multi-qa-mpnet-base-dot-v1
Maintainer: sentence-transformers
139
🖼️
| 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-mpnet-base-dot-v1 model is a sentence-transformers model that maps sentences and paragraphs to a 768-dimensional dense vector space. It was designed for semantic search tasks and trained on 215M (question, answer) pairs from diverse sources. This model can be compared to similar sentence-transformers models like all-mpnet-base-v2 and paraphrase-multilingual-mpnet-base-v2, which also aim to encode text into semantic representations.
Model inputs and outputs
Inputs
- Text: The model takes text input, either a single sentence or a paragraph.
Outputs
- Sentence embedding: The model outputs a 768-dimensional dense vector representation of the input text that captures its semantic meaning.
Capabilities
The multi-qa-mpnet-base-dot-v1 model is capable of generating semantic embeddings of text that can be used for tasks like semantic search, clustering, and similarity scoring. The model's training on a large corpus of question-answer pairs gives it strong performance on question answering and retrieval tasks.
What can I use it for?
The semantic embeddings produced by the multi-qa-mpnet-base-dot-v1 model can be used in a variety of downstream applications. For example, you could use it to build a semantic search engine, where you encode user queries and document content, and then retrieve the most relevant documents based on cosine similarity. You could also use the embeddings as features for text classification or clustering tasks.
Things to try
One interesting thing to try with this model is to compare its performance on question answering tasks to other similar models like all-mpnet-base-v2 and paraphrase-multilingual-mpnet-base-v2. You could also experiment with different pooling strategies (e.g. mean, max, CLS token) to see how they affect the model's performance on your specific task.
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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