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This tutorial demonstrates how to perform semantic clustering of user messages using Large Language Models (LLMs) by prompting them to analyze publicly available Discord messages. It covers methods for data extraction, sentiment scoring, KNN clustering, and visualization, emphasizing faster and less effort-intensive processes compared to traditional data science approaches.
Introducing sqlite-vec, a new SQLite extension for vector search written entirely in C. It's a stable release and can be installed in multiple ways. It runs on various platforms, is fast, and supports quantization techniques for efficient storage and search.
from sklearn.neighbors import NearestCentroid import numpy as np X = np.array( [-1, -1 » , -2, -1 » , -3, -2 » , 1, 1 » , 2, 1 » , 3, 2 » ]) y = np.array( 1, 1, 1, 2, 2, 2 » ) clf = NearestCentroid() clf.fit(X, y) NearestCentroid()
print(clf.predict( [-0.8, -1 » ])) 1 »
In your example if you use PCA to initialize your t-SNE you get widely spaced centroids; if you use random initialization you'll get tiny centroids and an uninteresting picture.
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