readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - consumer_complaints
  model-index:
  - name: distilbert-complaints-product
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # distilbert-complaints-product
  
  This model was trained from the [CFBP](https://www.consumerfinance.gov/data-research/consumer-complaints/) dataset, also made available on the HuggingFace Datasets library. This model predicts the type of financial complaint based on the text provided
  
  ## Model description
  
  A DistilBert Text Classification Model, with 18 possible classes to determine the nature of a financial customer complaint.
  
  ## Intended uses & limitations
  
  This model is used as part of.a demonstration for E2E Machine Learning Projects focused on Contact Centre Automation:  
  
  - **Infrastructure:** Terraform
  - **ML Ops:** HuggingFace (Datasets, Hub, Transformers) 
  - **Ml Explainability:** SHAP 
  - **Cloud:** AWS 
       - Model Hosting: Lambda 
       - DB Backend: DynamoDB
       - Orchestration: Step-Functions
       - UI Hosting: EC2
       - Routing: API Gateway 
  - **UI:** Budibase
  
  ## Training and evaluation data
  
  consumer_complaints dataset
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 64
  - 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
  
  ### Framework versions
  
  - Transformers 4.16.1
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.2
  - Tokenizers 0.11.0
  