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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- consumer_complaints |
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model-index: |
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- name: distilbert-complaints-product |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilbert-complaints-product |
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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 |
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## Model description |
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A DistilBert Text Classification Model, with 18 possible classes to determine the nature of a financial customer complaint. |
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## Intended uses & limitations |
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This model is used as part of.a demonstration for E2E Machine Learning Projects focused on Contact Centre Automation: |
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- **Infrastructure:** Terraform |
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- **ML Ops:** HuggingFace (Datasets, Hub, Transformers) |
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- **Ml Explainability:** SHAP |
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- **Cloud:** AWS |
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- Model Hosting: Lambda |
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- DB Backend: DynamoDB |
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- Orchestration: Step-Functions |
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- UI Hosting: EC2 |
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- Routing: API Gateway |
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- **UI:** Budibase |
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## Training and evaluation data |
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consumer_complaints dataset |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 3 |
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### Framework versions |
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- Transformers 4.16.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.2 |
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- Tokenizers 0.11.0 |
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