readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: small-mlm-snli-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. -->
  
  # small-mlm-snli-from-scratch-custom-tokenizer-target-conll2003
  
  This model is a fine-tuned version of [muhtasham/small-mlm-snli-from-scratch-custom-tokenizer](https://huggingface.co/muhtasham/small-mlm-snli-from-scratch-custom-tokenizer) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3378
  - Precision: 0.4244
  - Recall: 0.5441
  - F1: 0.4768
  - Accuracy: 0.8956
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.7215        | 1.14  | 500  | 0.5706          | 0.1492    | 0.2666 | 0.1913 | 0.8173   |
  | 0.5251        | 2.28  | 1000 | 0.4653          | 0.2138    | 0.3122 | 0.2538 | 0.8465   |
  | 0.4538        | 3.42  | 1500 | 0.4361          | 0.2118    | 0.4010 | 0.2772 | 0.8472   |
  | 0.3962        | 4.56  | 2000 | 0.3844          | 0.2868    | 0.4187 | 0.3404 | 0.8677   |
  | 0.356         | 5.69  | 2500 | 0.3705          | 0.3088    | 0.4731 | 0.3737 | 0.8702   |
  | 0.3256        | 6.83  | 3000 | 0.3540          | 0.3297    | 0.4798 | 0.3908 | 0.8790   |
  | 0.2923        | 7.97  | 3500 | 0.3442          | 0.3762    | 0.4845 | 0.4235 | 0.8851   |
  | 0.2599        | 9.11  | 4000 | 0.3410          | 0.3992    | 0.5101 | 0.4479 | 0.8904   |
  | 0.2345        | 10.25 | 4500 | 0.3375          | 0.4118    | 0.5279 | 0.4627 | 0.8928   |
  | 0.2119        | 11.39 | 5000 | 0.3378          | 0.4244    | 0.5441 | 0.4768 | 0.8956   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  