readme: ## bert-base-uncased finetuned on IMDB dataset
  
  Evaluation set was created by taking 1000 samples from test set
  
  ```
  DatasetDict({
      train: Dataset({
          features: ['text', 'label'],
          num_rows: 25000
      })
      dev: Dataset({
          features: ['text', 'label'],
          num_rows: 1000
      })
      test: Dataset({
          features: ['text', 'label'],
          num_rows: 24000
      })
  })
  ```
  
  ## Parameters
  ```
  max_sequence_length = 128
  batch_size = 32
  eval_steps = 100
  learning_rate=2e-05
  num_train_epochs=5
  early_stopping_patience = 10
  ```
  
  ## Training Run
  ```
   [2700/3910 1:11:43 < 32:09, 0.63 it/s, Epoch 3/5]
  Step 	Training Loss 	Validation Loss 	Accuracy 	Precision 	Recall 	F1 	Runtime 	Samples Per Second
  100 	No log 	0.371974 	0.845000 	0.798942 	0.917004 	0.853911 	15.256900 	65.544000
  200 	No log 	0.349631 	0.850000 	0.873913 	0.813765 	0.842767 	15.288600 	65.408000
  300 	No log 	0.359376 	0.845000 	0.869281 	0.807692 	0.837356 	15.303900 	65.343000
  400 	No log 	0.307613 	0.870000 	0.851351 	0.892713 	0.871542 	15.358400 	65.111000
  500 	0.364500 	0.309362 	0.856000 	0.807018 	0.931174 	0.864662 	15.326100 	65.248000
  600 	0.364500 	0.302709 	0.867000 	0.881607 	0.844130 	0.862461 	15.324400 	65.255000
  700 	0.364500 	0.300102 	0.871000 	0.894168 	0.838057 	0.865204 	15.474900 	64.621000
  800 	0.364500 	0.383784 	0.866000 	0.833333 	0.910931 	0.870406 	15.380100 	65.019000
  900 	0.364500 	0.309934 	0.874000 	0.881743 	0.860324 	0.870902 	15.358900 	65.109000
  1000 	0.254600 	0.332236 	0.872000 	0.894397 	0.840081 	0.866388 	15.442700 	64.756000
  1100 	0.254600 	0.330807 	0.871000 	0.877847 	0.858300 	0.867963 	15.410900 	64.889000
  1200 	0.254600 	0.352724 	0.872000 	0.925581 	0.805668 	0.861472 	15.272800 	65.476000
  1300 	0.254600 	0.278529 	0.881000 	0.891441 	0.864372 	0.877698 	15.408200 	64.900000
  1400 	0.254600 	0.291371 	0.878000 	0.854962 	0.906883 	0.880157 	15.427400 	64.820000
  1500 	0.208400 	0.324827 	0.869000 	0.904232 	0.821862 	0.861082 	15.338600 	65.195000
  1600 	0.208400 	0.377024 	0.884000 	0.898734 	0.862348 	0.880165 	15.414500 	64.874000
  1700 	0.208400 	0.375274 	0.885000 	0.881288 	0.886640 	0.883956 	15.367200 	65.073000
  1800 	0.208400 	0.378904 	0.880000 	0.877016 	0.880567 	0.878788 	15.363900 	65.088000
  1900 	0.208400 	0.410517 	0.874000 	0.866534 	0.880567 	0.873494 	15.324700 	65.254000
  2000 	0.130800 	0.404030 	0.876000 	0.888655 	0.856275 	0.872165 	15.414200 	64.875000
  2100 	0.130800 	0.390763 	0.883000 	0.882353 	0.880567 	0.881459 	15.341500 	65.183000
  2200 	0.130800 	0.417967 	0.880000 	0.875502 	0.882591 	0.879032 	15.351300 	65.141000
  2300 	0.130800 	0.390974 	0.883000 	0.898520 	0.860324 	0.879007 	15.396100 	64.952000
  2400 	0.130800 	0.479739 	0.874000 	0.856589 	0.894737 	0.875248 	15.460500 	64.681000
  2500 	0.098400 	0.473215 	0.875000 	0.883576 	0.860324 	0.871795 	15.392200 	64.968000
  2600 	0.098400 	0.532294 	0.872000 	0.889362 	0.846154 	0.867220 	15.364100 	65.087000
  2700 	0.098400 	0.536664 	0.881000 	0.880325 	0.878543 	0.879433 	15.351100 	65.142000
  
  TrainOutput(global_step=2700, training_loss=0.2004435383832013, metrics={'train_runtime': 4304.5331, 'train_samples_per_second': 0.908, 'total_flos': 7258763970957312, 'epoch': 3.45})
  ```
  
  ## Classification Report
  ```
              precision    recall  f1-score   support
  
             0       0.90      0.87      0.89     11994
             1       0.87      0.90      0.89     12006
  
      accuracy                           0.89     24000
     macro avg       0.89      0.89      0.89     24000
  weighted avg       0.89      0.89      0.89     24000
  ```
  