Text Embeddings and Semantic Search with Transformer Models
Explore how Transformer models generate vector representations of documents and queries, enabling effective semantic search and information retrieval.
HuggingFace
50.2K views • Nov 15, 2021
About this video
Learn how Transformer models can be used to represent documents and queries as vectors called embeddings. In this video, we apply this technique to create a semantic search engine!
This video is part of the Hugging Face course: http://huggingface.co/course
Open in colab to run the code samples:
https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/videos/semantic_search.ipynb
Related videos:
- Loading a custom dataset — https://youtu.be/HyQgpJTkRdE
- Slide and dice a dataset 🔪 — https://youtu.be/tqfSFcPMgOI
Don't have a Hugging Face account? Join now: http://huggingface.co/join
Have a question? Checkout the forums: https://discuss.huggingface.co/c/course/20
Subscribe to our newsletter: https://huggingface.curated.co/
This video is part of the Hugging Face course: http://huggingface.co/course
Open in colab to run the code samples:
https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/videos/semantic_search.ipynb
Related videos:
- Loading a custom dataset — https://youtu.be/HyQgpJTkRdE
- Slide and dice a dataset 🔪 — https://youtu.be/tqfSFcPMgOI
Don't have a Hugging Face account? Join now: http://huggingface.co/join
Have a question? Checkout the forums: https://discuss.huggingface.co/c/course/20
Subscribe to our newsletter: https://huggingface.curated.co/
Video Information
Views
50.2K
Likes
837
Duration
3:30
Published
Nov 15, 2021
User Reviews
4.7
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