Customer support teams often struggle to keep up with a flood of messages, but machine learning can help prioritize the most urgent ones. This article walks through a real-world scenario using the Hugging Face ecosystem to filter customer feedback.
Defining the Task, Dataset, and Model
Before coding, clearly define your use case. Here, the goal is to automatically identify highly unsatisfied customers so support can focus on them. This maps to a text classification task with five sentiment levels: very unsatisfied, unsatisfied, neutral, satisfied, very satisfied.
Choosing a Dataset
For training, the Amazon Reviews Multi dataset is ideal: it contains millions of product reviews with star ratings (1-5) that align with sentiment levels. It's large, high quality, and closely resembles real customer feedback. The dataset viewer on the Hugging Face Hub helps verify data suitability.
Selecting a Model
A pre-trained transformer model fine-tuned on this dataset can accurately classify messages. The Hugging Face Hub offers many models ready for fine-tuning, such as BERT or RoBERTa.
Implementation Steps
- Load the dataset using the
datasetslibrary. - Preprocess text with a tokenizer matched to your chosen model.
- Fine-tune the model on the dataset, adjusting for five classes.
- Evaluate on a held-out test set to ensure performance.
- Deploy the model via the Hub and use it to filter incoming messages.
The full code is available in this notebook.
By following this approach, your customer service team can automatically flag the most critical feedback and respond faster, improving customer satisfaction without increasing headcount.