roytalman/Deep_contrastive_embedding
Deep supervised conistrastive learning for small datasets (few shot learning). This repository takes labeled embedding data ,that could be extracted from pre-trained NLP, vision, or any other algorithm that extract embedding, and use deep FFN to learn new embedding that is fine-tuned for the current data. Th algorithm can improve classification per
This tool helps data scientists and machine learning engineers improve the accuracy of classification models, especially when working with very small datasets. You provide existing labeled data embeddings (from sources like NLP or computer vision models), and it outputs refined embeddings that are better optimized for your specific classification task. This is ideal for fine-tuning models on limited data.
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Use this if you need to boost the performance of a classification model when you only have a small number of labeled examples for your categories.
Not ideal if you are looking for a tool to extract initial embeddings from raw data (text, images, etc.) or if you have a very large, well-labeled dataset.
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Last pushed
Aug 24, 2024
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