Analyzing Intel® OneAPI's Multilingual Public Reviews with Fine Tuned BERT Model using Intel® OneAPI Toolkit (Daal4Py, OneDAL) and PyTorch

Risab Biswas

Jalpaiguri, West Bengal

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This is a fun project where I fine-tuned a BERT model for analyzing Intel® OneAPI Public Reviews. The model comes with a twist, that it doesn't have any language barrier. The input sentiment could be of almost (any) language. ...learn more

Project status: Under Development

oneAPI, Artificial Intelligence

Groups
Artificial Intelligence India, DeepLearning

Intel Technologies
oneAPI, Intel NUC, Intel Opt ML/DL Framework, Intel Python, DPC++

Overview / Usage

Sentiment Extraction is one of the widely adopted use-cases in the Industry. It's a very good starting point for NLP research enthusiasts.

You'll learn how to do the required text preprocessing (special tokens, padding, and attention masks) and build a Sentiment Classifier using the amazing Transformers library by Hugging Face!

Fine Tuned a BERT Sentiment Model using Intel OneAPI ToolKit and PyTorch. The training was done both on a NUC and Intel DevCloud. The model is trained on the IMDB Dataset of 50k reviews. The model accuracy came to 96.3% and the inference shows some great results.

This will give you a great understanding of Intel's OneAPI Advanced Analytics toolkit and how to leverage the power of Intel DevCloud.

Dataset- https://rb.gy/mri6k4

Methodology / Approach

Following are the Steps -

Intuitively understand what BERT is!
Setting up the environment on Intel DevCloud
Preprocess text data for BERT and build PyTorch Dataset (tokenization, attention masks, and padding)
Use Transfer Learning to build Sentiment Classifier using the Transformers library by Hugging Face
Evaluate the model on test data
Predict sentiment on raw text
Serving the solution as a web app using Flask