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dual-encoder

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ksm26 / Embedding-Models-From-Architecture-to-Implementation

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Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.

machine-learning word2vec word-embeddings embedding-models semantic-search bert model-training sentence-embeddings contrastive-loss dual-encoder bert-model bert-embeddings transformer-models bert-fine-tuning ai-applications ai-architecture rag-systems question-answer-retrieval
  • Updated Aug 21, 2024
  • Jupyter Notebook

Jonathan-Greif / QBV

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This repository provides the code for "Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretraining", presented at DCASE 2024. The paper addresses the challenge of audio retrieval using vocal imitations as queries, proposing a dual encoder architecture that leverages pretrained CNNs and an adapted NT-Xent loss for fine-tuning.

dual-encoder contrastive-learning audio-retrieval vocal-imitation qbv
  • Updated Oct 25, 2024
  • Python

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