readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: albert-base-v2-finetuned-combined-DS
    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-combined-DS
  
  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: 0.8777
  - Accuracy: 0.6103
  - Precision: 0.6156
  - Recall: 0.5964
  - F1: 0.5942
  
  ## 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: 3.2531528713821575e-05
  - train_batch_size: 8
  - eval_batch_size: 16
  - seed: 43
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.0726        | 0.5   | 711  | 1.0355          | 0.5028   | 0.3964    | 0.4551 | 0.3812 |
  | 1.0367        | 1.0   | 1422 | 1.1449          | 0.3357   | 0.4627    | 0.3504 | 0.2166 |
  | 1.0691        | 1.5   | 2133 | 1.0749          | 0.4993   | 0.4595    | 0.4282 | 0.3865 |
  | 0.9844        | 2.0   | 2844 | 0.9458          | 0.5351   | 0.5383    | 0.5383 | 0.5249 |
  | 0.9318        | 2.5   | 3555 | 0.9372          | 0.5569   | 0.5740    | 0.5596 | 0.5508 |
  | 0.9313        | 3.0   | 4266 | 0.9221          | 0.5274   | 0.5772    | 0.5326 | 0.5222 |
  | 0.8692        | 3.5   | 4977 | 0.9099          | 0.5611   | 0.5764    | 0.5585 | 0.5520 |
  | 0.853         | 3.99  | 5688 | 0.8999          | 0.5990   | 0.6089    | 0.5840 | 0.5814 |
  | 0.7954        | 4.49  | 6399 | 0.8821          | 0.6152   | 0.6177    | 0.6017 | 0.5988 |
  | 0.8015        | 4.99  | 7110 | 0.8777          | 0.6103   | 0.6156    | 0.5964 | 0.5942 |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  