readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - consumer-finance-complaints
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: distilbert-complaints-wandb-product
    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: 0.8690996641956535
      - name: F1
        type: f1
        value: 0.8645310918904254
      - name: Recall
        type: recall
        value: 0.8690996641956535
      - name: Precision
        type: precision
        value: 0.8629318199420283
  ---
  
  <!-- 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. -->
  
  # distilbert-complaints-wandb-product
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4431
  - Accuracy: 0.8691
  - F1: 0.8645
  - Recall: 0.8691
  - Precision: 0.8629
  
  ## 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: 5e-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: 500
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.562         | 0.51  | 2000  | 0.5107          | 0.8452   | 0.8346 | 0.8452 | 0.8252    |
  | 0.4548        | 1.01  | 4000  | 0.4628          | 0.8565   | 0.8481 | 0.8565 | 0.8466    |
  | 0.3439        | 1.52  | 6000  | 0.4519          | 0.8605   | 0.8544 | 0.8605 | 0.8545    |
  | 0.2626        | 2.03  | 8000  | 0.4412          | 0.8678   | 0.8618 | 0.8678 | 0.8626    |
  | 0.2717        | 2.53  | 10000 | 0.4431          | 0.8691   | 0.8645 | 0.8691 | 0.8629    |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  