readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: aces-roberta-base-reduced
    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. -->
  
  # aces-roberta-base-reduced
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3101
  - Precision: 0.9036
  - Recall: 0.9038
  - F1: 0.9029
  - Accuracy: 0.9038
  - F1 Who: 0.8727
  - F1 What: 0.8295
  - F1 Where: 0.8468
  - F1 How: 0.9414
  
  ## 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 | F1 Who | F1 What | F1 Where | F1 How |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
  | 0.6299        | 1.0   | 48   | 0.3723          | 0.8943    | 0.8946 | 0.8939 | 0.8946   | 0.8846 | 0.8179  | 0.8559   | 0.9208 |
  | 0.3067        | 2.0   | 96   | 0.3481          | 0.8911    | 0.8803 | 0.8820 | 0.8803   | 0.8649 | 0.8102  | 0.7766   | 0.9365 |
  | 0.2054        | 3.0   | 144  | 0.3018          | 0.9129    | 0.9121 | 0.9117 | 0.9121   | 0.8649 | 0.8571  | 0.8720   | 0.9430 |
  | 0.2196        | 4.0   | 192  | 0.3061          | 0.9108    | 0.9105 | 0.9098 | 0.9105   | 0.8649 | 0.8385  | 0.8610   | 0.9508 |
  | 0.1505        | 5.0   | 240  | 0.3101          | 0.9036    | 0.9038 | 0.9029 | 0.9038   | 0.8727 | 0.8295  | 0.8468   | 0.9414 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  