This project presents an end-to-end text classification system covering data collection, model training, and deployment. The model is designed to classify 138 distinct quotation genres. For a comprehensive list of identified quote genres, please refer to the keys in deployment\tag_types_encoded.json.
The dataset used for training this model was obtained by scraping quotes from the Goodreads website using the Selenium web automation tool. The data includes quotes from various genres such as:
- Love Quotes
- Life Quotes
- Inspirational Quotes
- Humor Quotes
- Philosophy Quotes
- Inspirational Quotes Quotes
- God Quotes
- Motivational Quotes
- Truth Quotes
- Wisdom Quotes
- Romance Quotes
- Poetry Quotes
- Death Quotes
- Happiness Quotes
- Hope Quotes
- Faith Quotes
- Success Quotes
- Life Quotes Quotes
- Time Quotes
- Motivation Quotes etc.
In total, approximately 38,611 quotes were collected. The dataset consists of three main columns: author name, quotation text, and genres. the data are stored in Dataset/dataquotes.csv.Anyone can access the dataset from here
At first, there were 23,415 different kinds of genres in the dataset. After looking closely, I noticed many of them were rare, so I got rid of those uncommon ones. In the end, I settled on 138 genres. Also, I removed some quotes that didn't have any genre assigned, leaving me with a total of 38,607 data points.The preprocessing can be found in Notebook/NLP_Multilabel_Classification.ipynb.
I use a distilrobera-base model for finetuning from HuggingFace Transformers using Fastai and Blurr. The training happened in three stages, and I picked the best model with an accuracy of 97.8% for deployment. You can check out the notebook for training here.
The trained model takes up 313 MB of memory. I managed to shrink it down to around 78 MB by compressing it using ONNX quantization. You can check out the smaller file from
here and you can access the main trained model using this link. Anyone interested to see the onxx code can access this from here named onnx_inference_.ipynb
The model is now live on the HuggingFace Spaces Gradio App. You can explore the implementation in the deployment folder. Access the Hugging Face Spaces link here
I set up a Flask App that takes in quotes and shows their genres as output. Find all the necessary files in the Flask branch on GitHub. You can try out the live website here
