readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: tiny-mlm-rotten_tomatoes-from-scratch-custom-tokenizer-target-conll2003
    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. -->
  
  # tiny-mlm-rotten_tomatoes-from-scratch-custom-tokenizer-target-conll2003
  
  This model is a fine-tuned version of [muhtasham/tiny-mlm-rotten_tomatoes-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/tiny-mlm-rotten_tomatoes-from-scratch-custom-tokenizer) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3866
  - Precision: 0.2926
  - Recall: 0.4611
  - F1: 0.3580
  - Accuracy: 0.8772
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - training_steps: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 1.0779        | 1.14  | 500  | 0.7741          | 0.0628    | 0.0628 | 0.0628 | 0.7972   |
  | 0.6749        | 2.28  | 1000 | 0.6240          | 0.1095    | 0.1542 | 0.1281 | 0.8198   |
  | 0.5795        | 3.42  | 1500 | 0.5582          | 0.1644    | 0.2497 | 0.1983 | 0.8358   |
  | 0.5224        | 4.56  | 2000 | 0.5173          | 0.1843    | 0.3007 | 0.2286 | 0.8456   |
  | 0.4832        | 5.69  | 2500 | 0.4865          | 0.1964    | 0.3394 | 0.2488 | 0.8528   |
  | 0.4491        | 6.83  | 3000 | 0.4519          | 0.2300    | 0.3746 | 0.2850 | 0.8621   |
  | 0.4138        | 7.97  | 3500 | 0.4238          | 0.2540    | 0.4214 | 0.3170 | 0.8684   |
  | 0.3843        | 9.11  | 4000 | 0.4093          | 0.2598    | 0.4313 | 0.3243 | 0.8711   |
  | 0.3638        | 10.25 | 4500 | 0.3973          | 0.2734    | 0.4465 | 0.3392 | 0.8741   |
  | 0.3483        | 11.39 | 5000 | 0.3866          | 0.2926    | 0.4611 | 0.3580 | 0.8772   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  