| 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. | |