Bank account or service Checking or savings account Consumer Loan Credit card Credit card or prepaid card Credit reporting Credit reporting, credit repair services, or other personal consumer reports Debt collection Money transfer, virtual currency, or money service Money transfers Mortgage Other financial service Payday loan Payday loan, title loan, or personal loan Prepaid card Student loan Vehicle loan or lease Virtual currency
---
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
should probably proofread and complete it, then remove this comment. -->

# 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
