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 Copy code pip install transformers Model Loading: You can easily load the pre-trained model for sentiment analysis using Hugging Face's AutoModelForSequenceClassification.
python Copy code from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("your-model-name") tokenizer = AutoTokenizer.from_pretrained("your-model-name") Inference: Tokenize your text data with the provided tokenizer and use the model for sentiment analysis.
python Copy code review = "This dress is amazing, I love it!" inputs = tokenizer(review, return_tensors="pt") outputs = model(**inputs) predicted_class = torch.argmax(outputs.logits) Customization: Fine-tune the model on your own dataset by following the provided example or training script.
Reporting: Analyze reviews and extract insights for your specific use case or business needs.
Model Card For more details on how to use and cite this model, please refer to the accompanying model card.
Issues and Contributions If you encounter any issues or have suggestions for improvements, please feel free to open an issue or contribute to this project.
License This model is provided under the MIT License.