readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: olm-bert-tiny-december-2022-target-glue-mnli
    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. -->
  
  # olm-bert-tiny-december-2022-target-glue-mnli
  
  This model is a fine-tuned version of [muhtasham/olm-bert-tiny-december-2022](https://huggingface.co/muhtasham/olm-bert-tiny-december-2022) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9980
  - Accuracy: 0.5011
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.0986        | 0.04  | 500  | 1.0979          | 0.3509   |
  | 1.098         | 0.08  | 1000 | 1.0988          | 0.3344   |
  | 1.083         | 0.12  | 1500 | 1.0572          | 0.4546   |
  | 1.0527        | 0.16  | 2000 | 1.0380          | 0.4709   |
  | 1.0447        | 0.2   | 2500 | 1.0279          | 0.4759   |
  | 1.0343        | 0.24  | 3000 | 1.0261          | 0.4724   |
  | 1.0266        | 0.29  | 3500 | 1.0158          | 0.4808   |
  | 1.0199        | 0.33  | 4000 | 1.0120          | 0.4841   |
  | 1.0141        | 0.37  | 4500 | 1.0022          | 0.4924   |
  | 1.0051        | 0.41  | 5000 | 0.9980          | 0.5011   |
  
  
  ### Framework versions
  
  - Transformers 4.27.0.dev0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.1.dev0
  - Tokenizers 0.13.2
  