14 Powerful Techniques Defining the Evolution of Embedding - Analytics Vidhya

This article explores the evolution of text embeddings, from simple count-based methods to sophisticated context-aware models. It highlights the role of leaderboards like MTEB in evaluating embedding performance and the accessibility of cutting-edge models through platforms like Hugging Face.

Table of Contents:

• MTEB Leaderboard Rankings
• Count Vectorization
• One-Hot Encoding
• TF-IDF
• Okapi BM25
• Word2Vec
• GloVe
• FastText
• Doc2Vec
• InferSent
• Universal Sentence Encoder (USE)
• Node2Vec
• ELMo
• BERT and its Variants (SBERT, DistilBERT, RoBERTa)
• CLIP and BLIP
• Embedding Comparison
• Conclusion

MTEB Leaderboard Rankings:

Modern large language models (LLMs) produce embeddings as part of their architecture. These can be fine-tuned for various tasks, making them highly versatile. The Massive Text Embedding Benchmark (MTEB) Leaderboard ranks these models based on performance across tasks like classification and retrieval, aiding practitioners in model selection.

The article then delves into classic vectorization techniques, demonstrating their role as foundational elements for modern embeddings.

Count Vectorization: This simple method represents text as vectors based on word counts. While straightforward and interpretable, it suffers from high dimensionality and lacks semantic context.

Code Example: (Code snippet included showing sklearn's CountVectorizer)

Output: (Image of CountVectorizer output included)

One-Hot Encoding: This technique assigns unique binary vectors to each word. Simple but highly inefficient for large vocabularies and lacks semantic understanding.

Code Example: (Code snippet included showing sklearn's CountVectorizer with binary=True)

Output: (Image of One-Hot Encoding output included)

The article compares CountVectorizer and One-Hot Encoding, highlighting their respective strengths and weaknesses. (Image comparing the two included)

TF-IDF (Term Frequency-Inverse Document Frequency): TF-IDF weighs word importance by considering both frequency within a document and rarity across the corpus. It improves upon raw counts but still lacks contextual understanding.

Code Example: (Code snippet included showing sklearn's TfidfVectorizer)

Output: (Image of TF-IDF output included)

Okapi BM25: A probabilistic model primarily for document ranking, BM25 enhances TF-IDF by accounting for document length and term frequency saturation. It's not a true embedding method but is influential in information retrieval.

Code Example: (Code snippet included showing BM25 implementation)

Output: (Image of BM25 output included)

Word2Vec (CBOW and Skip-gram): Word2Vec uses neural networks to learn dense vector representations capturing semantic and syntactic relationships. CBOW predicts a word from its context, while skip-gram predicts context from a word.

Code Example: (Code snippet included showing gensim's Word2Vec)

Output: (Image of Word2Vec output and visualizations included)

GloVe (Global Vectors for Word Representation): GloVe combines global co-occurrence statistics with local context to generate embeddings. It's known for its consistent representation across contexts.

Code Example: (Code snippet included showing GloVe implementation)

Output: (Image of GloVe output and visualization included)

FastText: FastText extends Word2Vec by incorporating subword information, improving handling of rare words and morphologically rich languages.

Code Example: (Code snippet included showing FastText implementation)

Output: (Image of FastText output included)

Doc2Vec: Doc2Vec extends Word2Vec to generate fixed-length vectors for variable-length documents, enabling document-level analysis.

Code Example: (Code snippet included showing Doc2Vec implementation)

Output: (Image of Doc2Vec output included)

InferSent: InferSent uses supervised learning on natural language inference datasets to generate high-quality sentence embeddings.

Code Example: (Reference to Kaggle notebook)

Output: (Image of InferSent output included)

Universal Sentence Encoder (USE): USE provides general-purpose sentence embeddings suitable for various NLP tasks with minimal fine-tuning.

Code Example: (Code snippet included showing USE implementation)

Output: (Image of USE output and visualization included)

Node2Vec: Node2Vec is a graph embedding technique applicable to NLP tasks involving network data.

Code Example: (Code snippet included showing Node2Vec implementation)

Output: (Image of Node2Vec output and visualization included)

ELMo (Embeddings from Language Models): ELMo generates dynamic word embeddings that change based on context, capturing syntactic and semantic nuances.

Code Example: (Reference to external article)

BERT and its Variants (SBERT, DistilBERT, RoBERTa): BERT uses a transformer architecture for bidirectional context understanding. SBERT is optimized for sentence comparison, DistilBERT is a lighter version, and RoBERTa improves BERT's training.

Code Example: (Code snippet included showing implementation of BERT variants)

Output: (Image of BERT variant output included)

CLIP and BLIP: CLIP and BLIP are multimodal models that generate embeddings for both images and text, enabling cross-modal understanding.

Code Example: (Code snippet included showing CLIP implementation)

Output: (Image of CLIP output included)

Embedding Comparison Table: (Table comparing all embedding methods included)

Conclusion: The article concludes by emphasizing the significant advancements in embedding techniques and the accessibility of these models for various applications. It highlights the importance of choosing the right embedding method based on specific needs and goals.

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