- 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
Description:
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Explore the intricacies of multilingual search and learn how to leverage Pinecone Inference and Serverless for building multilingual applications in this 54-minute talk by Arjun Patel, Developer Advocate at Pinecone. Dive into the use of multilingual embedding models, understand the benefits of multilingual models and Pinecone Inference in search applications, and witness a practical language learning demo involving cross-lingual and mono-lingual search. Gain insights into topics such as vector embeddings, LLMs, XLM-RoBERTA, Multilingual E5, and vector search basics with Pinecone. The presentation covers advanced concepts like weakly supervised contrastive pretraining, supervised finetuning, and handling cultural nuances in multilingual semantic search. Includes a Q&A session addressing evaluation methods, out-of-domain languages, and challenges with low-resource languages.
The Magic of Multilingual Search with Pinecone Serverless and Inference