readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: roberta-large-aces
    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. -->
  
  # roberta-large-aces
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5257
  - Precision: 0.8561
  - Recall: 0.8594
  - F1: 0.8553
  - Accuracy: 0.8594
  - F1 Who: 0.8494
  - F1 What: 0.8391
  - F1 Where: 0.7558
  - F1 How: 0.9208
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
  | 0.4619        | 1.0   | 87   | 0.5447          | 0.8247    | 0.8416 | 0.8308 | 0.8416   | 0.8309 | 0.8188  | 0.6973   | 0.9244 |
  | 0.4358        | 2.0   | 174  | 0.4662          | 0.8522    | 0.8571 | 0.8517 | 0.8571   | 0.8314 | 0.8446  | 0.7613   | 0.9238 |
  | 0.3793        | 3.0   | 261  | 0.4892          | 0.8507    | 0.8622 | 0.8556 | 0.8622   | 0.8321 | 0.8418  | 0.7725   | 0.9280 |
  | 0.2875        | 4.0   | 348  | 0.5034          | 0.8702    | 0.8641 | 0.8593 | 0.8641   | 0.8471 | 0.8441  | 0.7715   | 0.9225 |
  | 0.1847        | 5.0   | 435  | 0.5257          | 0.8561    | 0.8594 | 0.8553 | 0.8594   | 0.8494 | 0.8391  | 0.7558   | 0.9208 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  