readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BERT_NER_Ep6_PAD_50-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_NER_Ep6_PAD_50-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3741
  - Precision: 0.6510
  - Recall: 0.7399
  - F1: 0.6926
  - Accuracy: 0.9020
  
  ## 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: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 288  | 0.3648          | 0.5949    | 0.5907 | 0.5928 | 0.8792   |
  | 0.4815        | 2.0   | 576  | 0.3400          | 0.5860    | 0.7390 | 0.6536 | 0.8867   |
  | 0.4815        | 3.0   | 864  | 0.3217          | 0.6404    | 0.7129 | 0.6747 | 0.8992   |
  | 0.2206        | 4.0   | 1152 | 0.3430          | 0.6413    | 0.7321 | 0.6837 | 0.8995   |
  | 0.2206        | 5.0   | 1440 | 0.3560          | 0.6464    | 0.7377 | 0.6890 | 0.9010   |
  | 0.1487        | 6.0   | 1728 | 0.3741          | 0.6510    | 0.7399 | 0.6926 | 0.9020   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  