readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-large-cased-sigir-LR100-1-cased-150
    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-large-cased-sigir-LR100-1-cased-150
  
  This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.4908
  
  ## 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: 0.00015
  - train_batch_size: 30
  - eval_batch_size: 30
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 150.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | 7.0223        | 1.0   | 1    | 6.3939          |
  | 7.139         | 2.0   | 2    | 7.0236          |
  | 7.0156        | 3.0   | 3    | 7.0893          |
  | 7.2089        | 4.0   | 4    | 5.3251          |
  | 5.7375        | 5.0   | 5    | 3.5431          |
  | 3.8594        | 6.0   | 6    | 3.2612          |
  | 3.2379        | 7.0   | 7    | 2.8294          |
  | 3.2598        | 8.0   | 8    | 3.1189          |
  | 3.0582        | 9.0   | 9    | 2.3748          |
  | 2.6474        | 10.0  | 10   | 3.0741          |
  | 3.0331        | 11.0  | 11   | 1.8630          |
  | 2.7435        | 12.0  | 12   | 2.5527          |
  | 2.3269        | 13.0  | 13   | 2.1300          |
  | 2.0739        | 14.0  | 14   | 2.6337          |
  | 1.9651        | 15.0  | 15   | 1.8872          |
  | 2.1553        | 16.0  | 16   | 2.2753          |
  | 1.828         | 17.0  | 17   | 2.6648          |
  | 1.6217        | 18.0  | 18   | 2.5176          |
  | 1.681         | 19.0  | 19   | 1.0566          |
  | 1.5415        | 20.0  | 20   | 1.7287          |
  | 1.1874        | 21.0  | 21   | 1.8245          |
  | 1.1892        | 22.0  | 22   | 3.3104          |
  | 1.3973        | 23.0  | 23   | 1.7072          |
  | 1.132         | 24.0  | 24   | 2.7579          |
  | 1.2451        | 25.0  | 25   | 2.0380          |
  | 1.2669        | 26.0  | 26   | 2.7239          |
  | 1.1353        | 27.0  | 27   | 2.3039          |
  | 1.319         | 28.0  | 28   | 1.9489          |
  | 1.1246        | 29.0  | 29   | 2.1581          |
  | 1.332         | 30.0  | 30   | 2.5226          |
  | 1.2593        | 31.0  | 31   | 2.1640          |
  | 1.2299        | 32.0  | 32   | 1.1443          |
  | 1.0047        | 33.0  | 33   | 1.5572          |
  | 0.7948        | 34.0  | 34   | 1.8577          |
  | 1.151         | 35.0  | 35   | 1.9391          |
  | 0.905         | 36.0  | 36   | 2.2505          |
  | 1.0517        | 37.0  | 37   | 2.4417          |
  | 1.1055        | 38.0  | 38   | 1.9173          |
  | 0.837         | 39.0  | 39   | 2.2970          |
  | 0.911         | 40.0  | 40   | 1.6180          |
  | 0.7643        | 41.0  | 41   | 1.4066          |
  | 0.8754        | 42.0  | 42   | 2.7884          |
  | 1.2184        | 43.0  | 43   | 2.3925          |
  | 0.8623        | 44.0  | 44   | 2.0238          |
  | 0.7886        | 45.0  | 45   | 2.7872          |
  | 0.7089        | 46.0  | 46   | 2.7488          |
  | 0.5684        | 47.0  | 47   | 2.3370          |
  | 0.7299        | 48.0  | 48   | 2.1650          |
  | 0.6906        | 49.0  | 49   | 2.7732          |
  | 0.6876        | 50.0  | 50   | 3.0168          |
  | 0.3967        | 51.0  | 51   | 2.1091          |
  | 0.6191        | 52.0  | 52   | 1.0602          |
  | 0.5068        | 53.0  | 53   | 3.1522          |
  | 0.7189        | 54.0  | 54   | 3.0988          |
  | 0.6768        | 55.0  | 55   | 1.7392          |
  | 0.6247        | 56.0  | 56   | 1.8365          |
  | 0.5057        | 57.0  | 57   | 2.8095          |
  | 0.65          | 58.0  | 58   | 2.8217          |
  | 0.5398        | 59.0  | 59   | 2.3459          |
  | 0.5703        | 60.0  | 60   | 1.6460          |
  | 0.5543        | 61.0  | 61   | 1.7489          |
  | 0.5031        | 62.0  | 62   | 2.4358          |
  | 0.447         | 63.0  | 63   | 2.5432          |
  | 0.5056        | 64.0  | 64   | 3.0374          |
  | 0.5161        | 65.0  | 65   | 2.2071          |
  | 0.4509        | 66.0  | 66   | 3.4950          |
  | 0.3973        | 67.0  | 67   | 3.0061          |
  | 0.4469        | 68.0  | 68   | 2.7547          |
  | 0.4062        | 69.0  | 69   | 3.3072          |
  | 0.2518        | 70.0  | 70   | 2.1072          |
  | 0.2339        | 71.0  | 71   | 2.1484          |
  | 0.5058        | 72.0  | 72   | 3.2433          |
  | 0.2827        | 73.0  | 73   | 1.6271          |
  | 0.3282        | 74.0  | 74   | 2.1436          |
  | 0.3415        | 75.0  | 75   | 2.8307 