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Deconstructing the text embedding models

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Deconstructing the text embedding models
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CC Attribution - NonCommercial - ShareAlike 3.0 Unported:
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Selecting the optimal text embedding model is often guided by benchmarks such as the Massive Text Embedding Benchmark (MTEB). While choosing the best model from the leaderboard is a common practice, it may not always align perfectly with the unique characteristics of your specific dataset. This approach overlooks a crucial yet frequently underestimated element - the tokenizer. We will delve deep into the tokenizer's fundamental role, shedding light on its operations and introducing straightforward techniques to assess whether a particular model is suited to your data based solely on its tokenizer. We will explore the significance of the tokenizer in the fine-tuning process of embedding models and discuss strategic approaches to optimize its effectiveness.