Choosing embedding models

Embedding models impact an application’s performance, its ability to understand language and other forms of data, and ultimately, a project’s success. The following sections look at the parameters for choosing the right embedding model that aligns with the task requirements, characteristics of your dataset, and computational resources. This section explains vector dimensionality and model leaderboards as additional information to consider when choosing embedding models. For a quick overview of this section, you can consult Table 4.2.

Task requirements

Each type of task may benefit from different embedding models based on how they process and represent text data. For instance, tasks such as text classification and sentiment analysis often require a deep understanding of semantic relationships at the word level. Word2vec or GloVe are particularly beneficial in these cases, as they provide robust word-level embeddings that capture semantic...