readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: mDeBERTa-mnli-kaggle-contradictory-my-dear-watson
    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. -->
  
  # mDeBERTa-mnli-kaggle-contradictory-my-dear-watson
  
  This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3225
  - Accuracy: 0.8890
  
  ## 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: 1e-06
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4778        | 1.0   | 606  | 0.3361          | 0.8899   |
  | 0.3766        | 2.0   | 1212 | 0.3217          | 0.8899   |
  | 0.344         | 3.0   | 1818 | 0.3218          | 0.8903   |
  | 0.3349        | 4.0   | 2424 | 0.3222          | 0.8894   |
  | 0.3263        | 5.0   | 3030 | 0.3225          | 0.8890   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  