readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-ner-nlp
    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-nlp
  
  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: 0.0812
  - Precision: 0.8835
  - Recall: 0.9039
  - F1: 0.8936
  - Accuracy: 0.9804
  
  ## Model description
  ### Essential info about tagged entities
  - geo: Geographical Entity
  - gpe: Geopolitical Entity
  - tim: Time Indicator  
  ### Label description
  - Label 0: 'B-geo',
  - Label 1: 'B-gpe',
  - Label 2: 'B-tim',
  - Label 3: 'I-geo',
  - Label 4: 'I-gpe',
  - Label 5: 'I-tim',
  - Label 6: 'O'
  
  ## 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: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0384        | 1.0   | 1781 | 0.0671          | 0.8770    | 0.9038 | 0.8902 | 0.9799   |
  | 0.0295        | 2.0   | 3562 | 0.0723          | 0.8844    | 0.8989 | 0.8915 | 0.9804   |
  | 0.023         | 3.0   | 5343 | 0.0731          | 0.8787    | 0.9036 | 0.8910 | 0.9800   |
  | 0.0186        | 4.0   | 7124 | 0.0812          | 0.8835    | 0.9039 | 0.8936 | 0.9804   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  