readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: albert-base-v2-finetuned-rte
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: rte
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7581227436823105
  ---
  
  <!-- 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. -->
  
  # albert-base-v2-finetuned-rte
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2496
  - Accuracy: 0.7581
  
  ## 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: 10
  - eval_batch_size: 10
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 1.0   | 249  | 0.5914          | 0.6751   |
  | No log        | 2.0   | 498  | 0.5843          | 0.7184   |
  | 0.5873        | 3.0   | 747  | 0.6925          | 0.7220   |
  | 0.5873        | 4.0   | 996  | 1.1613          | 0.7545   |
  | 0.2149        | 5.0   | 1245 | 1.2496          | 0.7581   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  