readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  model-index:
  - name: tiny-vanilla-target-glue-qqp
    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-vanilla-target-glue-qqp
  
  This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4162
  - Accuracy: 0.7951
  - F1: 0.7610
  
  ## 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
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 0.5864        | 0.04  | 500  | 0.5228          | 0.7257   | 0.6710 |
  | 0.5173        | 0.09  | 1000 | 0.4944          | 0.7372   | 0.7000 |
  | 0.5005        | 0.13  | 1500 | 0.4983          | 0.7317   | 0.7096 |
  | 0.4853        | 0.18  | 2000 | 0.4763          | 0.7451   | 0.7176 |
  | 0.474         | 0.22  | 2500 | 0.4644          | 0.7546   | 0.7240 |
  | 0.4584        | 0.26  | 3000 | 0.4570          | 0.7617   | 0.7321 |
  | 0.4584        | 0.31  | 3500 | 0.4513          | 0.7640   | 0.7348 |
  | 0.4531        | 0.35  | 4000 | 0.4587          | 0.7587   | 0.7345 |
  | 0.4536        | 0.4   | 4500 | 0.4523          | 0.7627   | 0.7383 |
  | 0.4444        | 0.44  | 5000 | 0.4282          | 0.7847   | 0.7467 |
  | 0.4323        | 0.48  | 5500 | 0.4415          | 0.7718   | 0.7445 |
  | 0.4315        | 0.53  | 6000 | 0.4130          | 0.7969   | 0.7556 |
  | 0.4275        | 0.57  | 6500 | 0.4339          | 0.7791   | 0.7506 |
  | 0.4272        | 0.62  | 7000 | 0.4127          | 0.7949   | 0.7568 |
  | 0.4182        | 0.66  | 7500 | 0.4162          | 0.7951   | 0.7610 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  