Data Science Project | Part 1 | Name Entity Recognition with Bert

Welcome to Part 1 of our Data Science Project series!

In this session, we dive into the powerful field of Named Entity Recognition (NER) using BERT (Bidirectional Encoder Representations from Transformers), a state-of-the-art model in natural language processing (NLP).

Named Entity Recognition is crucial for extracting and categorizing entities such as names, organizations, and locations from text data.

What You'll Learn:

Introduction to Named Entity Recognition: Understand the importance of NER in extracting meaningful information from unstructured text and its applications across various industries.

Overview of BERT: Introduction to BERT, its architecture, and how it revolutionizes NLP tasks by capturing bidirectional context from large amounts of text data.

Dataset Preparation: Explore the dataset used for training the BERT model for NER, including data preprocessing steps such as tokenization and handling entity annotations.

Fine-Tuning BERT: Step-by-step guide on fine-tuning the pre-trained BERT model for NER tasks, including adjusting hyperparameters and integrating custom entity types.

Model Evaluation: Learn how to evaluate the performance of your BERT-based NER model using metrics such as precision, recall, and F1 score.


Phone: +91 8071176111

Website: https://ineuron.ai/
Instagram:   / official_ineuron.ai  
Discord :   / discord  
YouTube:    / @ineuronintelligence  
Hindi:    / @ineurontechhindi  
Tech News:    / @ineurontechnews  
DevHub:    / @ineurondevhub  
DevOps :    / @ineurondevops  
Non Tech :    / @ineuronnontech  
Linkedin:   / ineuron-ai  
Twitter:   / ineuron_ai  
Quora: https://www.quora.com/profile/INeuron...