Multilabel_Dataset_Category_Classifier

An end to end text classification model with data collection, model training,model compression and deployment.
This model can classify 102 different types of dataset types
The keys of deployment\category_types_encoded.json shows the dataset category.
To deploy this model, I used distilroberta-base huggingface model which is used for multilabel text classification. But, I also trained my model using microsoft/deberta-base and bert-base-uncased model with 96.20% Accuracy with 5 epochs and finally, I compared and deploy the best model for this project. N.B: the classes name used in the scrapers\dataset_url_scraper.py and scrapers\dataset_info_scraper.py can be changed due to Kaggle's policy. It might be occurred to tackle the scraping.

Data Collection

Data was collected from Kaggle Website
The data collection process is divided into 2 steps:

1. Dataset URL Scraping: The dataset urls were scraped with scrapers\dataset_url_scraper.py and the urls are stored in data\dataset_urls.csv
2. Dataset Info Scraping: Using the urls, dataset title, description and categories are scraped with scrapers\dataset_info_scraper.py and they are stored in data\dataset_info.csv

In total, I scraped 20,942 dataset details.

Data Preprocessing

After removing the duplicate urls and null values from the dataset the dataset transformed to 19,920 from 20,942. Initially there were 437 different categories in the dataset. After some analysis, I found out 334 of them are rare based on setting up the threshold which was 0.5% of the total length of the dataset. So, I removed those genres and then I have 102 categories.

Model Training

I finetuned a distilrobera-base model from HuggingFace Transformers using Fastai and Blurr. The model training notebook can be viewed notebooks/distilroberta_modelTraining

Model Compression and ONNX Inference

The trained model has a memory of 315.5MB but I compressed this model using ONNX(Open Neural Network Exchange) quantization and brought it 78.8MB

Model Comparison

Why I did not choose DeBERTa and BERT Models?

As I used Google Colab for free that is why it did not not possible to train my model using these two models with proper learning rate as these two models take more memory than distilroberta-base and when I wanted to train my model using proper learning rate I got MemoryOutError in google colab..So, there might be overfitting issue using these models.Moreover, I got less train loss and valid loss for distilroberta-base model.Further, Distilled version of RoBERTa, meaning it’s smaller and faster to train,retains much of RoBERTa’s performance while being more resource-efficient, suitable for scenarios with limited computational resources. For these reasons, I decided to deploy distilroberta-base model.

Model Deployment

The compressed model is deployed to HuggingFace Spaces Gradio App. The implementation can be found in deployment folder or here

Web Deployment

Deployed a Flask App built to take descprition and show the categories as output. If the model recognize any category based on your provided description then it will return null value. Check flask branch. The website is live here

Build from the sources

Python version 3.10 or 3.11 is required.

To get started with the project, follow these steps:

1. Clone the project repository from GitHub

   git clone https://github.com/armanulalam/multilabel_dataset_classifier.git

2. Set up the required dependencies and libraries by executing the following command:

   pip install -r requirements.txt

3. Scraping the data. scrapers/dataset_urls_scraper.py scrapers/dataset_info_scraper.py You will get the two csv files which are in data folder.

4. Train the model using the dataset by executing the following notebook: disltilroberta_modelTraining.ipynb.

5. Compress the model and ONNX(Open Neural Network Exchange) Inference by executing the following notebook: ONNX_distilroberta.ipynb

6. Now, you can deploy the model by executing the following commands:

   cd .\deployment\
   python app.py

7.To deploy the model using Flask app and Render server you need to switch Flask Branch.

If you have any further query, do not hesitate to message me to [email protected]