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: 0.0712
  - Precision: 0.8945
  - Recall: 0.9182
  - F1: 0.9062
  - Accuracy: 0.9793
  
  ## 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: 64
  - total_eval_batch_size: 20
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1318        | 1.0   | 219  | 0.0967          | 0.8371    | 0.8714 | 0.8539 | 0.9705   |
  | 0.0597        | 2.0   | 438  | 0.0735          | 0.8912    | 0.9052 | 0.8981 | 0.9779   |
  | 0.0523        | 3.0   | 657  | 0.0712          | 0.8945    | 0.9182 | 0.9062 | 0.9793   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.10.0+cpu
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  