readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-uncased-finetuned-ner-kmeans
    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. -->
  
  # bert-base-uncased-finetuned-ner-kmeans
  
  This model is a fine-tuned version of [ArBert/bert-base-uncased-finetuned-ner](https://huggingface.co/ArBert/bert-base-uncased-finetuned-ner) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1169
  - Precision: 0.9084
  - Recall: 0.9245
  - F1: 0.9164
  - Accuracy: 0.9792
  
  ## 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.036         | 1.0   | 1123 | 0.1010          | 0.9086    | 0.9117 | 0.9101 | 0.9779   |
  | 0.0214        | 2.0   | 2246 | 0.1094          | 0.9033    | 0.9199 | 0.9115 | 0.9784   |
  | 0.014         | 3.0   | 3369 | 0.1169          | 0.9084    | 0.9245 | 0.9164 | 0.9792   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  