readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: distilbert_sa_GLUE_Experiment_qqp_256
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE QQP
        type: glue
        config: qqp
        split: validation
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8029680930002473
      - name: F1
        type: f1
        value: 0.7323432565015792
  ---
  
  <!-- 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_sa_GLUE_Experiment_qqp_256
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QQP dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4425
  - Accuracy: 0.8030
  - F1: 0.7323
  - Combined Score: 0.7677
  
  ## 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: 5e-05
  - train_batch_size: 256
  - eval_batch_size: 256
  - seed: 10
  - distributed_type: multi-GPU
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 50
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.53          | 1.0   | 1422  | 0.5023          | 0.7557   | 0.6592 | 0.7075         |
  | 0.479         | 2.0   | 2844  | 0.4823          | 0.7679   | 0.6483 | 0.7081         |
  | 0.4522        | 3.0   | 4266  | 0.4788          | 0.7741   | 0.6474 | 0.7108         |
  | 0.4263        | 4.0   | 5688  | 0.4753          | 0.7829   | 0.6911 | 0.7370         |
  | 0.4009        | 5.0   | 7110  | 0.4536          | 0.7906   | 0.7194 | 0.7550         |
  | 0.3772        | 6.0   | 8532  | 0.4497          | 0.7949   | 0.7200 | 0.7574         |
  | 0.3548        | 7.0   | 9954  | 0.4453          | 0.8010   | 0.7201 | 0.7606         |
  | 0.3332        | 8.0   | 11376 | 0.4425          | 0.8030   | 0.7323 | 0.7677         |
  | 0.3132        | 9.0   | 12798 | 0.4654          | 0.7938   | 0.7375 | 0.7657         |
  | 0.2951        | 10.0  | 14220 | 0.4551          | 0.8056   | 0.7423 | 0.7739         |
  | 0.2777        | 11.0  | 15642 | 0.4675          | 0.8120   | 0.7374 | 0.7747         |
  | 0.2625        | 12.0  | 17064 | 0.4946          | 0.8082   | 0.7451 | 0.7766         |
  | 0.2473        | 13.0  | 18486 | 0.5041          | 0.8102   | 0.7469 | 0.7786         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  