readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: ernie-finetuned-qqp
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: qqp
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9156566905763047
      - name: F1
        type: f1
        value: 0.8860522622468757
  ---
  
  <!-- 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. -->
  
  # ernie-finetuned-qqp
  
  This model is a fine-tuned version of [nghuyong/ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4381
  - Accuracy: 0.9157
  - F1: 0.8861
  
  ## 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 | Accuracy | F1     |
  |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
  | 0.2522        | 1.0   | 22741  | 0.2505          | 0.8997   | 0.8633 |
  | 0.1903        | 2.0   | 45482  | 0.2645          | 0.9071   | 0.8761 |
  | 0.1599        | 3.0   | 68223  | 0.2986          | 0.9115   | 0.8816 |
  | 0.1214        | 4.0   | 90964  | 0.3640          | 0.9133   | 0.8828 |
  | 0.0809        | 5.0   | 113705 | 0.4381          | 0.9157   | 0.8861 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  