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Update models-on-hugging-face@dLEg4IA3F5jgc44Bst9if.md (#8978)

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* Update models-on-hugging-face@dLEg4IA3F5jgc44Bst9if.md

Added overview of embedding models on Hugging Face

- Added a concise summary explaining what embeddings are and their common use cases
- Listed popular embedding models like MiniLM, GTE, E5, and BGE
- Included an official Hugging Face video on text embeddings
- Linked to the full list of embedding models available on Hugging Face Hub

* Fix formatting issues in Hugging Face embedding models content

- Add missing blank line after title (follows established pattern)
- Fix grammatical error: remove duplicated 'which' and em dash
- Maintain original content structure and meaning

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

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Co-authored-by: Kamran Ahmed <kamranahmed.se@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
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2025-08-07 19:27:26 +03:00
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# Models on Hugging Face
# Models on Hugging Face
Embedding models are used to convert raw data like text, code, or images into high-dimensional vectors that capture semantic meaning. These vector representations allow AI systems to compare, cluster, and retrieve information based on similarity rather than exact matches. Hugging Face provides a wide range of pretrained embedding models such as `all-MiniLM-L6-v2`, `gte-base`, `Qwen3-Embedding-8B` and `bge-base` which are commonly used for tasks like semantic search, recommendation systems, duplicate detection, and retrieval-augmented generation (RAG). These models can be accessed through libraries like transformers or sentence-transformers, making it easy to generate high-quality embeddings for both general-purpose and task-specific applications.
Learn more from the following resources:
- [@video@Hugging Face - Text embeddings & semantic search](https://www.youtube.com/watch?v=OATCgQtNX2o)
- [@official@Hugging Face Embedding Models](https://huggingface.co/models?pipeline_tag=feature-extraction)