readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: small-mlm-rotten_tomatoes-from-scratch-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. -->
  
  # small-mlm-rotten_tomatoes-from-scratch-target-conll2003
  
  This model is a fine-tuned version of [muhtasham/small-mlm-rotten_tomatoes-from-scratch](https://huggingface.co/muhtasham/small-mlm-rotten_tomatoes-from-scratch) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2814
  - Precision: 0.5816
  - Recall: 0.7099
  - F1: 0.6394
  - Accuracy: 0.9299
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.6075        | 1.14  | 500  | 0.4273          | 0.3917    | 0.4366 | 0.4129 | 0.8743   |
  | 0.3582        | 2.28  | 1000 | 0.3436          | 0.4459    | 0.5478 | 0.4916 | 0.8969   |
  | 0.2718        | 3.42  | 1500 | 0.3134          | 0.4499    | 0.6254 | 0.5233 | 0.9032   |
  | 0.2142        | 4.56  | 2000 | 0.2927          | 0.4969    | 0.6298 | 0.5555 | 0.9122   |
  | 0.1751        | 5.69  | 2500 | 0.2815          | 0.5500    | 0.6570 | 0.5988 | 0.9192   |
  | 0.1468        | 6.83  | 3000 | 0.2693          | 0.5472    | 0.6930 | 0.6115 | 0.9221   |
  | 0.119         | 7.97  | 3500 | 0.2669          | 0.5506    | 0.6880 | 0.6117 | 0.9245   |
  | 0.0959        | 9.11  | 4000 | 0.2817          | 0.5691    | 0.6747 | 0.6174 | 0.9267   |
  | 0.0804        | 10.25 | 4500 | 0.2860          | 0.5572    | 0.7065 | 0.6230 | 0.9251   |
  | 0.0687        | 11.39 | 5000 | 0.2814          | 0.5816    | 0.7099 | 0.6394 | 0.9299   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  