readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-base-uncased-pretrain-finetuned-coqa-falt
    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-base-uncased-pretrain-finetuned-coqa-falt
  
  This model is a fine-tuned version of [alistvt/bert-base-uncased-pretrained-mlm-coqa-stories](https://huggingface.co/alistvt/bert-base-uncased-pretrained-mlm-coqa-stories) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.8125
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.4039        | 0.29  | 2000  | 3.0921          |
  | 3.1438        | 0.59  | 4000  | 2.8826          |
  | 3.0252        | 0.88  | 6000  | 2.7885          |
  | 2.7112        | 1.18  | 8000  | 2.7720          |
  | 2.6703        | 1.47  | 10000 | 2.7581          |
  | 2.6432        | 1.77  | 12000 | 2.7316          |
  | 2.385         | 2.06  | 14000 | 2.7798          |
  | 2.3314        | 2.36  | 16000 | 2.7836          |
  | 2.3433        | 2.65  | 18000 | 2.7650          |
  | 2.3604        | 2.95  | 20000 | 2.7585          |
  | 2.2232        | 3.24  | 22000 | 2.8120          |
  | 2.2094        | 3.53  | 24000 | 2.7945          |
  | 2.2306        | 3.83  | 26000 | 2.8125          |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  