For example, two bikes might be semantically similar but have different vector representations due to variations in the vectorization process. Bridging this semantic gap can mean going back to the vectorization process and capturing more accurate semantic features of items in their vector representations...
The application of data vectorization is truly limitless at this point. Once data is turned into vectors, you can perform tasks such as fraud or anomaly detection. Data processing, transformation, and mapping can be part of a machine-learning model. Chatbots can be fed production documentation a...
Through vectorization and the prowess of large language models (LLMs), generative AI achieves its game-changing potential. In the era of generative AI, vector embeddings lay the groundwork; vector databases amplify its impact. What is a vector database? How does it work? What are some common...
For this explainer, we will focus on the vector representations applicable under natural language processing (NLP), that is, vectors that represent words, entities or documents. We will illustrate the vectorization process by vectorizing a small corpus of sentences: “the cat sat on the mat”, ...
A most known approach for vectorization is known as a bag of words that counts the number of times a word, from a predefined collection of words, appears in the text you want to analyze. The text data converted into vectors, along with the anticipated predictions like tags, is fed to the...
(such as large language models, LLM) in avector space. “Vectorization” is the process of converting words into vectors. The relationships between the words are effectively captured as well. In the vector space, words with similar meanings or contexts as vectors appear to be physically close ...
Query vectorization Once you have vectorized your knowledge base you can do the same to the user query. When the model sees a new query, it uses the same preprocessing and embedding techniques. This ensures that the query vector is compatible with the document vectors in the index. Retrieval...
Text Processing: In natural language processing (NLP), text data must undergo preprocessing transformations like tokenization, stemming, and vectorization to be used effectively for analysis or machine learning. Data transformation helps us turn messy data into something neat and useful, making it easier...
The act of converting text to numerics is known as tokenization. Typically the numeric value is quite a few numbers, like 1562 for each text. We won’t get too deep into the process of vectorization here, but you can learn more in our “What are vector embeddings?” guide. ...
Task parallelism is used to reduce serial time by running tasks concurrently; in pipelining, for example, where a series of tasks is performed on a single set of data. 4. Superword-level parallelism More advanced than ILP, superword-level parallelism (SLP) is a vectorization tactic that is ...