readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: bert-base-cased-stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE STSB
        type: glue
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.8963451800582044
  ---
  
  <!-- 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-cased-stsb
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE STSB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4322
  - Pearson: 0.9007
  - Spearmanr: 0.8963
  - Combined Score: 0.8985
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
  | 1.6464        | 1.39  | 500  | 0.5662          | 0.8820  | 0.8814    | 0.8817         |
  | 0.3329        | 2.78  | 1000 | 0.5070          | 0.8913  | 0.8883    | 0.8898         |
  | 0.173         | 4.17  | 1500 | 0.4465          | 0.8988  | 0.8943    | 0.8966         |
  | 0.1085        | 5.56  | 2000 | 0.4537          | 0.8958  | 0.8917    | 0.8937         |
  | 0.0816        | 6.94  | 2500 | 0.4594          | 0.8977  | 0.8933    | 0.8955         |
  | 0.0621        | 8.33  | 3000 | 0.4450          | 0.8997  | 0.8950    | 0.8974         |
  | 0.0519        | 9.72  | 3500 | 0.4322          | 0.9007  | 0.8963    | 0.8985         |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.7.1
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  