readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
    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. -->
  
  # distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0245
  - Precision: 0.8602
  - Recall: 0.9015
  - F1: 0.8804
  - Accuracy: 0.9921
  
  ## 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: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0157        | 0.06  | 500   | 0.0286          | 0.8569    | 0.8605 | 0.8587 | 0.9908   |
  | 0.0156        | 0.11  | 1000  | 0.0287          | 0.8340    | 0.8940 | 0.8630 | 0.9910   |
  | 0.0141        | 0.17  | 1500  | 0.0296          | 0.8368    | 0.8853 | 0.8604 | 0.9908   |
  | 0.014         | 0.23  | 2000  | 0.0296          | 0.8356    | 0.8915 | 0.8627 | 0.9910   |
  | 0.0144        | 0.28  | 2500  | 0.0306          | 0.8310    | 0.8896 | 0.8593 | 0.9906   |
  | 0.0136        | 0.34  | 3000  | 0.0290          | 0.8384    | 0.8842 | 0.8607 | 0.9910   |
  | 0.0134        | 0.4   | 3500  | 0.0306          | 0.8514    | 0.8779 | 0.8645 | 0.9912   |
  | 0.0138        | 0.46  | 4000  | 0.0307          | 0.8475    | 0.8790 | 0.8630 | 0.9910   |
  | 0.0139        | 0.51  | 4500  | 0.0301          | 0.8208    | 0.9002 | 0.8587 | 0.9908   |
  | 0.014         | 0.57  | 5000  | 0.0320          | 0.8307    | 0.8981 | 0.8631 | 0.9909   |
  | 0.0155        | 0.63  | 5500  | 0.0307          | 0.8329    | 0.8992 | 0.8648 | 0.9909   |
  | 0.0154        | 0.68  | 6000  | 0.0268          | 0.8403    | 0.8971 | 0.8677 | 0.9913   |
  | 0.0178        | 0.74  | 6500  | 0.0269          | 0.8548    | 0.8869 | 0.8705 | 0.9916   |
  | 0.0177        | 0.8   | 7000  | 0.0268          | 0.8552    | 0.8904 | 0.8725 | 0.9917   |
  | 0.0178        | 0.85  | 7500  | 0.0267          | 0.8498    | 0.8972 | 0.8729 | 0.9917   |
  | 0.017         | 0.91  | 8000  | 0.0259          | 0.8517    | 0.8969 | 0.8737 | 0.9917   |
  | 0.0176        | 0.97  | 8500  | 0.0249          | 0.8523    | 0.8921 | 0.8717 | 0.9916   |
  | 0.0157        | 1.02  | 9000  | 0.0274          | 0.8535    | 0.8990 | 0.8757 | 0.9918   |
  | 0.0134        | 1.08  | 9500  | 0.0293          | 0.8375    | 0.9060 | 0.8704 | 0.9913   |
  | 0.0132        | 1.14  | 10000 | 0.0278          | 0.8648    | 0.8864 | 0.8755 | 0.9919   |
  | 0.0135        | 1.19  | 10500 | 0.0273          | 0.8540    | 0.8958 | 0.8744 | 0.9917   |
  | 0.0133        | 1.25  | 11000 | 0.0277          | 0.8442    | 0.9034 | 0.8728 | 0.9917   |
  | 0.0138        | 1.31  | 11500 | 0.0276          | 0.8484    | 0.9035 | 0.8751 | 0.9917   |
  | 0.0141        | 1.37  | 12000 | 0.0274          | 0.8501    | 0.9016 | 0.8751 | 0.9918   |
  | 0.0137        | 1.42  | 12500 | 0.0274          | 0.8529    | 0.9010 | 0.8763 | 0.9918   |
  | 0.014         | 1.48  | 13000 | 0.0269          | 0.8509    | 0.9022 | 0.8758 | 0.9919   |
  | 0.0141        | 1.54  | 13500 | 0.0260          | 0.8653    | 0.8926 | 0.8787 | 0.9920   |
  | 0.0149        | 1.59  | 14000 | 0.0258          | 0.8521    | 0.9048 | 0.8777 | 0.9919   |
  | 0.0149        | 1.65  | 14500 | 0.0257          | 0.8607    | 0.8980 | 0.8790 | 0.9921   |
  | 0.0152        | 1.71  | 15000 | 0.0257          | 0.8596    | 0.9001 | 0.8794 | 0.9920   |
  | 0.015         | 1.76  | 15500 | 0.0257          | 0.8556    | 0.9032 | 0.8788 | 0.9920   |
  | 0.0157        | 1.82  | 16000 | 0.0248          | 0.8620    | 0.8993 | 0.8802 | 0.9922   |
  | 0.0158        | 1.88  | 16500 | 0.0251          | 0.8573    | 0.9036 | 0.8798 | 0.9921   |
  | 0.0163        | 1.93  | 17000 | 0.0248          | 0.8579    | 0.9034 | 0.8800 | 0.9921   |
  | 0.017         | 1.99  | 17500 | 0.0245          | 0.8602    | 0.9015 | 0.8804 | 0.9921   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0
  - Datasets 2.3.2
  - Tokenizers 0.10.3
  