This fine-tuned DistilBERT model is specifically designed for document classification. It classifies customer feedback into six predefined categories: Shipping and Delivery, Customer Service, Price and Value, Quality and Performance, Use and Design, and Other. By leveraging the transformer-based architecture of DistilBERT, the model efficiently handles the syntactic patterns of text, providing accurate document classification based on content, style, and structure.
Model type: DistilBERT (fine-tuned for text classification)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: distilbert/distilbert-base-uncased
Bias, Risks, and Limitations
While the model achieves high accuracy across the six categories, it has limitations when dealing with overlapping categories or multiple labels within a single document. The model is designed for single-label classification, meaning it can only detect one label per document. If a document contains features of multiple categories (e.g., both 'Quality and Performance' and 'Price and Value'), the model may struggle to correctly identify both and will predict only one category, potentially leading to misclassification.
Recommendations
Users (both direct and downstream) should be aware of the model's single-label prediction limitation. In cases where a document contains features of multiple categories, additional models or multi-label classification techniques should be considered.
Training Data
A custom synthetic dataset was created for this task, focusing on the structural features of text. The dataset provides examples from six categories, helping the model learn from both the syntactic organization and the meaning of the text.
Training Hyperparameters
Model: distilbert/distilbert-base-uncased
Learning Rate: 3e-5
Epochs: 7
Train Batch Size: 16
Gradient Accumulation Steps: 2
Weight Decay: 0.015
Warm-up Ratio: 0.1
Evaluation
The model was evaluated using a custom dataset representing the same six document categories. Performance was measured based on accuracy, precision, recall, and F1-score across the categories.
Here is an example of how to use this model for inference:
python
1from transformers import pipeline
23classifier = pipeline("text-classification", model="dnzblgn/Customer-Reviews-Classification")4result = classifier("The product arrived on time and was exactly as described.")5print(result)
