
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+ - Object Precision: 0.7576
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+ - Object Recall: 0.6868
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+ - Object F1: 0.7205
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+ - Object Number: 182
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+ - Predicate Precision: 0.9713
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+ - Predicate Recall: 0.9967
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+ - Predicate F1: 0.9839
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+ - Predicate Number: 306
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+ - Overall Precision: 0.8719
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+ - Overall Recall: 0.8415
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+ - Overall F1: 0.8565
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+ - Overall Accuracy: 0.9429
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Addressee Precision | Addressee Recall | Addressee F1 | Addressee Number | Benefactive Precision | Benefactive Recall | Benefactive F1 | Benefactive Number | Causator Precision | Causator Recall | Causator F1 | Causator Number | Cause Precision | Cause Recall | Cause F1 | Cause Number | Contrsubject Precision | Contrsubject Recall | Contrsubject F1 | Contrsubject Number | Deliberative Precision | Deliberative Recall | Deliberative F1 | Deliberative Number | Experiencer Precision | Experiencer Recall | Experiencer F1 | Experiencer Number | Object Precision | Object Recall | Object F1 | Object Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------:|:------------:|:--------:|:------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 0.2845 | 1.0 | 181 | 0.2356 | 0.8 | 0.8 | 0.8000 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.7895 | 0.8333 | 0.8108 | 18 | 0.0 | 0.0 | 0.0 | 13 | 0.0 | 0.0 | 0.0 | 6 | 0.0 | 0.0 | 0.0 | 5 | 0.7320 | 0.7889 | 0.7594 | 90 | 0.7740 | 0.6209 | 0.6890 | 182 | 0.9744 | 0.9935 | 0.9838 | 306 | 0.875 | 0.8098 | 0.8412 | 0.9376 |
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+ | 0.1875 | 1.99 | 362 | 0.2041 | 0.7273 | 0.8 | 0.7619 | 10 | 0.0 | 0.0 | 0.0 | 1 | 0.8824 | 0.8333 | 0.8571 | 18 | 0.6667 | 0.1538 | 0.25 | 13 | 0.6667 | 0.3333 | 0.4444 | 6 | 1.0 | 0.4 | 0.5714 | 5 | 0.7660 | 0.8 | 0.7826 | 90 | 0.7576 | 0.6868 | 0.7205 | 182 | 0.9713 | 0.9967 | 0.9839 | 306 | 0.8719 | 0.8415 | 0.8565 | 0.9429 |
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  ### Framework versions