readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: albert-base-v2-finetuned-filtered-0609
    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. -->
  
  # albert-base-v2-finetuned-filtered-0609
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2062
  - Accuracy: 0.9723
  - Precision: 0.9724
  - Recall: 0.9723
  - F1: 0.9723
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.2688        | 1.0   | 3180  | 0.2282          | 0.9560   | 0.9577    | 0.9560 | 0.9562 |
  | 0.2268        | 2.0   | 6360  | 0.1909          | 0.9638   | 0.9640    | 0.9638 | 0.9638 |
  | 0.1831        | 3.0   | 9540  | 0.2590          | 0.9572   | 0.9584    | 0.9572 | 0.9572 |
  | 0.1588        | 4.0   | 12720 | 0.1752          | 0.9673   | 0.9678    | 0.9673 | 0.9673 |
  | 0.0972        | 5.0   | 15900 | 0.1868          | 0.9695   | 0.9696    | 0.9695 | 0.9695 |
  | 0.0854        | 6.0   | 19080 | 0.2042          | 0.9701   | 0.9707    | 0.9701 | 0.9702 |
  | 0.0599        | 7.0   | 22260 | 0.1793          | 0.9748   | 0.9749    | 0.9748 | 0.9749 |
  | 0.0389        | 8.0   | 25440 | 0.1996          | 0.9742   | 0.9743    | 0.9742 | 0.9742 |
  | 0.0202        | 9.0   | 28620 | 0.2188          | 0.9723   | 0.9726    | 0.9723 | 0.9724 |
  | 0.0152        | 10.0  | 31800 | 0.2062          | 0.9723   | 0.9724    | 0.9723 | 0.9723 |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.9.1+cu111
  - Datasets 1.16.1
  - Tokenizers 0.12.1
  