readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: bert_sentence_classifier
    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. -->
  
  # bert_sentence_classifier
  
  This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.0040
  - F1: 0.6123
  - Precision: 0.6123
  - Recall: 0.6123
  
  ## 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: 1e-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: 8
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | F1     | Precision | Recall |
  |:-------------:|:-----:|:------:|:---------------:|:------:|:---------:|:------:|
  | 2.0049        | 0.04  | 500    | 1.5854          | 0.5693 | 0.5693    | 0.5693 |
  | 1.552         | 0.07  | 1000   | 1.4428          | 0.6131 | 0.6131    | 0.6131 |
  | 1.502         | 0.11  | 1500   | 1.3977          | 0.6213 | 0.6213    | 0.6213 |
  | 1.4515        | 0.14  | 2000   | 1.3926          | 0.6200 | 0.6200    | 0.6200 |
  | 1.43          | 0.18  | 2500   | 1.3553          | 0.6350 | 0.6350    | 0.6350 |
  | 1.413         | 0.21  | 3000   | 1.3461          | 0.6346 | 0.6346    | 0.6346 |
  | 1.4109        | 0.25  | 3500   | 1.3199          | 0.6496 | 0.6496    | 0.6496 |
  | 1.3853        | 0.28  | 4000   | 1.3338          | 0.6406 | 0.6406    | 0.6406 |
  | 1.3788        | 0.32  | 4500   | 1.3306          | 0.6471 | 0.6471    | 0.6471 |
  | 1.3585        | 0.35  | 5000   | 1.3295          | 0.6410 | 0.6410    | 0.6410 |
  | 1.356         | 0.39  | 5500   | 1.3025          | 0.6441 | 0.6441    | 0.6441 |
  | 1.3534        | 0.42  | 6000   | 1.3197          | 0.6406 | 0.6406    | 0.6406 |
  | 1.3324        | 0.46  | 6500   | 1.2932          | 0.6436 | 0.6436    | 0.6436 |
  | 1.3563        | 0.49  | 7000   | 1.3202          | 0.6488 | 0.6488    | 0.6488 |
  | 1.3121        | 0.53  | 7500   | 1.3024          | 0.6428 | 0.6428    | 0.6428 |
  | 1.3092        | 0.56  | 8000   | 1.3142          | 0.6419 | 0.6419    | 0.6419 |
  | 1.3769        | 0.6   | 8500   | 1.2974          | 0.6441 | 0.6441    | 0.6441 |
  | 1.3487        | 0.63  | 9000   | 1.2882          | 0.6556 | 0.6556    | 0.6556 |
  | 1.3475        | 0.67  | 9500   | 1.2928          | 0.6441 | 0.6441    | 0.6441 |
  | 1.3038        | 0.7   | 10000  | 1.2846          | 0.6488 | 0.6488    | 0.6488 |
  | 1.3371        | 0.74  | 10500  | 1.2894          | 0.6591 | 0.6591    | 0.6591 |
  | 1.3222        | 0.77  | 11000  | 1.2745          | 0.6535 | 0.6535    | 0.6535 |
  | 1.2983        | 0.81  | 11500  | 1.2832          | 0.6526 | 0.6526    | 0.6526 |
  | 1.3505        | 0.84  | 12000  | 1.2812          | 0.6531 | 0.6531    | 0.6531 |
  | 1.2752        | 0.88  | 12500  | 1.2629          | 0.6578 | 0.6578    | 0.6578 |
  | 1.3115        | 0.91  | 13000  | 1.2787          | 0.6453 | 0.6453    | 0.6453 |
  | 1.3353        | 0.95  | 13500  | 1.2707          | 0.6539 | 0.6539    | 0.6539 |
  | 1.2982        | 0.98  | 14000  | 1.2618          | 0.6569 | 0.6569    | 0.6569 |
  | 1.1885        | 1.02  | 14500  | 1.2999          | 0.6544 | 0.6544    | 0.6544 |
  | 1.1339        | 1.05  | 15000  | 1.3086          | 0.6458 | 0.6458    | 0.6458 |
  | 1.0661        | 1.09  | 15500  | 1.2871          | 0.6582 | 0.6582    | 0.6582 |
  | 1.109         | 1.12  | 16000  | 1.2800          | 0.6608 | 0.6608    | 0.6608 |
  | 1.0305        | 1.16  | 16500  | 1.3098          | 0.6604 | 0.6604    | 0.6604 |
  | 1.0855        | 1.19  | 17000  | 1.2968          | 0.6587 | 0.6587    | 0.6587 |
  | 1.0933        | 1.23  | 17500  | 1.3075          | 0.6509 | 0.6509    | 0.6509 |
  | 1.1229        | 1.26  | 18000  | 1.3018          | 0.6496 | 0.6496    | 0.6496 |
  | 1.1043        | 1.3   | 18500  | 1.2832          | 0.6565 | 0.6565    | 0.6565 |
  | 1.1344        | 1.33  | 19000  | 1.2825          | 0.6591 | 0.6591    | 0.6591 |
  | 1.1467        | 1.37  | 19500  | 1.2797          | 0.6642 | 0.6642    | 0.6642 |
  | 1.0596        | 1.4   | 20000  | 1.2841          | 0.6522 | 0.6522    | 0.6522 |
  | 1.1286        | 1.44  | 20500  | 1.2912          | 0.6544 | 0.6544    | 0.6544 |
  | 1.1219        | 1.47  | 21000  | 1.3143          | 0.6509 | 0.6509    | 0.6509 |
  | 1.1339        | 1.51  | 21500  | 1.3021          | 0.6539 | 0.6539    | 0.6539 |
  | 1.1091        | 1.54  | 22000  | 1.2738          | 0.6625 | 0.6625    | 0.6625 |
  | 1.1403        | 1.58  | 22500  | 1.2822          | 0.6548 | 0.6548    | 0.6548 |
  | 1.146    