readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: unifiedqa-v2-t5-base-1363200-finetuned-causalqa-squad
    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. -->
  
  # unifiedqa-v2-t5-base-1363200-finetuned-causalqa-squad
  
  This model is a fine-tuned version of [allenai/unifiedqa-v2-t5-base-1363200](https://huggingface.co/allenai/unifiedqa-v2-t5-base-1363200) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2574
  
  ## 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: 5e-05
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|
  | 0.7378        | 0.05  | 73   | 1.1837          |
  | 0.6984        | 0.1   | 146  | 0.8918          |
  | 0.4511        | 0.15  | 219  | 0.8342          |
  | 0.4696        | 0.2   | 292  | 0.7642          |
  | 0.295         | 0.25  | 365  | 0.7996          |
  | 0.266         | 0.3   | 438  | 0.7773          |
  | 0.2372        | 0.35  | 511  | 0.8592          |
  | 0.2881        | 0.39  | 584  | 0.8440          |
  | 0.2578        | 0.44  | 657  | 0.8306          |
  | 0.2733        | 0.49  | 730  | 0.8228          |
  | 0.2073        | 0.54  | 803  | 0.8419          |
  | 0.2683        | 0.59  | 876  | 0.8241          |
  | 0.2693        | 0.64  | 949  | 0.8573          |
  | 0.355         | 0.69  | 1022 | 0.8204          |
  | 0.2246        | 0.74  | 1095 | 0.8530          |
  | 0.2468        | 0.79  | 1168 | 0.8410          |
  | 0.3102        | 0.84  | 1241 | 0.8035          |
  | 0.2115        | 0.89  | 1314 | 0.8262          |
  | 0.1855        | 0.94  | 1387 | 0.8560          |
  | 0.1772        | 0.99  | 1460 | 0.8747          |
  | 0.1509        | 1.04  | 1533 | 0.9132          |
  | 0.1871        | 1.09  | 1606 | 0.8920          |
  | 0.1624        | 1.14  | 1679 | 0.9085          |
  | 0.1404        | 1.18  | 1752 | 0.9460          |
  | 0.1639        | 1.23  | 1825 | 0.9812          |
  | 0.0983        | 1.28  | 1898 | 0.9790          |
  | 0.1395        | 1.33  | 1971 | 0.9843          |
  | 0.1439        | 1.38  | 2044 | 0.9877          |
  | 0.1397        | 1.43  | 2117 | 1.0338          |
  | 0.1095        | 1.48  | 2190 | 1.0589          |
  | 0.1228        | 1.53  | 2263 | 1.0498          |
  | 0.1246        | 1.58  | 2336 | 1.0923          |
  | 0.1438        | 1.63  | 2409 | 1.0995          |
  | 0.1305        | 1.68  | 2482 | 1.0867          |
  | 0.1077        | 1.73  | 2555 | 1.1013          |
  | 0.2104        | 1.78  | 2628 | 1.0765          |
  | 0.1633        | 1.83  | 2701 | 1.0796          |
  | 0.1658        | 1.88  | 2774 | 1.0314          |
  | 0.1358        | 1.92  | 2847 | 0.9823          |
  | 0.1571        | 1.97  | 2920 | 0.9826          |
  | 0.1127        | 2.02  | 2993 | 1.0324          |
  | 0.0927        | 2.07  | 3066 | 1.0679          |
  | 0.0549        | 2.12  | 3139 | 1.1069          |
  | 0.0683        | 2.17  | 3212 | 1.1624          |
  | 0.0677        | 2.22  | 3285 | 1.1174          |
  | 0.0615        | 2.27  | 3358 | 1.1431          |
  | 0.0881        | 2.32  | 3431 | 1.1721          |
  | 0.0807        | 2.37  | 3504 | 1.1885          |
  | 0.0955        | 2.42  | 3577 | 1.1991          |
  | 0.0779        | 2.47  | 3650 | 1.1999          |
  | 0.11          | 2.52  | 3723 | 1.1774          |
  | 0.0852        | 2.57  | 3796 | 1.2095          |
  | 0.0616        | 2.62  | 3869 | 1.1824          |
  | 0.072         | 2.67  | 3942 | 1.2397          |
  | 0.1055        | 2.71  | 4015 | 1.2181          |
  | 0.0806        | 2.76  | 4088 | 1.2159          |
  | 0.0684        | 2.81  | 4161 | 1.1864          |
  | 0.0869        | 2.86  | 4234 | 1.1816          |
  | 0.1023        | 2.91  | 4307 | 1.1717          |
  | 0.0583        | 2.96  | 4380 | 1.1477          |
  | 0.0684        | 3.01  | 4453 | 1.1662          |
  | 0.0319        | 3.06  | 4526 | 1.2174          |
  | 0.0609        | 3.11  | 4599 | 1.1947          |
  | 0.0435        | 3.16  | 4672 | 1.1821          |
  | 0.0417        | 3.21  | 4745 | 1.1964          |
  | 0.0502        | 3.26  | 4818 | 1.2140          |
  | 0.0844        | 3.31  | 4891 | 1.2028          |
  | 0.0692        | 3.36  | 4964 | 1.2215          |
  | 0.0366        | 3.41  | 5037 | 1.2136          |
  | 0.0615        | 3.46  | 5110 | 1.2224          |
  | 0.0656        | 3.5   | 5183 | 1.2468          |
  | 0.0469        | 3.55  | 5256 | 1.2554          |
  | 0.0475        | 3.6   | 5329 | 1.2804          |
  | 0.0998        | 3.65  | 5402 | 1.2035          |
  | 0.0505        | 3.7   | 5475 | 1.2095    