readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-greek-uncased-v1-finetuned-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. -->
  
  # bert-base-greek-uncased-v1-finetuned-ner
  
  This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1052
  - Precision: 0.8440
  - Recall: 0.8566
  - F1: 0.8503
  - Accuracy: 0.9768
  
  ## 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 | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.64  | 250  | 0.0913          | 0.7814    | 0.8208 | 0.8073 | 0.9728   |
  | 0.1144        | 1.29  | 500  | 0.0823          | 0.7940    | 0.8448 | 0.8342 | 0.9755   |
  | 0.1144        | 1.93  | 750  | 0.0812          | 0.8057    | 0.8212 | 0.8328 | 0.9751   |
  | 0.0570        | 2.58  | 1000 | 0.0855          | 0.8244    | 0.8514 | 0.8292 | 0.9744   |
  | 0.0570        | 3.22  | 1250 | 0.0926          | 0.8329    | 0.8441 | 0.8397 | 0.9760   |
  | 0.0393        | 3.87  | 1500 | 0.0869          | 0.8256  	| 0.8633 | 0.8440 | 0.9774   |
  | 0.0393        | 4.51  | 1750 | 0.1049          | 0.8290    | 0.8636 | 0.8459 | 0.9766   |
  | 0.026         | 5.15  | 2000 | 0.1093          | 0.8440    | 0.8566 | 0.8503 | 0.9768   |
  | 0.026         | 5.8   | 2250 | 0.1172          | 0.8301    | 0.8514 | 0.8406 | 0.9760   |
  | 0.0189        | 6.44  | 2500 | 0.1273          | 0.8238    | 0.8688 | 0.8457 | 0.9766   |
  | 0.0189        | 7.09  | 2750 | 0.1246          | 0.8350    | 0.8539 | 0.8443 | 0.9764   |
  | 0.0148        | 7.73  | 3000 | 0.1262          | 0.8333    | 0.8608 | 0.8468 | 0.9764   |
  | 0.0148        | 8.38  | 3250 | 0.1347          | 0.8319    | 0.8591 | 0.8453 | 0.9762   |
  | 0.0010        | 9.02  | 3500 | 0.1325          | 0.8376    | 0.8504 | 0.8439 | 0.9766   |
  | 0.0010        | 9.66  | 3750 | 0.1362          | 0.8371    | 0.8563 | 0.8466 | 0.9765   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  