readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: cv-ner
    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. -->
  
  # cv-ner
  
  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: 0.0956
  - Precision: 0.8906
  - Recall: 0.9325
  - F1: 0.9111
  - Accuracy: 0.9851
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 91   | 0.2049          | 0.6618    | 0.7362 | 0.6970 | 0.9534   |
  | 0.5036        | 2.0   | 182  | 0.1156          | 0.7873    | 0.8630 | 0.8234 | 0.9722   |
  | 0.1442        | 3.0   | 273  | 0.1078          | 0.8262    | 0.9039 | 0.8633 | 0.9771   |
  | 0.0757        | 4.0   | 364  | 0.1179          | 0.8652    | 0.9059 | 0.8851 | 0.9780   |
  | 0.0526        | 5.0   | 455  | 0.0907          | 0.888     | 0.9080 | 0.8979 | 0.9837   |
  | 0.0342        | 6.0   | 546  | 0.0972          | 0.8926    | 0.9346 | 0.9131 | 0.9832   |
  | 0.0245        | 7.0   | 637  | 0.1064          | 0.8937    | 0.9284 | 0.9107 | 0.9834   |
  | 0.0188        | 8.0   | 728  | 0.0965          | 0.8980    | 0.9366 | 0.9169 | 0.9850   |
  | 0.0159        | 9.0   | 819  | 0.0999          | 0.91      | 0.9305 | 0.9201 | 0.9846   |
  | 0.0141        | 10.0  | 910  | 0.0956          | 0.8906    | 0.9325 | 0.9111 | 0.9851   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  