readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - cuad
  model-index:
  - name: tiny-bert-finetuned-cuad
    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. -->
  
  # tiny-bert-finetuned-cuad
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the portion of cuad dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4606
  
  # Note 
  The model was not trained on the whole dataset but, the first 10% of `train` + the first 10% of `test`.
  ```bash
  raw_datasets_train, raw_datasets_test = load_dataset("cuad", split=['train[:10%]', 'test[:10%]']) 
  datasets = DatasetDict({'train': raw_datasets_train, 'validation': raw_datasets_test})
  ```
  
  ## 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: 1024
  - eval_batch_size: 1024
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 1.0   | 136  | 2.9644          |
  | No log        | 2.0   | 272  | 1.9337          |
  | No log        | 3.0   | 408  | 1.4375          |
  | 2.7124        | 4.0   | 544  | 1.0978          |
  | 2.7124        | 5.0   | 680  | 0.8571          |
  | 2.7124        | 6.0   | 816  | 0.6907          |
  | 2.7124        | 7.0   | 952  | 0.5799          |
  | 0.9512        | 8.0   | 1088 | 0.5105          |
  | 0.9512        | 9.0   | 1224 | 0.4726          |
  | 0.9512        | 10.0  | 1360 | 0.4606          |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  