all-MiniLM-L6-v2
Maintainer: sentence-transformers
1.8K
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| 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 all-MiniLM-L6-v2 is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space. This model can be used for tasks like clustering or semantic search. It was fine-tuned on a large dataset of over 1 billion sentence pairs using a contrastive learning objective.
Similar models include the all-MiniLM-L12-v2, which has a deeper 12-layer architecture, and the all-mpnet-base-v2, which has a 768-dimensional output.
Model inputs and outputs
Inputs
- Text input, such as a single sentence or short paragraph
Outputs
- A 384-dimensional vector representation of the input text
Capabilities
The all-MiniLM-L6-v2 model is capable of encoding text into a dense vector space that captures semantic information. This allows it to be used for tasks like semantic search, where you can find relevant documents for a given query, or clustering, where you can group similar text together.
What can I use it for?
The all-MiniLM-L6-v2 model can be useful for a variety of natural language processing tasks that involve understanding the meaning of text. Some potential use cases include:
- Semantic search: Use the model to encode queries and documents, then find the most relevant documents for a given query by computing cosine similarity between the query and document embeddings.
- Text clustering: Cluster documents or sentences based on their vector representations to group similar content together.
- Recommendation systems: Encode user queries or items (e.g., products, articles) into the vector space and use the distances between them to make personalized recommendations.
- Data augmentation: Generate new text samples by finding similar sentences in the vector space and making minor modifications.
Things to try
Some interesting things to try with the all-MiniLM-L6-v2 model include:
- Exploring the vector space: Visualize the vector representations of different text inputs to get a sense of how the model captures semantic relationships.
- Zero-shot classification: Use the model to encode text and labels, then classify new inputs by computing cosine similarity between the input and label embeddings.
- Multilingual applications: The model can be used for cross-lingual tasks by encoding texts in different languages into the same vector space.
- Probing the model's capabilities: Design targeted evaluation tasks to better understand the model's strengths and weaknesses in representing different types of semantic information.
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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