readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert_sa_GLUE_Experiment_mnli_96
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MNLI
        type: glue
        config: mnli
        split: validation_matched
        args: mnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.5545158665581774
  ---
  
  <!-- 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_mnli_96
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE MNLI dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9288
  - Accuracy: 0.5545
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.0498        | 1.0   | 1534  | 0.9988          | 0.5084   |
  | 0.9757        | 2.0   | 3068  | 0.9532          | 0.5303   |
  | 0.9458        | 3.0   | 4602  | 0.9435          | 0.5377   |
  | 0.9272        | 4.0   | 6136  | 0.9306          | 0.5456   |
  | 0.9122        | 5.0   | 7670  | 0.9305          | 0.5474   |
  | 0.8992        | 6.0   | 9204  | 0.9294          | 0.5489   |
  | 0.8867        | 7.0   | 10738 | 0.9260          | 0.5522   |
  | 0.8752        | 8.0   | 12272 | 0.9319          | 0.5559   |
  | 0.8645        | 9.0   | 13806 | 0.9336          | 0.5604   |
  | 0.8545        | 10.0  | 15340 | 0.9200          | 0.5629   |
  | 0.8443        | 11.0  | 16874 | 0.9200          | 0.5664   |
  | 0.8338        | 12.0  | 18408 | 0.9298          | 0.5672   |
  | 0.8252        | 13.0  | 19942 | 0.9383          | 0.5647   |
  | 0.8168        | 14.0  | 21476 | 0.9428          | 0.5691   |
  | 0.8084        | 15.0  | 23010 | 0.9325          | 0.5730   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  