readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-ner-TRANS
    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-finetuned-ner-TRANS
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1053
  - Precision: 0.7911
  - Recall: 0.8114
  - F1: 0.8011
  - Accuracy: 0.9815
  
  ## 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: 12
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.077         | 1.0   | 3762  | 0.0724          | 0.7096    | 0.7472 | 0.7279 | 0.9741   |
  | 0.0538        | 2.0   | 7524  | 0.0652          | 0.7308    | 0.7687 | 0.7493 | 0.9766   |
  | 0.0412        | 3.0   | 11286 | 0.0643          | 0.7672    | 0.7875 | 0.7772 | 0.9788   |
  | 0.0315        | 4.0   | 15048 | 0.0735          | 0.7646    | 0.7966 | 0.7803 | 0.9793   |
  | 0.0249        | 5.0   | 18810 | 0.0772          | 0.7805    | 0.7981 | 0.7892 | 0.9801   |
  | 0.0213        | 6.0   | 22572 | 0.0783          | 0.7829    | 0.8063 | 0.7944 | 0.9805   |
  | 0.0187        | 7.0   | 26334 | 0.0858          | 0.7821    | 0.8010 | 0.7914 | 0.9809   |
  | 0.0157        | 8.0   | 30096 | 0.0860          | 0.7837    | 0.8120 | 0.7976 | 0.9812   |
  | 0.0122        | 9.0   | 33858 | 0.0963          | 0.7857    | 0.8129 | 0.7990 | 0.9813   |
  | 0.0107        | 10.0  | 37620 | 0.0993          | 0.7934    | 0.8089 | 0.8010 | 0.9812   |
  | 0.0091        | 11.0  | 41382 | 0.1031          | 0.7882    | 0.8123 | 0.8001 | 0.9814   |
  | 0.0083        | 12.0  | 45144 | 0.1053          | 0.7911    | 0.8114 | 0.8011 | 0.9815   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.1
  - Datasets 2.0.0
  - Tokenizers 0.10.3
  