To generate embeddings for product descriptions, you typically use a machine learning model to convert text into numerical vectors that capture semantic meaning. Start by selecting a pre-trained language model like BERT, Word2Vec, or a sentence transformer (e.g., Sentence-BERT). These models are trained on large text corpora and can map words or sentences to dense vectors. For example, using Hugging Face’s transformers library, you can load a pre-trained BERT model, tokenize the product description, and extract embeddings from the model’s output layers. Alternatively, sentence-transformers simplify this by providing APIs that directly generate sentence-level embeddings with minimal code. The key is to choose a model that aligns with your use case—broader models for general semantics or domain-specific ones for specialized vocabulary.
The process involves three main steps: preprocessing, model inference, and post-processing. First, clean the product descriptions by removing irrelevant characters, normalizing text (lowercasing, stemming), and handling missing values. For instance, a description like “Men’s Waterproof Jacket – Size L, 100% Nylon” might be simplified to “men waterproof jacket size large 100 nylon.” Next, tokenize the text and feed it into the model. With TensorFlow or PyTorch, this involves encoding the text into input IDs and attention masks, then running a forward pass. For example, using PyTorch and BERT, you’d extract the [CLS] token’s output or average the hidden states to create a fixed-length vector. Post-process the embeddings by normalizing them (e.g., L2 normalization) to ensure consistent scales, which improves performance in tasks like similarity search.
Once embeddings are generated, they can be used for tasks like search, clustering, or recommendations. For example, you could compute cosine similarity between embeddings to find related products or reduce dimensionality with PCA for visualization. Tools like FAISS or Annoy optimize similarity searches at scale. A practical workflow might involve generating embeddings for 10,000 products using a batch inference script, storing them in a vector database like Pinecone, and querying them in real time via an API. If performance is critical, fine-tune the model on your product data to better capture domain-specific patterns. Always validate embeddings by testing downstream tasks—e.g., check if similar products are grouped correctly in a clustering experiment.