readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BERT_NER_Ep5_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_Ep5_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: 0.3893
  - Precision: 0.6540
  - Recall: 0.7348
  - F1: 0.6920
  - Accuracy: 0.9006
  
  ## 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: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 288  | 0.3705          | 0.5852    | 0.6215 | 0.6028 | 0.8793   |
  | 0.4885        | 2.0   | 576  | 0.3351          | 0.5925    | 0.7317 | 0.6548 | 0.8865   |
  | 0.4885        | 3.0   | 864  | 0.3196          | 0.6471    | 0.7138 | 0.6788 | 0.8994   |
  | 0.2172        | 4.0   | 1152 | 0.3368          | 0.6454    | 0.7323 | 0.6861 | 0.8992   |
  | 0.2172        | 5.0   | 1440 | 0.3491          | 0.6507    | 0.7312 | 0.6886 | 0.9008   |
  | 0.1459        | 6.0   | 1728 | 0.3833          | 0.6715    | 0.7018 | 0.6863 | 0.9013   |
  | 0.1045        | 7.0   | 2016 | 0.3893          | 0.6540    | 0.7348 | 0.6920 | 0.9006   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  