Text Classification with Hugging Face BERT

Text classification is one of the most common natural language processing tasks, and ShopMax India uses it to automatically sort incoming customer reviews into sentiment categories - positive, negative, or neutral. Instead of manually reading thousands of reviews from Mumbai and Bangalore customers, the system routes negative feedback to the support team instantly.

Hugging Face provides pre-trained BERT models through its transformers library. The pipeline API wraps the model and tokenizer into a single callable that handles tokenization, inference, and decoding. For sentiment analysis, the text-classification pipeline uses a fine-tuned BERT variant like distilbert-base-uncased-finetuned-sst-2-english which outputs POSITIVE or NEGATIVE labels with confidence scores.

The example below classifies ShopMax India customer reviews using the Hugging Face text classification pipeline. It processes a batch of reviews and prints the predicted sentiment and confidence for each one.

It gives the following output,

Review: The Samsung TV delivered to my Delhi address was in pe...
Sentiment: POSITIVE (99.84% confidence)

Review: Ordered a laptop from ShopMax but it arrived with a cr...
Sentiment: NEGATIVE (99.91% confidence)

Review: Fast delivery to Hyderabad. The product is okay, nothi...
Sentiment: POSITIVE (62.15% confidence)

Review: Excellent customer support from ShopMax India. They res...
Sentiment: POSITIVE (99.93% confidence)

Review: The headphones stopped working after two days. Terrible...
Sentiment: NEGATIVE (99.88% confidence)

In production, ShopMax India should batch reviews in groups of 32 to stay within BERT's 512-token limit and avoid memory errors. Use truncation=True and max_length=512 in the pipeline call for long reviews. For multi-class classification beyond positive/negative, fine-tune on your own labelled dataset using Hugging Face Trainer API with categories like delivery, product quality, and customer service.