readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_rte
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE RTE
        type: glue
        config: rte
        split: validation
        args: rte
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.4729241877256318
  ---
  
  <!-- 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_logit_kd_rte
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE RTE dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4234
  - Accuracy: 0.4729
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4797        | 1.0   | 10   | 0.4265          | 0.4729   |
  | 0.4185        | 2.0   | 20   | 0.4243          | 0.4729   |
  | 0.4177        | 3.0   | 30   | 0.4247          | 0.4729   |
  | 0.4158        | 4.0   | 40   | 0.4234          | 0.4729   |
  | 0.4139        | 5.0   | 50   | 0.4357          | 0.4729   |
  | 0.4052        | 6.0   | 60   | 0.4433          | 0.4729   |
  | 0.3792        | 7.0   | 70   | 0.4748          | 0.4874   |
  | 0.3413        | 8.0   | 80   | 0.4962          | 0.4946   |
  | 0.3104        | 9.0   | 90   | 0.5339          | 0.4838   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  