readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: small-vanilla-target-glue-mnli-linear-probe
    results: []
  ---
  
  <!-- 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. -->
  
  # small-vanilla-target-glue-mnli-linear-probe
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0612
  - Accuracy: 0.4363
  
  ## 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: 3e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - training_steps: 5000
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1093        | 0.04  | 500  | 1.0875          | 0.3914   |
  | 1.089         | 0.08  | 1000 | 1.0814          | 0.3988   |
  | 1.0811        | 0.12  | 1500 | 1.0760          | 0.4113   |
  | 1.0753        | 0.16  | 2000 | 1.0728          | 0.4200   |
  | 1.0758        | 0.2   | 2500 | 1.0702          | 0.4252   |
  | 1.0727        | 0.24  | 3000 | 1.0684          | 0.4269   |
  | 1.0707        | 0.29  | 3500 | 1.0665          | 0.4295   |
  | 1.0702        | 0.33  | 4000 | 1.0648          | 0.4317   |
  | 1.0654        | 0.37  | 4500 | 1.0627          | 0.4352   |
  | 1.0637        | 0.41  | 5000 | 1.0612          | 0.4363   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0.dev0
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.1.dev0
  - Tokenizers 0.13.2
  