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Today Unsupervised Sentence Transformers, Tomorrow Skynet (how TSDAE works)

To adapt a pretrained transformer to produce meaningful sentence vectors, we typically need a more supervised fine-tuning approach. We can use datasets like natural language inference (NLI) pairs, labeled semantic textual similarity (STS) data, or parallel data (pairs of translations). For some domains and languages, such as finance and English, this data is fairly easy to find or gather. But many domains and many languages have very little labeled data. If you can find semantic similarity pairs for the agriculture industry, please let me know. There are many languages, such as Dhivehi, where unlabelled data is hard to find and labelled data practically non-existent. This means you either spend a very long time gathering tens of thousands of labeled samples or you can try an unsupervised fine-tuning approach. Unsupervised training methods for sentence transformers are not as effective as their supervised counterparts, but they do work. And if you have no other choice, why not? In this video, we will introduce the concept of unsupervised fine-tuning for sentence transformers. We will learn to train these models using the unsupervised Transformer-based Sequential Denoising Auto-Encoder (TSDAE) approach. 🌲 Pinecone article: https://www.pinecone.io/learn/unsuper... 🤖 70% Discount on the NLP With Transformers in Python course: https://bit.ly/3DFvvY5 🎉 Subscribe for Article and Video Updates!   / subscribe     / membership   👾 Discord:   / discord   00:00 Why Language Embedding Matters 05:12 Supervised Methods 05:29 Natural Language Inference 07:15 Semantic Textual Similarity 07:43 Multilingual Training 10:00 TSDAE (Unsupervised) 18:50 Data Preparation 29:05 Initialize Model 32:39 Model Training 36:25 NLTK Error 37:15 Evaluation 41:01 TSDAE vs Supervised Methods 42:42 Why TSDAE is Cool