Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Harshit Hemant Gupta
- Model type: DistilBERT-based text classification
- Language(s) (NLP): English
- Finetuned from model [optional]: distilbert-base-uncased
Uses
Direct Use
The model is intended to classify bank customer complaints into relevant categories directly. It can be integrated into customer support systems for automated categorization and faster resolution of issues.
Downstream Use [optional]
This model can be used as a foundation for other text classification tasks in the financial services domain, such as customer sentiment analysis or fraud detection.
Out-of-Scope Use
The model may not perform well outside of its training data scope, particularly for text unrelated to financial complaints.
Bias, Risks, and Limitations
The model's performance depends heavily on the quality and diversity of the training data. It might inherit biases present in the training dataset and may not generalize well for all demographic groups or complaint styles.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
The model was fine-tuned on a dataset of labeled customer complaints. The data includes text from various financial domains, such as credit reporting and mortgage issues.
Training Procedure
Preprocessing: Tokenized input text using the distilbert-base-uncased tokenizer.
Hyperparameters: Not specified in the provided content.
Framework: Hugging Face transformers library with PyTorch backend
Testing Data
Testing Data: Evaluation was conducted using a test dataset containing customer complaints similar to the training data.
Metrics: Metrics like accuracy, precision, recall, and F1-score were likely used to measure the model's performance.
Results: The specific performance metrics are not detailed in the provided content. Users should evaluate the model on their datasets to verify its effectiveness.
Summary
Citation [optional]
If you use this model, please consider citing the Hugging Face repository.
