readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - klue
  metrics:
  - pearsonr
  model-index:
  - name: bert-base-finetuned-sts
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: klue
        type: klue
        args: sts
      metrics:
      - name: Pearsonr
        type: pearsonr
        value: 0.9000373376026184
  ---
  
  <!-- 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-finetuned-sts
  
  This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4582
  - Pearsonr: 0.9000
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Pearsonr |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 183  | 0.5329          | 0.8827   |
  | No log        | 2.0   | 366  | 0.4549          | 0.8937   |
  | 0.2316        | 3.0   | 549  | 0.4656          | 0.8959   |
  | 0.2316        | 4.0   | 732  | 0.4651          | 0.8990   |
  | 0.2316        | 5.0   | 915  | 0.4582          | 0.9000   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  