In this post, I want to introduce you to text embedding using BERT and MediaPipe. Text embedding is an essential technique in Natural Language Processing (NLP) that helps convert words or sentences into numeric representations (also known as vectors) that machine learning models can easily process. Whether you're into AI, NLP, or machine learning, this post will give you a basic understanding of using BERT with MediaPipe for text embedding.

🔍 What is Text Embedding?

Text embedding transforms text into numeric representations that machines can understand. This is key for tasks like text classification, sentiment analysis, and even comparing the similarity between different sentences.

🤖 What is BERT?

BERT (Bidirectional Encoder Representations from Transformers) is one of the most powerful transformer models for NLP tasks. It helps understand the context of a word based on the surrounding words, which is crucial for accurate text embeddings.

🔧 What is MediaPipe?

MediaPipe is a cross-platform framework primarily used for machine learning and AI-driven solutions. In this tutorial, we’ll see how MediaPipe can be combined with BERT for generating embeddings that can be used for a variety of NLP tasks, like comparing sentence similarities using Cosine Similarity.

📘 Example Code

Here’s a simple example of how you can use the SentenceTransformer (a variation of BERT) for text embedding and compute Cosine Similarity between two sentences using MediaPipe:

# Import the necessary libraries
from sentence_transformers import SentenceTransformer, util

# Load a pre-trained sentence transformer model
sentence_model = SentenceTransformer('all-MiniLM-L6-v2')

# Example sentences
emb1 = sentence_model.encode("I am eating Apple")
emb2 = sentence_model.encode("I am eating Apple")

# Compute cosine similarity between the embeddings
cos_sim = util.cos_sim(emb1, emb2)

# Output the similarity score
print("Cosine-Similarity:", cos_sim)

This code takes two sentences, computes their embeddings using the BERT model, and compares the embeddings using Cosine Similarity.

📹 Learn by Example

Instead of diving deep into theory, I’ve created a video tutorial that walks you through the whole process step-by-step. You’ll learn how to:

• Use BERT for text embedding
• Integrate it with MediaPipe
• Compute Cosine Similarity for comparing sentences

Check out the full video here: Watch on YouTube

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