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Fine tuning Embedding model for domain specific search

Instructor led workshop to finetune an pre trained text embedding models using custom domain data.

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4.7

Rating

10 Hrs

Duration

Advance

Level

10

Assignments

What you'll learn

How to fine tune a base embedding model from custom domain data
How to evaluate the performance of fine-tuned model vs original model
Approaches for creating multimodal embeddings

Who should take this course

Ideal for anyone with python programming and Machine learning and NLP knowledge
Anyone who wants to build custom text embedding for better accuracy in semantic similarity or information extraction

Skills you'll gain

Python
Text embeddings
Ontology based model
NLP
Machine learning
Deep Learning
BERT
Transformer

Course contents

Understanding the use case objective for Fine tuning text embedding model for domain specific search

Text preprocessing
Gather positive and negative pairs

Pick the base model
Pick up a loss function

Evaluate performance using general pretrained model
Evaluate performance using custom trained model

Approach discussion

Building custom NER for medical domain

This workshop will focus on creating specialized NER model trained on medical texts to identify medical entities and extract relationship among them.

LLM fine-tuning for medical domain

This workshop includes training or finetuning a more lightweight version of a LLM for specific tasks or ontology domains.

Building Knowledge Graph from documents

Here we will built a pipeline to convert unstructured text data into a graph representation of knowledge base.