Using Pre-trained Word Embeddings

Popular Pre-trained Embeddings

📚 Google Word2Vec

300d vectors, 3M vocab
Trained on Google News

🌐 Stanford GloVe

Multiple dimensions available
Common Crawl, Wikipedia, Twitter

⚡ Facebook FastText

Subword information
157 languages available

🏥 Domain-Specific

BioWordVec, Law2Vec
Specialized vocabularies

Integration Techniques

🔍 Feature Extraction

🎯 Fine-tuning

🧱 As Embedding Layer

❓ Out-of-Vocabulary Words

Typical Workflow

📥

Load Embeddings

Download and load pre-trained vectors

🗺️

Map Vocabulary

Align with your dataset vocab

🚀

Initialize Model

Set up embedding layer

🎓

Train Model

Fine-tune or freeze

# Quick example: Using GloVe with Keras
from tensorflow.keras.layers import Embedding

embedding_layer = Embedding(
    input_dim=vocab_size,
    output_dim=300,
    weights=[embedding_matrix],
    trainable=False  # Freeze pre-trained weights
)
            

Prepared by Dr. Gorkem Kar

Using Pre-trained Word Embeddings

Popular Pre-trained Embeddings

Integration Techniques

Typical Workflow

Modal Title