AugSBERT: Domain Transfer for Sentence Transformers
🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp

When building language models, we can spend months optimizing training and model parameters, but it’s useless if we don't have the correct data.

The success of our language models relies first and foremost on data. The augmented SBERT training strategy can help us.

Given this scenario, we can transfer information from an out-of-domain (or *source*) dataset to our target domain. We will learn how to do this here. First, we will learn to assess which source datasets align best with our target domain quickly. Then we will explain and work through the AugSBERT domain-transfer training strategy.

🌲 Pinecone article:
https://www.pinecone.io/learn/augsbert-domain-transfer/

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👾 Discord:
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🔗 n-gram Similarity Script: https://gist.github.com/jamescalam/b73f37017ae32bd6094747c4b0fca94a
🔗 AugSBERT In-Domain Article: https://www.pinecone.io/learn/data-augmentation/

00:00 Why Use Domain Transfer
04:08 Strategy Outline
06:05 Train Source Cross-Encoder
12:44 Cross-Encoder Outcome
15:12 Labeling Target Data
20:31 Training Bi-encoder
23:58 Evaluator Bi-encoder Performance
28:08 Final Points