readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - consumer-finance-complaints
  metrics:
  - accuracy
  - f1
  - recall
  - precision
  model-index:
  - name: distilbert-base-uncased-wandb-week-3-complaints-classifier-256
    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: 0.8234544620559604
      - name: F1
        type: f1
        value: 0.8176243580045963
      - name: Recall
        type: recall
        value: 0.8234544620559604
      - name: Precision
        type: precision
        value: 0.8171438106054644
  ---
  
  <!-- 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-base-uncased-wandb-week-3-complaints-classifier-256
  
  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: 0.5453
  - Accuracy: 0.8235
  - F1: 0.8176
  - Recall: 0.8235
  - Precision: 0.8171
  
  ## 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: 4.097565552226687e-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: 256
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
  | 0.6691        | 0.61  | 1500 | 0.6475          | 0.7962   | 0.7818 | 0.7962 | 0.7875    |
  | 0.5361        | 1.22  | 3000 | 0.5794          | 0.8161   | 0.8080 | 0.8161 | 0.8112    |
  | 0.4659        | 1.83  | 4500 | 0.5453          | 0.8235   | 0.8176 | 0.8235 | 0.8171    |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  