Generating Text Data Embeddings with BERT
Learn how to use BERT to generate embeddings for text data, ranging from word-level to sentence and document-level embeddings.
Dataset
In our complete example at the end of the lesson, we’ll use a
We’ll take a methodical approach to the example, starting with explaining the embedding model. Subsequently, we’ll explain the process of generating word and sentence/document embeddings using simple text examples. Finally, we’ll apply our learnings to our “job title and description” dataset to build a small application for finding jobs.
Embedding model: BERT
BERT is a popular choice to generate word, sentence, and document embeddings.
BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art natural language processing model developed by Google. It’s designed to capture the bidirectional context of words in a text corpus by pretraining on large amounts of unlabeled text data. Key components of the BERT embedding model include the following:
Tokenization: BERT tokenizes input text into subword tokens using WordPiece tokenization. This allows BERT to handle out-of-vocabulary words and capture morphological variations.
Transformer architecture: BERT utilizes a transformer architecture consisting of multiple layers of self-attention mechanisms and feedforward neural networks. This architecture enables BERT to capture contextual information from the left and right contexts of each word in a sentence.
Pretraining: BERT is pretrained on large text corpora using two unsupervised learning tasks: masked language model (MLM) and next sentence prediction (NSP). In MLM, BERT predicts masked words in a sentence based on the context of the surrounding words. In NSP, BERT predicts whether two sentences appear consecutively in the original text.
Embedding generation: During pretraining, BERT learns contextualized embeddings for each token in the input text. These embeddings capture the meaning of individual words and their relationships with surrounding words in the context of a sentence.
Fine-tuning: After pretraining, BERT can be fine-tuned on downstream tasks such as text classification, named entity recognition, and sentiment analysis. Fine-tuning adapts BERT’s parameters to the specific task, improving its performance.
Pretrained BERT model (bert-base-uncased)
The bert-base-uncased model is a pretrained version of BERT developed by Google. It is a powerful tool for generating high-quality embeddings that capture contextual information and semantic meaning in natural language text. We’ll use it to generate word, sentence, and ...