readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: mobilebert_add_GLUE_Experiment_mrpc_128
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE MRPC
        type: glue
        config: mrpc
        split: validation
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6838235294117647
      - name: F1
        type: f1
        value: 0.8122270742358079
  ---
  
  <!-- 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. -->
  
  # mobilebert_add_GLUE_Experiment_mrpc_128
  
  This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on the GLUE MRPC dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6231
  - Accuracy: 0.6838
  - F1: 0.8122
  - Combined Score: 0.7480
  
  ## 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: 128
  - eval_batch_size: 128
  - 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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Combined Score |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------------:|
  | 0.6471        | 1.0   | 29   | 0.6239          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6304        | 2.0   | 58   | 0.6242          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6314        | 3.0   | 87   | 0.6249          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6307        | 4.0   | 116  | 0.6250          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6298        | 5.0   | 145  | 0.6233          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6283        | 6.0   | 174  | 0.6233          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6283        | 7.0   | 203  | 0.6231          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6224        | 8.0   | 232  | 0.6265          | 0.6838   | 0.8122 | 0.7480         |
  | 0.6042        | 9.0   | 261  | 0.6355          | 0.6838   | 0.8122 | 0.7480         |
  | 0.5862        | 10.0  | 290  | 0.6303          | 0.6838   | 0.8122 | 0.7480         |
  | 0.5717        | 11.0  | 319  | 0.6515          | 0.6324   | 0.7525 | 0.6924         |
  | 0.5641        | 12.0  | 348  | 0.6412          | 0.6838   | 0.8122 | 0.7480         |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.14.0a0+410ce96
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  