---
language: en
---

# 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**: Positive, Negative, Neutral
- **Input Format**: Text-based clothing reviews
- **Output Format**: Sentiment category labels

## Usage

### Installation

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

```bash
pip install transformers

### Model Loading

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

```python
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)

## Inference
You can use this model to perform sentiment analysis on text. Here's an example of how to do it in Python:

```python
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))