readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  model-index:
  - name: bert-finetuned-ner
    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-finetuned-ner
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0589
  - Overall Precision: 0.9362
  - Overall Recall: 0.9500
  - Overall F1: 0.9430
  - Overall Accuracy: 0.9873
  - Loc F1: 0.9616
  - Misc F1: 0.8783
  - Org F1: 0.9121
  - Per F1: 0.9797
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Misc F1 | Org F1 | Per F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:-------:|:------:|:------:|
  | 0.0745        | 1.0   | 1756 | 0.0556          | 0.9183            | 0.9345         | 0.9263     | 0.9848           | 0.9501 | 0.8499  | 0.8775 | 0.9765 |
  | 0.0321        | 2.0   | 3512 | 0.0542          | 0.9346            | 0.9475         | 0.9410     | 0.9872           | 0.9618 | 0.8761  | 0.9073 | 0.9773 |
  | 0.0172        | 3.0   | 5268 | 0.0589          | 0.9362            | 0.9500         | 0.9430     | 0.9873           | 0.9616 | 0.8783  | 0.9121 | 0.9797 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  