readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: bert-base-uncased-stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE STSB
        type: glue
        config: stsb
        split: validation
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.8872483552831365
  ---
  
  <!-- 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. -->
  
  # bert-base-uncased-stsb
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE STSB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4676
  - Pearson: 0.8901
  - Spearmanr: 0.8872
  - Combined Score: 0.8887
  
  ## 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: 5e-05
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 10
  - distributed_type: multi-GPU
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
  | 2.3939        | 1.0   | 45   | 0.7358          | 0.8686  | 0.8653    | 0.8669         |
  | 0.5084        | 2.0   | 90   | 0.4959          | 0.8835  | 0.8799    | 0.8817         |
  | 0.3332        | 3.0   | 135  | 0.5002          | 0.8846  | 0.8815    | 0.8830         |
  | 0.2202        | 4.0   | 180  | 0.4962          | 0.8854  | 0.8827    | 0.8840         |
  | 0.1642        | 5.0   | 225  | 0.4848          | 0.8864  | 0.8839    | 0.8852         |
  | 0.1312        | 6.0   | 270  | 0.4987          | 0.8872  | 0.8866    | 0.8869         |
  | 0.1057        | 7.0   | 315  | 0.4840          | 0.8895  | 0.8848    | 0.8871         |
  | 0.0935        | 8.0   | 360  | 0.4753          | 0.8887  | 0.8840    | 0.8863         |
  | 0.0835        | 9.0   | 405  | 0.4676          | 0.8901  | 0.8872    | 0.8887         |
  | 0.0749        | 10.0  | 450  | 0.4808          | 0.8901  | 0.8867    | 0.8884         |
  | 0.0625        | 11.0  | 495  | 0.4760          | 0.8893  | 0.8857    | 0.8875         |
  | 0.0607        | 12.0  | 540  | 0.5113          | 0.8899  | 0.8859    | 0.8879         |
  | 0.0564        | 13.0  | 585  | 0.4918          | 0.8900  | 0.8860    | 0.8880         |
  | 0.0495        | 14.0  | 630  | 0.4749          | 0.8905  | 0.8868    | 0.8887         |
  | 0.0446        | 15.0  | 675  | 0.4889          | 0.8888  | 0.8856    | 0.8872         |
  | 0.045         | 16.0  | 720  | 0.4680          | 0.8918  | 0.8889    | 0.8904         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  