multi-qa-MiniLM-L6-cos-v1
Maintainer: sentence-transformers
102
👨🏫
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The multi-qa-MiniLM-L6-cos-v1 is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. It was designed for semantic search, and has been trained on 215M (question, answer) pairs from diverse sources. Similar models include multi-qa-mpnet-base-dot-v1, which maps sentences to a 768-dimensional space, and all-MiniLM-L12-v2, a 384-dimensional model trained on over 1 billion sentence pairs.
Model inputs and outputs
Inputs
- Text input, such as a sentence or paragraph
Outputs
- A 384-dimensional dense vector representation of the input text
Capabilities
The multi-qa-MiniLM-L6-cos-v1 model is capable of encoding text into a semantic vector space, where documents with similar meanings are placed closer together. This allows it to be used for tasks like semantic search, where the model can find the most relevant documents for a given query.
What can I use it for?
The multi-qa-MiniLM-L6-cos-v1 model is well-suited for building semantic search applications, where users can search for relevant documents or passages based on the meaning of their queries, rather than just keyword matching. For example, you could use this model to build a FAQ search system, where users can find the most relevant answers to their questions.
Things to try
One interesting thing to try with this model is to use it as a feature extractor for other NLP tasks, such as text classification or clustering. The semantic vector representations produced by the model can provide powerful features that capture the meaning of the text, which may improve the performance of downstream models.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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