readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - spearmanr
  model-index:
  - name: data2vec-text-base-finetuned-stsb
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: stsb
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.8716633516590501
  ---
  
  <!-- 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. -->
  
  # data2vec-text-base-finetuned-stsb
  
  This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5530
  - Pearson: 0.8732
  - Spearmanr: 0.8717
  
  ## 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: 7.725353773731373e-05
  - train_batch_size: 32
  - eval_batch_size: 16
  - seed: 5
  - 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   | 180  | 1.0650          | 0.8102  | 0.8380    |
  | No log        | 2.0   | 360  | 0.6211          | 0.8524  | 0.8497    |
  | 0.9312        | 3.0   | 540  | 0.5917          | 0.8640  | 0.8642    |
  | 0.9312        | 4.0   | 720  | 0.5672          | 0.8695  | 0.8686    |
  | 0.9312        | 5.0   | 900  | 0.5530          | 0.8732  | 0.8717    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  