readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: aces-roberta-base
    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
  
  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: 0.5792
  - Precision: 0.8165
  - Recall: 0.8393
  - F1: 0.8267
  - Accuracy: 0.8393
  - F1 Who: 0.8596
  - F1 What: 0.8556
  - F1 Where: 0.8497
  - F1 How: 0.9371
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------:|:-------:|:--------:|:------:|
  | 0.9477        | 1.0   | 48   | 0.7611          | 0.7340    | 0.7941 | 0.7498 | 0.7941   | 0.8624 | 0.8184  | 0.7453   | 0.8823 |
  | 0.5627        | 2.0   | 96   | 0.6313          | 0.8049    | 0.8151 | 0.8038 | 0.8151   | 0.8496 | 0.8310  | 0.7660   | 0.9374 |
  | 0.4324        | 3.0   | 144  | 0.5742          | 0.8168    | 0.8402 | 0.8268 | 0.8402   | 0.8596 | 0.8525  | 0.8542   | 0.9342 |
  | 0.4351        | 4.0   | 192  | 0.5794          | 0.8168    | 0.8402 | 0.8265 | 0.8402   | 0.8522 | 0.8556  | 0.8615   | 0.9430 |
  | 0.3059        | 5.0   | 240  | 0.5792          | 0.8165    | 0.8393 | 0.8267 | 0.8393   | 0.8596 | 0.8556  | 0.8497   | 0.9371 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  