language: en
license: apache-2.0
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))