readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: flash-cards-3
    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. -->
  
  # flash-cards-3
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7948
  
  ## 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: 0.0001
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 2.4513        | 0.02  | 100   | 2.1769          |
  | 2.0154        | 0.05  | 200   | 2.1152          |
  | 1.9263        | 0.07  | 300   | 2.0674          |
  | 1.8961        | 0.1   | 400   | 2.0238          |
  | 1.8337        | 0.12  | 500   | 2.0077          |
  | 1.8257        | 0.15  | 600   | 1.9759          |
  | 1.7823        | 0.17  | 700   | 1.9606          |
  | 1.7577        | 0.2   | 800   | 1.9522          |
  | 1.7334        | 0.22  | 900   | 1.9317          |
  | 1.7246        | 0.25  | 1000  | 1.9378          |
  | 1.7257        | 0.27  | 1100  | 1.9243          |
  | 1.699         | 0.3   | 1200  | 1.9139          |
  | 1.716         | 0.32  | 1300  | 1.9115          |
  | 1.696         | 0.35  | 1400  | 1.9026          |
  | 1.668         | 0.37  | 1500  | 1.8991          |
  | 1.6892        | 0.4   | 1600  | 1.8887          |
  | 1.6557        | 0.42  | 1700  | 1.8877          |
  | 1.6801        | 0.44  | 1800  | 1.8882          |
  | 1.6523        | 0.47  | 1900  | 1.8778          |
  | 1.649         | 0.49  | 2000  | 1.8725          |
  | 1.6599        | 0.52  | 2100  | 1.8718          |
  | 1.6394        | 0.54  | 2200  | 1.8740          |
  | 1.6288        | 0.57  | 2300  | 1.8703          |
  | 1.6403        | 0.59  | 2400  | 1.8645          |
  | 1.6387        | 0.62  | 2500  | 1.8677          |
  | 1.6172        | 0.64  | 2600  | 1.8583          |
  | 1.6347        | 0.67  | 2700  | 1.8672          |
  | 1.627         | 0.69  | 2800  | 1.8506          |
  | 1.6053        | 0.72  | 2900  | 1.8533          |
  | 1.6181        | 0.74  | 3000  | 1.8557          |
  | 1.6146        | 0.77  | 3100  | 1.8492          |
  | 1.5963        | 0.79  | 3200  | 1.8527          |
  | 1.5977        | 0.82  | 3300  | 1.8581          |
  | 1.5787        | 0.84  | 3400  | 1.8490          |
  | 1.6129        | 0.87  | 3500  | 1.8396          |
  | 1.5929        | 0.89  | 3600  | 1.8360          |
  | 1.5866        | 0.91  | 3700  | 1.8421          |
  | 1.5594        | 0.94  | 3800  | 1.8485          |
  | 1.5946        | 0.96  | 3900  | 1.8300          |
  | 1.5622        | 0.99  | 4000  | 1.8351          |
  | 1.5798        | 1.01  | 4100  | 1.8374          |
  | 1.5718        | 1.04  | 4200  | 1.8368          |
  | 1.5517        | 1.06  | 4300  | 1.8287          |
  | 1.5576        | 1.09  | 4400  | 1.8271          |
  | 1.5605        | 1.11  | 4500  | 1.8331          |
  | 1.5467        | 1.14  | 4600  | 1.8236          |
  | 1.5396        | 1.16  | 4700  | 1.8229          |
  | 1.5463        | 1.19  | 4800  | 1.8288          |
  | 1.553         | 1.21  | 4900  | 1.8230          |
  | 1.5571        | 1.24  | 5000  | 1.8231          |
  | 1.5451        | 1.26  | 5100  | 1.8192          |
  | 1.5278        | 1.29  | 5200  | 1.8180          |
  | 1.5285        | 1.31  | 5300  | 1.8220          |
  | 1.5403        | 1.33  | 5400  | 1.8190          |
  | 1.5189        | 1.36  | 5500  | 1.8276          |
  | 1.5495        | 1.38  | 5600  | 1.8230          |
  | 1.5169        | 1.41  | 5700  | 1.8185          |
  | 1.516         | 1.43  | 5800  | 1.8174          |
  | 1.5355        | 1.46  | 5900  | 1.8199          |
  | 1.5321        | 1.48  | 6000  | 1.8167          |
  | 1.5335        | 1.51  | 6100  | 1.8122          |
  | 1.5236        | 1.53  | 6200  | 1.8140          |
  | 1.5232        | 1.56  | 6300  | 1.8106          |
  | 1.5233        | 1.58  | 6400  | 1.8120          |
  | 1.5053        | 1.61  | 6500  | 1.8102          |
  | 1.5056        | 1.63  | 6600  | 1.8162          |
  | 1.5074        | 1.66  | 6700  | 1.8153          |
  | 1.5204        | 1.68  | 6800  | 1.8129          |
  | 1.5115        | 1.71  | 6900  | 1.8119          |
  | 1.4929        | 1.73  | 7000  | 1.8127          |
  | 1.5278        | 1.76  | 7100  | 1.8102          |
  | 1.4959        | 1.78  | 7200  | 1.8087          |
  | 1.5028        | 1.8   | 7300  | 1.8091          |
  | 1.5169        | 1.83  | 7400  | 1.8057          |
  | 1.5181        | 1