Embedders in AI

Overview

Embedders are algorithms or models used to convert data (e.g., text, images, audio) into a fixed-size vector representation, commonly known as embeddings. These embeddings capture the semantic meaning of the data and are essential in various machine learning and natural language processing tasks.

Types of Embedders

  1. Word Embeddings

    • Convert words into dense vectors.
    • Examples: Word2Vec, GloVe, FastText.

  2. Sentence Embeddings

    • Convert sentences or phrases into dense vectors.
    • Examples: Sentence-BERT, Universal Sentence Encoder.

  3. Document Embeddings

    • Convert entire documents into dense vectors.
    • Examples: Doc2Vec, Transformer-based models.

  4. Image Embeddings

    • Convert images into dense vectors.
    • Examples: Convolutional Neural Networks (CNNs), ResNet, Inception.

  5. Audio Embeddings

    • Convert audio signals into dense vectors.
    • Examples: MFCC, Wave2Vec.

Applications

  • Information Retrieval: Efficiently retrieve relevant documents or data.
  • Text Classification: Classify text into predefined categories.
  • Recommendation Systems: Provide personalized recommendations.
  • Clustering: Group similar data points together.
  • Semantic Search: Improve search results by understanding context and meaning.

Word2Vec

from gensim.models import Word2Vec

# Training Word2Vec model
sentences = [["this", "is", "a", "sample", "sentence"], ["word", "embeddings", "are", "useful"]]
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)

# Getting embedding for a word
word_embedding = model.wv['sample']
print(word_embedding)

BERT (Bidirectional Encoder Representations from Transformers)

from transformers import BertTokenizer, BertModel
import torch

# Initialize BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

# Encode a sentence
sentence = "Embedding models are powerful."
inputs = tokenizer(sentence, return_tensors='pt')
outputs = model(**inputs)

# Get the sentence embedding
sentence_embedding = outputs.last_hidden_state.mean(dim=1).squeeze()
print(sentence_embedding)

Sentence-BERT

from sentence_transformers import SentenceTransformer

# Initialize Sentence-BERT model
model = SentenceTransformer('paraphrase-MiniLM-L6-v2')

# Encode a list of sentences
sentences = ["Embedding models are powerful.", "They convert text into vectors."]
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print(f"Sentence: {sentence}\nEmbedding: {embedding}\n")

Evaluation Metrics

  • Cosine Similarity: Measures the cosine of the angle between two vectors.
  • Euclidean Distance: Measures the straight-line distance between two vectors.
  • Dot Product: Measures the product of the magnitudes of two vectors and the cosine of the angle between them.