readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: rubert-base-srl-seqlabeling
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # rubert-base-srl-seqlabeling
  
  This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1723
  - Causator Precision: 0.8539
  - Causator Recall: 0.8352
  - Causator F1: 0.8444
  - Causator Number: 91
  - Expiriencer Precision: 0.9259
  - Expiriencer Recall: 0.9740
  - Expiriencer F1: 0.9494
  - Expiriencer Number: 77
  - Instrument Precision: 0.375
  - Instrument Recall: 1.0
  - Instrument F1: 0.5455
  - Instrument Number: 3
  - Other Precision: 0.0
  - Other Recall: 0.0
  - Other F1: 0.0
  - Other Number: 1
  - Predicate Precision: 0.9352
  - Predicate Recall: 0.9902
  - Predicate F1: 0.9619
  - Predicate Number: 102
  - Overall Precision: 0.8916
  - Overall Recall: 0.9307
  - Overall F1: 0.9107
  - Overall Accuracy: 0.9667
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Causator Precision | Causator Recall | Causator F1 | Causator Number | Expiriencer Precision | Expiriencer Recall | Expiriencer F1 | Expiriencer Number | Instrument Precision | Instrument Recall | Instrument F1 | Instrument Number | Other Precision | Other Recall | Other F1 | Other Number | Predicate Precision | Predicate Recall | Predicate F1 | Predicate Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:---------------:|:------------:|:--------:|:------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
  | 0.2552        | 1.0   | 56   | 0.3471          | 0.8841             | 0.6703          | 0.7625      | 91              | 0.8421                | 0.8312             | 0.8366         | 77                 | 0.0                  | 0.0               | 0.0           | 3                 | 0.0             | 0.0          | 0.0      | 1            | 0.9259              | 0.9804           | 0.9524       | 102              | 0.8893            | 0.8212         | 0.8539     | 0.9203           |
  | 0.2385        | 2.0   | 112  | 0.1608          | 0.9103             | 0.7802          | 0.8402      | 91              | 0.9375                | 0.9740             | 0.9554         | 77                 | 0.2857               | 0.6667            | 0.4           | 3                 | 0.0             | 0.0          | 0.0      | 1            | 0.9519              | 0.9706           | 0.9612       | 102              | 0.9182            | 0.9015         | 0.9098     | 0.9554           |
  | 0.0367        | 3.0   | 168  | 0.1311          | 0.8902             | 0.8022          | 0.8439      | 91              | 0.9375                | 0.9740             | 0.9554         | 77                 | 0.4286               | 1.0               | 0.6           | 3                 | 0.0             | 0.0          | 0.0      | 1            | 0.9709              | 0.9804           | 0.9756       | 102              | 0.9228            | 0.9161         | 0.9194     | 0.9673           |
  | 0.0494        | 4.0   | 224  | 0.1507          | 0.7812             | 0.8242          | 0.8021      | 91              | 0.9241                | 0.9481             | 0.9359         | 77                 | 0.4286               | 1.0               | 0.6           | 3                 | 0.0             | 0.0          | 0.0      | 1            | 0.9524              | 0.9804           | 0.9662       | 102              | 0.8746            | 0.9161         | 0.8948     | 0.9637           |
  | 0.0699        | 5.0   | 280  | 0.1830          | 0.8276             | 0.7912          | 0.8090      | 91              | 0.8941                | 0.9870             | 0.9383         | 77                 | 0.375                | 1.0               | 0.5455        | 3                 | 0.0             | 0.0          | 0.0      | 1            | 0.9352              | 0.9902           | 0.9619       | 102              | 0.875             | 0.9197         | 0.896