ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
- Challenges in measuring similarity between unstructured text data like movie descriptions.
- Simple NLP methods may not yield meaningful results; thus, a controlled vocabulary is proposed.
- Using an LLM, a genre list is generated for movie titles, which helps improve the similarity model.
A function is created to find the most similar movies to a given title based on cosine similarity scores.
Network visualization highlights clusters of genres linked via movies, showcasing potential improvements in recommender systems.
- 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.
With deep learning, the ROI for having clean and high quality data is immense, and this is realized in every phase of training. For context, the era right before BERT in the text classification world was one where you wanted an abundance of data, even at the expense of quality. It was more important to have representation via examples than for the examples to be perfect. This is because many Al systems did not use pre-trained embeddings (or they weren't any good, anyway) that could be leveraged by a model to apply practical generalizability. In 2018, BERT was a breakthrough for down-stream text tasks,
DSPy provides composable and declarative modules for instructing LMs in a familiar Pythonic syntax. It upgrades "prompting techniques" like chain-of-thought and self-reflection from hand-adapted string manipulation tricks into truly modular generalized operations that learn to adapt to your task.
Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques
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