readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-small-finetuned-ner-to-multilabel-finer-139
    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-small-finetuned-ner-to-multilabel-finer-139
  
  This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0019
  
  ## 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: 3e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: constant
  - num_epochs: 40
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.1398        | 0.0   | 500   | 0.0244          |
  | 0.0164        | 0.01  | 1000  | 0.0114          |
  | 0.01          | 0.01  | 1500  | 0.0084          |
  | 0.0081        | 0.02  | 2000  | 0.0073          |
  | 0.0072        | 0.02  | 2500  | 0.0068          |
  | 0.0069        | 0.03  | 3000  | 0.0065          |
  | 0.0067        | 0.03  | 3500  | 0.0063          |
  | 0.0066        | 0.04  | 4000  | 0.0062          |
  | 0.0061        | 0.04  | 4500  | 0.0062          |
  | 0.0069        | 0.04  | 5000  | 0.0061          |
  | 0.0063        | 0.05  | 5500  | 0.0061          |
  | 0.0062        | 0.05  | 6000  | 0.0061          |
  | 0.006         | 0.06  | 6500  | 0.0061          |
  | 0.0059        | 0.06  | 7000  | 0.0056          |
  | 0.0058        | 0.07  | 7500  | 0.0054          |
  | 0.0054        | 0.07  | 8000  | 0.0054          |
  | 0.0057        | 0.08  | 8500  | 0.0053          |
  | 0.0057        | 0.08  | 9000  | 0.0052          |
  | 0.0056        | 0.08  | 9500  | 0.0051          |
  | 0.0051        | 0.09  | 10000 | 0.0050          |
  | 0.0054        | 0.09  | 10500 | 0.0049          |
  | 0.005         | 0.1   | 11000 | 0.0048          |
  | 0.0049        | 0.1   | 11500 | 0.0046          |
  | 0.0049        | 0.11  | 12000 | 0.0046          |
  | 0.0046        | 0.11  | 12500 | 0.0044          |
  | 0.0043        | 0.12  | 13000 | 0.0043          |
  | 0.0045        | 0.12  | 13500 | 0.0042          |
  | 0.0042        | 0.12  | 14000 | 0.0042          |
  | 0.0042        | 0.13  | 14500 | 0.0039          |
  | 0.0042        | 0.13  | 15000 | 0.0038          |
  | 0.0039        | 0.14  | 15500 | 0.0037          |
  | 0.004         | 0.14  | 16000 | 0.0036          |
  | 0.0037        | 0.15  | 16500 | 0.0035          |
  | 0.0036        | 0.15  | 17000 | 0.0035          |
  | 0.0036        | 0.16  | 17500 | 0.0035          |
  | 0.0035        | 0.16  | 18000 | 0.0033          |
  | 0.0037        | 0.16  | 18500 | 0.0033          |
  | 0.0035        | 0.17  | 19000 | 0.0032          |
  | 0.0032        | 0.17  | 19500 | 0.0031          |
  | 0.0032        | 0.18  | 20000 | 0.0031          |
  | 0.0033        | 0.18  | 20500 | 0.0030          |
  | 0.003         | 0.19  | 21000 | 0.0030          |
  | 0.0034        | 0.19  | 21500 | 0.0029          |
  | 0.0031        | 0.2   | 22000 | 0.0029          |
  | 0.003         | 0.2   | 22500 | 0.0028          |
  | 0.0032        | 0.2   | 23000 | 0.0028          |
  | 0.003         | 0.21  | 23500 | 0.0027          |
  | 0.0029        | 0.21  | 24000 | 0.0027          |
  | 0.0027        | 0.22  | 24500 | 0.0026          |
  | 0.0029        | 0.22  | 25000 | 0.0026          |
  | 0.0027        | 0.23  | 25500 | 0.0026          |
  | 0.0028        | 0.23  | 26000 | 0.0026          |
  | 0.0027        | 0.24  | 26500 | 0.0025          |
  | 0.0026        | 0.24  | 27000 | 0.0025          |
  | 0.0026        | 0.24  | 27500 | 0.0025          |
  | 0.0026        | 0.25  | 28000 | 0.0024          |
  | 0.0025        | 0.25  | 28500 | 0.0024          |
  | 0.0026        | 0.26  | 29000 | 0.0024          |
  | 0.0025        | 0.26  | 29500 | 0.0024          |
  | 0.0024        | 0.27  | 30000 | 0.0024          |
  | 0.0026        | 0.27  | 30500 | 0.0023          |
  | 0.0024        | 0.28  | 31000 | 0.0023          |
  | 0.0025        | 0.28  | 31500 | 0.0023          |
  | 0.0024        | 0.28  | 32000 | 0.0023          |
  | 0.0023        | 0.29  | 32500 | 0.0022          |
  | 0.0024        | 0.29  | 33000 | 0.0022          |
  | 0.0024        | 0.3   | 33500 | 0.0022          |
  | 0.0022        | 0.3   | 34000 | 0.0022          |
  | 0.0023        | 0.31  | 34500 | 0.0021          |
  | 0.0023        | 0.31  | 35000 | 0.0021          |
  | 0.0024        | 0.32  | 35500 | 0.0021          |
  | 0.0023        | 0.32  | 36000 | 0.0021          |
  | 0.0023        | 0.32  | 36500 | 0.0021          |
  | 0.0021    