readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - consumer-finance-complaints
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: distilroberta-base-wandb-week-3-complaints-classifier-1024
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: consumer-finance-complaints
        type: consumer-finance-complaints
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8279904184292339
      - name: F1
        type: f1
        value: 0.8236604095677945
      - name: Recall
        type: recall
        value: 0.8279904184292339
      - name: Precision
        type: precision
        value: 0.8235526237070518
  ---
  
  <!-- 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. -->
  
  # distilroberta-base-wandb-week-3-complaints-classifier-1024
  
  This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5351
  - Accuracy: 0.8280
  - F1: 0.8237
  - Recall: 0.8280
  - Precision: 0.8236
  
  ## 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: 9.027176214786854e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1024
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.7756        | 0.61  | 1500 | 0.7411          | 0.7647   | 0.7375 | 0.7647 | 0.7606    |
  | 0.5804        | 1.22  | 3000 | 0.6140          | 0.8088   | 0.8052 | 0.8088 | 0.8077    |
  | 0.5008        | 1.83  | 4500 | 0.5351          | 0.8280   | 0.8237 | 0.8280 | 0.8236    |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  