readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-uncased-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-base-uncased-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: 0.0615
  - Precision: 0.9222
  - Recall: 0.9372
  - F1: 0.9297
  - Accuracy: 0.9838
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - distributed_type: IPU
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 16
  - total_eval_batch_size: 5
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - training precision: Mixed Precision
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.051         | 1.0   | 877  | 0.0667          | 0.9090    | 0.9190 | 0.9139 | 0.9811   |
  | 0.2483        | 2.0   | 1754 | 0.0600          | 0.9295    | 0.9344 | 0.9320 | 0.9839   |
  | 0.0153        | 3.0   | 2631 | 0.0615          | 0.9222    | 0.9372 | 0.9297 | 0.9838   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.10.0+cpu
  - Datasets 2.7.1
  - Tokenizers 0.12.1
  