readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - spearmanr
  model-index:
  - name: tiny-vanilla-target-glue-stsb
    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-stsb
  
  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: 0.8716
  - Pearson: 0.8141
  - Spearmanr: 0.8126
  
  ## 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 | Pearson | Spearmanr |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
  | 3.1371        | 2.78  | 500  | 1.1225          | 0.7236  | 0.7292    |
  | 0.9656        | 5.56  | 1000 | 1.0702          | 0.7703  | 0.7991    |
  | 0.7396        | 8.33  | 1500 | 1.0547          | 0.7931  | 0.8189    |
  | 0.6264        | 11.11 | 2000 | 0.9011          | 0.8123  | 0.8214    |
  | 0.5242        | 13.89 | 2500 | 0.8999          | 0.8135  | 0.8174    |
  | 0.4756        | 16.67 | 3000 | 0.9771          | 0.8142  | 0.8192    |
  | 0.4225        | 19.44 | 3500 | 0.9021          | 0.8168  | 0.8176    |
  | 0.3879        | 22.22 | 4000 | 0.9447          | 0.8176  | 0.8181    |
  | 0.3547        | 25.0  | 4500 | 0.8787          | 0.8226  | 0.8216    |
  | 0.3355        | 27.78 | 5000 | 0.9789          | 0.8157  | 0.8169    |
  | 0.3143        | 30.56 | 5500 | 0.9259          | 0.8152  | 0.8149    |
  | 0.2925        | 33.33 | 6000 | 0.8716          | 0.8141  | 0.8126    |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  