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rubert-tiny2-srl

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bert
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@@ -14,48 +14,43 @@ 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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- - eval_loss: 0.4175
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- - eval_Benefactive_precision: 0.0
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- - eval_Benefactive_recall: 0.0
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- - eval_Benefactive_f1: 0.0
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- - eval_Benefactive_number: 5
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- - eval_Causator_precision: 0.0
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- - eval_Causator_recall: 0.0
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- - eval_Causator_f1: 0.0
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- - eval_Causator_number: 26
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- - eval_Cause_precision: 0.0
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- - eval_Cause_recall: 0.0
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- - eval_Cause_f1: 0.0
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- - eval_Cause_number: 21
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- - eval_ContrSubject_precision: 0.0
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- - eval_ContrSubject_recall: 0.0
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- - eval_ContrSubject_f1: 0.0
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- - eval_ContrSubject_number: 19
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- - eval_Deliberative_precision: 0.0
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- - eval_Deliberative_recall: 0.0
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- - eval_Deliberative_f1: 0.0
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- - eval_Deliberative_number: 10
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- - eval_Experiencer_precision: 0.5512
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- - eval_Experiencer_recall: 0.4321
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- - eval_Experiencer_f1: 0.4844
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- - eval_Experiencer_number: 162
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- - eval_Object_precision: 0.6905
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- - eval_Object_recall: 0.0963
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- - eval_Object_f1: 0.1691
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- - eval_Object_number: 301
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- - eval_Predicate_precision: 0.9360
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- - eval_Predicate_recall: 0.9737
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- - eval_Predicate_f1: 0.9545
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- - eval_Predicate_number: 571
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- - eval_overall_precision: 0.8585
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- - eval_overall_recall: 0.5874
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- - eval_overall_f1: 0.6976
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- - eval_overall_accuracy: 0.8855
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- - eval_runtime: 1.6021
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- - eval_samples_per_second: 355.786
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- - eval_steps_per_second: 355.786
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- - epoch: 1.0
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- - step: 4864
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1.1643470912014148e-05
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  - train_batch_size: 1
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  - eval_batch_size: 1
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- - seed: 163748
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_ratio: 0.28
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  - num_epochs: 5
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  - mixed_precision_training: Native AMP
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  ### Framework versions
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  - Transformers 4.28.1
 
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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: 0.3357
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+ - Benefactive Precision: 1.0
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+ - Benefactive Recall: 0.5
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+ - Benefactive F1: 0.6667
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+ - Benefactive Number: 2
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+ - Causator Precision: 1.0
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+ - Causator Recall: 1.0
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+ - Causator F1: 1.0
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+ - Causator Number: 12
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+ - Cause Precision: 0.4
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+ - Cause Recall: 0.4
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+ - Cause F1: 0.4000
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+ - Cause Number: 5
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+ - Contrsubject Precision: 0.75
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+ - Contrsubject Recall: 0.6667
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+ - Contrsubject F1: 0.7059
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+ - Contrsubject Number: 9
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+ - Deliberative Precision: 1.0
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+ - Deliberative Recall: 1.0
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+ - Deliberative F1: 1.0
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+ - Deliberative Number: 4
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+ - Experiencer Precision: 0.7442
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+ - Experiencer Recall: 0.8101
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+ - Experiencer F1: 0.7758
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+ - Experiencer Number: 79
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+ - Object Precision: 0.7551
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+ - Object Recall: 0.7450
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+ - Object F1: 0.7500
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+ - Object Number: 149
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+ - Predicate Precision: 0.9809
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+ - Predicate Recall: 0.9885
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+ - Predicate F1: 0.9847
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+ - Predicate Number: 260
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+ - Overall Precision: 0.8705
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+ - Overall Recall: 0.8788
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+ - Overall F1: 0.8746
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+ - Overall Accuracy: 0.9411
 
 
 
 
 
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.00010372880304918982
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  - train_batch_size: 1
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  - eval_batch_size: 1
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+ - seed: 923789
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.29
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  - num_epochs: 5
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  - mixed_precision_training: Native AMP
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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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+ | 0.3041 | 1.0 | 4864 | 0.3394 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 0.4167 | 0.5882 | 12 | 0.0 | 0.0 | 0.0 | 5 | 1.0 | 0.1111 | 0.2000 | 9 | 0.0 | 0.0 | 0.0 | 4 | 0.8372 | 0.4557 | 0.5902 | 79 | 0.8730 | 0.3691 | 0.5189 | 149 | 0.9884 | 0.9846 | 0.9865 | 260 | 0.9515 | 0.6788 | 0.7924 | 0.9141 |
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+ | 0.3178 | 2.0 | 9728 | 0.2692 | 0.0 | 0.0 | 0.0 | 2 | 1.0 | 1.0 | 1.0 | 12 | 1.0 | 0.2 | 0.3333 | 5 | 1.0 | 0.2222 | 0.3636 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7403 | 0.7215 | 0.7308 | 79 | 0.8523 | 0.5034 | 0.6329 | 149 | 0.9808 | 0.9846 | 0.9827 | 260 | 0.9142 | 0.7788 | 0.8411 | 0.9321 |
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+ | 0.124 | 3.0 | 14592 | 0.2990 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.0 | 0.0 | 0.0 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 0.5 | 0.6667 | 4 | 0.7386 | 0.8228 | 0.7784 | 79 | 0.8 | 0.6980 | 0.7455 | 149 | 0.9885 | 0.9885 | 0.9885 | 260 | 0.8904 | 0.8596 | 0.8748 | 0.9435 |
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+ | 0.104 | 4.0 | 19456 | 0.2852 | 1.0 | 0.5 | 0.6667 | 2 | 0.9231 | 1.0 | 0.9600 | 12 | 0.4286 | 0.6 | 0.5 | 5 | 0.6 | 0.6667 | 0.6316 | 9 | 1.0 | 0.75 | 0.8571 | 4 | 0.7253 | 0.8354 | 0.7765 | 79 | 0.7044 | 0.7517 | 0.7273 | 149 | 0.9847 | 0.9885 | 0.9866 | 260 | 0.8440 | 0.8846 | 0.8638 | 0.9359 |
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+ | 0.0918 | 5.0 | 24320 | 0.3357 | 1.0 | 0.5 | 0.6667 | 2 | 1.0 | 1.0 | 1.0 | 12 | 0.4 | 0.4 | 0.4000 | 5 | 0.75 | 0.6667 | 0.7059 | 9 | 1.0 | 1.0 | 1.0 | 4 | 0.7442 | 0.8101 | 0.7758 | 79 | 0.7551 | 0.7450 | 0.7500 | 149 | 0.9809 | 0.9885 | 0.9847 | 260 | 0.8705 | 0.8788 | 0.8746 | 0.9411 |
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  ### Framework versions
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  - Transformers 4.28.1