readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: distilbert-base-uncased-finetuned-stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.8636303639161342
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-stsb
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5644
  - Pearson: 0.8666
  - Spearmanr: 0.8636
  
  ## 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: 16
  - 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 | Pearson | Spearmanr |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
  | No log        | 1.0   | 360  | 0.6366          | 0.8537  | 0.8516    |
  | 1.0464        | 2.0   | 720  | 0.6171          | 0.8632  | 0.8626    |
  | 0.4002        | 3.0   | 1080 | 0.6082          | 0.8663  | 0.8643    |
  | 0.4002        | 4.0   | 1440 | 0.5644          | 0.8666  | 0.8636    |
  | 0.2479        | 5.0   | 1800 | 0.5780          | 0.8654  | 0.8624    |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  