readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_sa_GLUE_Experiment_logit_kd_data_aug_sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8635321100917431
  ---
  
  <!-- 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_data_aug_sst2
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4215
  - Accuracy: 0.8635
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.434         | 1.0   | 4374  | 0.6108          | 0.8222   |
  | 0.2568        | 2.0   | 8748  | 0.5465          | 0.8314   |
  | 0.207         | 3.0   | 13122 | 0.5067          | 0.8463   |
  | 0.1819        | 4.0   | 17496 | 0.4734          | 0.8544   |
  | 0.1666        | 5.0   | 21870 | 0.4785          | 0.8578   |
  | 0.1563        | 6.0   | 26244 | 0.4539          | 0.8589   |
  | 0.1492        | 7.0   | 30618 | 0.4600          | 0.8589   |
  | 0.1436        | 8.0   | 34992 | 0.4445          | 0.8647   |
  | 0.1394        | 9.0   | 39366 | 0.4270          | 0.8727   |
  | 0.1361        | 10.0  | 43740 | 0.4524          | 0.8601   |
  | 0.1334        | 11.0  | 48114 | 0.4244          | 0.8693   |
  | 0.1313        | 12.0  | 52488 | 0.4469          | 0.8635   |
  | 0.1292        | 13.0  | 56862 | 0.4556          | 0.8498   |
  | 0.1277        | 14.0  | 61236 | 0.4257          | 0.8635   |
  | 0.1263        | 15.0  | 65610 | 0.4392          | 0.8567   |
  | 0.1251        | 16.0  | 69984 | 0.4215          | 0.8635   |
  | 0.124         | 17.0  | 74358 | 0.4289          | 0.8578   |
  | 0.123         | 18.0  | 78732 | 0.4448          | 0.8601   |
  | 0.1222        | 19.0  | 83106 | 0.4562          | 0.8555   |
  | 0.1214        | 20.0  | 87480 | 0.4377          | 0.8544   |
  | 0.1207        | 21.0  | 91854 | 0.4563          | 0.8555   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  