readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: roberta-base-stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE STSB
        type: glue
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.9092158650855444
  ---
  
  <!-- 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. -->
  
  # roberta-base-stsb
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE STSB dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4221
  - Pearson: 0.9116
  - Spearmanr: 0.9092
  - Combined Score: 0.9104
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
  | 1.6552        | 1.39  | 500  | 0.5265          | 0.8925  | 0.8925    | 0.8925         |
  | 0.3579        | 2.78  | 1000 | 0.4626          | 0.9022  | 0.8991    | 0.9007         |
  | 0.2198        | 4.17  | 1500 | 0.4396          | 0.9054  | 0.9042    | 0.9048         |
  | 0.1585        | 5.56  | 2000 | 0.4537          | 0.9069  | 0.9052    | 0.9060         |
  | 0.1139        | 6.94  | 2500 | 0.4975          | 0.9091  | 0.9065    | 0.9078         |
  | 0.0868        | 8.33  | 3000 | 0.4221          | 0.9116  | 0.9092    | 0.9104         |
  | 0.073         | 9.72  | 3500 | 0.4311          | 0.9096  | 0.9077    | 0.9086         |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.7.1
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  