readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-protagonist-english
    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-protagonist-english
  
  This model is a fine-tuned version of [Jean-Baptiste/roberta-large-ner-english](https://huggingface.co/Jean-Baptiste/roberta-large-ner-english) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0630
  - Precision: 0.8646
  - Recall: 0.8839
  - F1: 0.8742
  - Accuracy: 0.9876
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 25   | 0.0659          | 0.8860    | 0.9018 | 0.8938 | 0.9862   |
  | No log        | 2.0   | 50   | 0.0583          | 0.8553    | 0.8705 | 0.8628 | 0.9860   |
  | No log        | 3.0   | 75   | 0.0593          | 0.8728    | 0.8884 | 0.8805 | 0.9876   |
  | No log        | 4.0   | 100  | 0.0622          | 0.8559    | 0.875  | 0.8653 | 0.9871   |
  | No log        | 5.0   | 125  | 0.0630          | 0.8646    | 0.8839 | 0.8742 | 0.9876   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu102
  - Datasets 2.2.1
  - Tokenizers 0.11.0
  