readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - collection3
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: rubert-finetuned-collection3
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: collection3
        type: collection3
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.9354685646500593
      - name: Recall
        type: recall
        value: 0.9577362156910372
      - name: F1
        type: f1
        value: 0.9464714354296688
      - name: Accuracy
        type: accuracy
        value: 0.986481047855993
  ---
  
  <!-- 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-finetuned-collection3
  
  This model is a fine-tuned version of [sberbank-ai/ruBert-base](https://huggingface.co/sberbank-ai/ruBert-base) on the collection3 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0514
  - Precision: 0.9355
  - Recall: 0.9577
  - F1: 0.9465
  - Accuracy: 0.9865
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0794        | 1.0   | 1163 | 0.0536          | 0.9178    | 0.9466 | 0.9320 | 0.9825   |
  | 0.0391        | 2.0   | 2326 | 0.0512          | 0.9228    | 0.9553 | 0.9388 | 0.9853   |
  | 0.0191        | 3.0   | 3489 | 0.0514          | 0.9355    | 0.9577 | 0.9465 | 0.9865   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0.dev20220929+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  