readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: gpt2-ner-invoiceSenderRecipient_all_inv_03_01
    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. -->
  
  # gpt2-ner-invoiceSenderRecipient_all_inv_03_01
  
  This model was trained from scratch on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0307
  - Precision: 0.7932
  - Recall: 0.8488
  - F1: 0.8201
  - Accuracy: 0.9895
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0363        | 0.01  | 500   | 0.0338          | 0.7846    | 0.7969 | 0.7907 | 0.9884   |
  | 0.0392        | 0.02  | 1000  | 0.0346          | 0.7665    | 0.8211 | 0.7929 | 0.9881   |
  | 0.0363        | 0.04  | 1500  | 0.0347          | 0.7701    | 0.8075 | 0.7884 | 0.9880   |
  | 0.0396        | 0.05  | 2000  | 0.0347          | 0.7454    | 0.8375 | 0.7888 | 0.9879   |
  | 0.0366        | 0.06  | 2500  | 0.0350          | 0.7519    | 0.8345 | 0.7911 | 0.9879   |
  | 0.0382        | 0.07  | 3000  | 0.0356          | 0.7500    | 0.8434 | 0.7939 | 0.9877   |
  | 0.0424        | 0.09  | 3500  | 0.0358          | 0.7517    | 0.8287 | 0.7883 | 0.9877   |
  | 0.0385        | 0.1   | 4000  | 0.0352          | 0.7605    | 0.8225 | 0.7903 | 0.9880   |
  | 0.0382        | 0.11  | 4500  | 0.0361          | 0.7494    | 0.8159 | 0.7813 | 0.9874   |
  | 0.0372        | 0.12  | 5000  | 0.0345          | 0.7817    | 0.8044 | 0.7929 | 0.9885   |
  | 0.0377        | 0.14  | 5500  | 0.0346          | 0.7749    | 0.8238 | 0.7986 | 0.9884   |
  | 0.0383        | 0.15  | 6000  | 0.0359          | 0.7568    | 0.8341 | 0.7936 | 0.9879   |
  | 0.0372        | 0.16  | 6500  | 0.0356          | 0.7548    | 0.8356 | 0.7932 | 0.9879   |
  | 0.0371        | 0.17  | 7000  | 0.0352          | 0.7540    | 0.8477 | 0.7981 | 0.9880   |
  | 0.0368        | 0.19  | 7500  | 0.0349          | 0.7662    | 0.8310 | 0.7973 | 0.9881   |
  | 0.0388        | 0.2   | 8000  | 0.0339          | 0.7648    | 0.8336 | 0.7977 | 0.9883   |
  | 0.0368        | 0.21  | 8500  | 0.0336          | 0.7729    | 0.8305 | 0.8006 | 0.9886   |
  | 0.0389        | 0.22  | 9000  | 0.0340          | 0.7750    | 0.8208 | 0.7972 | 0.9884   |
  | 0.0384        | 0.24  | 9500  | 0.0349          | 0.7549    | 0.8499 | 0.7996 | 0.9880   |
  | 0.0376        | 0.25  | 10000 | 0.0358          | 0.7531    | 0.8390 | 0.7938 | 0.9875   |
  | 0.0354        | 0.26  | 10500 | 0.0346          | 0.7650    | 0.8318 | 0.7970 | 0.9882   |
  | 0.0358        | 0.27  | 11000 | 0.0338          | 0.7694    | 0.8397 | 0.8030 | 0.9886   |
  | 0.0389        | 0.28  | 11500 | 0.0341          | 0.7586    | 0.8502 | 0.8018 | 0.9882   |
  | 0.0383        | 0.3   | 12000 | 0.0342          | 0.7688    | 0.8275 | 0.7971 | 0.9881   |
  | 0.0355        | 0.31  | 12500 | 0.0337          | 0.7783    | 0.8281 | 0.8024 | 0.9885   |
  | 0.0372        | 0.32  | 13000 | 0.0338          | 0.7703    | 0.8399 | 0.8036 | 0.9884   |
  | 0.0369        | 0.33  | 13500 | 0.0331          | 0.7683    | 0.8427 | 0.8038 | 0.9886   |
  | 0.0361        | 0.35  | 14000 | 0.0336          | 0.7699    | 0.8322 | 0.7999 | 0.9885   |
  | 0.0361        | 0.36  | 14500 | 0.0336          | 0.7735    | 0.8390 | 0.8049 | 0.9885   |
  | 0.0372        | 0.37  | 15000 | 0.0333          | 0.7747    | 0.8343 | 0.8034 | 0.9887   |
  | 0.0366        | 0.38  | 15500 | 0.0343          | 0.7646    | 0.8468 | 0.8036 | 0.9883   |
  | 0.0345        | 0.4   | 16000 | 0.0333          | 0.7790    | 0.8334 | 0.8053 | 0.9887   |
  | 0.0363        | 0.41  | 16500 | 0.0329          | 0.7783    | 0.8301 | 0.8034 | 0.9887   |
  | 0.0348        | 0.42  | 17000 | 0.0341          | 0.7626    | 0.8533 | 0.8054 | 0.9884   |
  | 0.0391        | 0.43  | 17500 | 0.0324          | 0.7873    | 0.8295 | 0.8079 | 0.9889   |
  | 0.0344        | 0.45  | 18000 | 0.0334          | 0.7769    | 0.8369 | 0.8058 | 0.9887   |
  | 0.0378        | 0.46  | 18500 | 0.0337          | 0.7741    | 0.8394 | 0.8054 | 0.9886   |
  | 0.035         | 0.47  | 19000 | 0.0328          | 0.7827    | 0.8323 | 0.8067 | 0.9888   |
  | 0.0351        | 0.48  | 19500 | 0.0327          | 0.7815    | 0.8371 | 0.8083 | 0.9889   |
  | 0.037         | 0.5   | 20000 | 0.0328          | 0.7793    | 0.83