readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: mdeberta-v3-base-finetuned-pos
    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-pos
  
  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: 0.0887
  - Acc: 0.9814
  - F1: 0.8861
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Acc    | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | No log        | 1.0   | 439  | 0.0965          | 0.9749 | 0.8471 |
  | 0.3317        | 2.0   | 878  | 0.0815          | 0.9783 | 0.8702 |
  | 0.0775        | 3.0   | 1317 | 0.0780          | 0.9812 | 0.8825 |
  | 0.0568        | 4.0   | 1756 | 0.0769          | 0.9809 | 0.8827 |
  | 0.0444        | 5.0   | 2195 | 0.0799          | 0.9811 | 0.8885 |
  | 0.0339        | 6.0   | 2634 | 0.0834          | 0.9813 | 0.8821 |
  | 0.0278        | 7.0   | 3073 | 0.0845          | 0.9817 | 0.8843 |
  | 0.0222        | 8.0   | 3512 | 0.0866          | 0.9814 | 0.8863 |
  | 0.0222        | 9.0   | 3951 | 0.0885          | 0.9814 | 0.8862 |
  | 0.0188        | 10.0  | 4390 | 0.0887          | 0.9814 | 0.8861 |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  