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---
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
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