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- Language Embedding Theory
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Classroom Contents
The Magic of Multilingual Search with Pinecone Serverless and Inference
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- 1 - Introduction
- 2 - Tatoeba and Multilingual Semantic Search
- 3 - What is Multilingual Semantic Search?
- 4 - Applications of Multilingual Semantic Search
- 5 - How do we achieve multilingual semantic search?
- 6 - A Crash Course in LLMs
- 7 - What are Vectors and Vector Embeddings?
- 8 - Distributional Hypothesis
- 9 - What are LLMs anyway?
- 10 - How does XLM-RoBERTA work?
- 11 - XLM-R: Big Multilingual Datasets
- 12 - XLM-R: Tokenization
- 13 - XLM-R: Masked Language Modeling
- 14 - Getting Doc embeddings
- 15 - Why XLM-R Isn't Enough
- 16 - Multilingual E5 for Multilingual Search Embeddings
- 17 - mE5: Training Data
- 18 - mE5: Weakly Supervised Contrastive Pretraining
- 19 - mE5: Supervised Finetuning and Dataset Distribution
- 20 - Basics of Vector Search with Pinecone
- 21 - Using Pinecone Inference
- 22 - Querying with Pinecone
- 23 - Demo Time: Language Learning with Multilingual Semantic Search
- 24 - Demo Architecture
- 25 - Live walkthrough of Notebook
- 26 - Embedding with Pinecone Inference
- 27 - Batch Embedding and Upsertion
- 28 - Query Embeddings, and cross-lingual search
- 29 - Tips and Tricks for Multilingual Semantic Search
- 30 - QA Time
- 31 - Evaluating Semantic Search
- 32 - Language Embedding Theory
- 33 - What happens for Out of Domain Languages? Transfer Theory
- 34 - Why isn't Translation Sufficient?
- 35 - Handling Negation in Queries
- 36 - Handling Cultural Nuance
- 37 - Low Resource Languages
