ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
A ready-to-run tutorial in Python and scikit-learn to evaluate a classification model compared to a baseline model
- Embeddings transform words and sentences into sequences of numbers for computers to understand language.
- This technology powers tools like Siri, Alexa, Google Translate, and generative AI systems like ChatGPT, Bard, and DALL-E.
- In the early days, embeddings were crafted by hand, which was time-consuming and couldn't adapt to language nuances easily.
- The 3D hand-crafted embedding app provides an interactive experience to understand this concept.
- The star visualization method offers an intuitive way to understand word embeddings.
- Machine learning models like Word2Vec and GloVe revolutionized the generation of word embeddings from large text datasets.
- Universal Sentence Encoder (USE) extends the concept of word embeddings to entire sentences.
- TensorFlow Projector is an advanced tool to interactively explore high-dimensional data like word and sentence embeddings.
Apply sound data-based anomalous behavior detection, diagnose the root cause via object detection concurrently, and inform the user via SMS.
With all the hype around AI/ML in observability, it's more likely than ever that companies benefit from storing and viewing data in one system and training ML models in another.