readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - rotten_tomatoes
  metrics:
  - accuracy
  model-index:
  - name: rtm_DistilBERT_5E
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: rotten_tomatoes
        type: rotten_tomatoes
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.82
  ---
  
  <!-- 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. -->
  
  # rtm_DistilBERT_5E
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the rotten_tomatoes dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6835
  - Accuracy: 0.82
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - 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 | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.6822        | 0.09  | 50   | 0.6391          | 0.76     |
  | 0.5531        | 0.19  | 100  | 0.4684          | 0.7667   |
  | 0.4546        | 0.28  | 150  | 0.4479          | 0.7733   |
  | 0.4495        | 0.37  | 200  | 0.3953          | 0.8067   |
  | 0.4239        | 0.47  | 250  | 0.4211          | 0.7933   |
  | 0.3951        | 0.56  | 300  | 0.4126          | 0.7933   |
  | 0.3861        | 0.66  | 350  | 0.3950          | 0.7933   |
  | 0.4108        | 0.75  | 400  | 0.4091          | 0.82     |
  | 0.3778        | 0.84  | 450  | 0.4107          | 0.7933   |
  | 0.3627        | 0.94  | 500  | 0.4203          | 0.7933   |
  | 0.3648        | 1.03  | 550  | 0.4190          | 0.8      |
  | 0.2899        | 1.12  | 600  | 0.4436          | 0.8      |
  | 0.2637        | 1.22  | 650  | 0.4504          | 0.82     |
  | 0.2885        | 1.31  | 700  | 0.4406          | 0.82     |
  | 0.3226        | 1.4   | 750  | 0.4398          | 0.8333   |
  | 0.3147        | 1.5   | 800  | 0.4239          | 0.82     |
  | 0.2937        | 1.59  | 850  | 0.4227          | 0.8133   |
  | 0.3149        | 1.69  | 900  | 0.3791          | 0.82     |
  | 0.3227        | 1.78  | 950  | 0.3888          | 0.8133   |
  | 0.2727        | 1.87  | 1000 | 0.4215          | 0.82     |
  | 0.2722        | 1.97  | 1050 | 0.4099          | 0.8333   |
  | 0.1908        | 2.06  | 1100 | 0.4595          | 0.82     |
  | 0.2276        | 2.15  | 1150 | 0.4572          | 0.84     |
  | 0.2239        | 2.25  | 1200 | 0.4545          | 0.8333   |
  | 0.1986        | 2.34  | 1250 | 0.4895          | 0.82     |
  | 0.2388        | 2.43  | 1300 | 0.4352          | 0.86     |
  | 0.1901        | 2.53  | 1350 | 0.4806          | 0.84     |
  | 0.2227        | 2.62  | 1400 | 0.5473          | 0.8067   |
  | 0.2221        | 2.72  | 1450 | 0.5010          | 0.84     |
  | 0.1955        | 2.81  | 1500 | 0.5315          | 0.8267   |
  | 0.2114        | 2.9   | 1550 | 0.5410          | 0.8133   |
  | 0.1827        | 3.0   | 1600 | 0.5721          | 0.8133   |
  | 0.1527        | 3.09  | 1650 | 0.5616          | 0.8133   |
  | 0.1464        | 3.18  | 1700 | 0.5935          | 0.8067   |
  | 0.135         | 3.28  | 1750 | 0.6145          | 0.82     |
  | 0.1668        | 3.37  | 1800 | 0.6901          | 0.8067   |
  | 0.1702        | 3.46  | 1850 | 0.6067          | 0.8133   |
  | 0.1738        | 3.56  | 1900 | 0.5981          | 0.82     |
  | 0.1506        | 3.65  | 1950 | 0.6073          | 0.8267   |
  | 0.1584        | 3.75  | 2000 | 0.6549          | 0.8133   |
  | 0.1698        | 3.84  | 2050 | 0.6660          | 0.8267   |
  | 0.1626        | 3.93  | 2100 | 0.6645          | 0.8267   |
  | 0.1483        | 4.03  | 2150 | 0.6497          | 0.82     |
  | 0.1342        | 4.12  | 2200 | 0.6643          | 0.82     |
  | 0.1064        | 4.21  | 2250 | 0.6775          | 0.82     |
  | 0.1302        | 4.31  | 2300 | 0.6876          | 0.82     |
  | 0.1847        | 4.4   | 2350 | 0.6821          | 0.8133   |
  | 0.1055        | 4.49  | 2400 | 0.6928          | 0.8133   |
  | 0.1372        | 4.59  | 2450 | 0.6877          | 0.8133   |
  | 0.131         | 4.68  | 2500 | 0.6769          | 0.8267   |
  | 0.1242        | 4.78  | 2550 | 0.6769          | 0.8267   |
  | 0.1289        | 4.87  | 2600 | 0.6810          | 0.82     |
  | 0.1488        | 4.96  | 2650 | 0.6835          | 0.82     |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  