readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: mdeberta-v3-base-finetuned-recores
    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-v3-base-finetuned-recores
  
  This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6094
  - Accuracy: 0.2011
  
  ## 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: 2e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 3000
  - num_epochs: 25
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 1.6112        | 1.0   | 1047  | 1.6094          | 0.1901   |
  | 1.608         | 2.0   | 2094  | 1.6094          | 0.1873   |
  | 1.6127        | 3.0   | 3141  | 1.6095          | 0.1983   |
  | 1.6125        | 4.0   | 4188  | 1.6094          | 0.2424   |
  | 1.6118        | 5.0   | 5235  | 1.6094          | 0.1956   |
  | 1.6181        | 6.0   | 6282  | 1.6094          | 0.2094   |
  | 1.6229        | 7.0   | 7329  | 1.6095          | 0.1680   |
  | 1.6125        | 8.0   | 8376  | 1.6094          | 0.1736   |
  | 1.6134        | 9.0   | 9423  | 1.6094          | 0.2066   |
  | 1.6174        | 10.0  | 10470 | 1.6093          | 0.2204   |
  | 1.6161        | 11.0  | 11517 | 1.6096          | 0.2121   |
  | 1.6198        | 12.0  | 12564 | 1.6094          | 0.2039   |
  | 1.6182        | 13.0  | 13611 | 1.6094          | 0.2287   |
  | 1.6208        | 14.0  | 14658 | 1.6094          | 0.2287   |
  | 1.6436        | 15.0  | 15705 | 1.6092          | 0.2287   |
  | 1.6209        | 16.0  | 16752 | 1.6094          | 0.2094   |
  | 1.6097        | 17.0  | 17799 | 1.6094          | 0.2094   |
  | 1.6115        | 18.0  | 18846 | 1.6094          | 0.2149   |
  | 1.6249        | 19.0  | 19893 | 1.6094          | 0.1956   |
  | 1.6201        | 20.0  | 20940 | 1.6094          | 0.1763   |
  | 1.6217        | 21.0  | 21987 | 1.6094          | 0.1956   |
  | 1.6193        | 22.0  | 23034 | 1.6094          | 0.1846   |
  | 1.6171        | 23.0  | 24081 | 1.6095          | 0.1983   |
  | 1.6123        | 24.0  | 25128 | 1.6095          | 0.1846   |
  | 1.6164        | 25.0  | 26175 | 1.6094          | 0.2011   |
  
  
  ### Framework versions
  
  - Transformers 4.19.0
  - Pytorch 1.10.1+cu102
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  