readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: greek_legal_bert_v2-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. -->
  
  # greek_legal_bert_v2-finetuned-ner
  
  This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0900
  - Precision: 0.8424
  - Recall: 0.8638
  - F1: 0.8530
  - Accuracy: 0.9775
  
  ## 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  | 0.0839          | 0.7859    | 0.8539 | 0.8185 | 0.9737   |
  | 0.1127        | 1.29  | 500  | 0.0783          | 0.8092    | 0.8569 | 0.8324 | 0.9759   |
  | 0.1127        | 1.93  | 750  | 0.0743          | 0.8284    | 0.8446 | 0.8364 | 0.9766   |
  | 0.0538        | 2.58  | 1000 | 0.0816          | 0.8243    | 0.8597 | 0.8416 | 0.9774   |
  | 0.0538        | 3.22  | 1250 | 0.0900          | 0.8424    | 0.8638 | 0.8530 | 0.9776   |
  | 0.0346        | 3.87  | 1500 | 0.0890          | 0.8401    | 0.8597 | 0.8498 | 0.9770   |
  | 0.0346        | 4.51  | 1750 | 0.0964          | 0.8342    | 0.8576 | 0.8457 | 0.9768   |
  | 0.0233        | 5.15  | 2000 | 0.1094          | 0.8336    | 0.8645 | 0.8488 | 0.9768   |
  | 0.0233        | 5.8   | 2250 | 0.1110          | 0.8456    | 0.8549 | 0.8502 | 0.9777   |
  | 0.0161        | 6.44  | 2500 | 0.1224          | 0.8408    | 0.8535 | 0.8471 | 0.9769   |
  | 0.0161        | 7.09  | 2750 | 0.1281          | 0.8347    | 0.8624 | 0.8483 | 0.9770   |
  | 0.0114        | 7.73  | 3000 | 0.1268          | 0.8397    | 0.8573 | 0.8484 | 0.9773   |
  | 0.0114        | 8.38  | 3250 | 0.1308          | 0.8388    | 0.8549 | 0.8468 | 0.9771   |
  | 0.0088        | 9.02  | 3500 | 0.1301          | 0.8412    | 0.8559 | 0.8485 | 0.9772   |
  | 0.0088        | 9.66  | 3750 | 0.1368          | 0.8396    | 0.8604 | 0.8499 | 0.9772   |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  