Women's Clothing Reviews Sentiment Analysis with DistilBERT

Overview

This Hugging Face repository contains a fine-tuned DistilBERT model for sentiment analysis of women's clothing reviews. The model is designed to classify reviews into positive, negative, or neutral sentiment categories, providing valuable insights into customer opinions.

Model Details

Model Architecture: Fine-tuned DistilBERT
Sentiment Categories: Neutral [0], Negative [1], Positive [2]
Input Format: Text-based clothing reviews
Output Format: Sentiment category labels

Fine-tuning procedure

This model was fine-tuned using a relatively small dataset containing 23487 rows broken down into train/eval/test dataset. Nevertheless, the fine-tuned model was able to performs slightly better than the base-distilbert-model on the test dataset.

Training result

It achieved the following results on the evaluation set:

Validation Loss: 1.1677

Comparison between the base distilbert model VS fine-tuned distilbert

Model	Accuracy	Precision	Recall	F1 Score
DistilBERT base model	0.79	0.77	0.79	0.77
DistilBERT fine-tuned	0.85	0.86	0.85	0.85

Installation

To use this model, you'll need to install the Hugging Face Transformers library and any additional dependencies.

pip install transformers
pip install torch

Usage

You can easily load the pre-trained model for sentiment analysis using Hugging Face's DistilBertForSequenceClassification and DistilBertTokenizerFast.

from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast
import torch

model_name = "ongaunjie/distilbert-cloths-sentiment" 
tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)

review = "This dress is amazing, I love it!"
inputs = tokenizer.encode(review, return_tensors="pt")
with torch.no_grad():
    outputs = model(inputs)
predicted_class = int(torch.argmax(outputs.logits))