nomic-embed-text-v1.5-GGUF
Maintainer: nomic-ai
48
🏷️
| 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
nomic-embed-text-v1.5 is an improvement upon Nomic Embed that utilizes Matryoshka Representation Learning which gives developers the flexibility to trade off the embedding size for a negligible reduction in performance. The model can produce contextual embeddings of text that are useful for a variety of natural language processing tasks like information retrieval, text classification, and clustering.
Model inputs and outputs
Inputs
- Text: The model takes in text strings as input, with specific prefixes required for different use cases like search queries, documents, classifications, and clustering.
Outputs
- Embeddings: The model outputs fixed-size vector representations of the input text. The dimensionality of the embeddings can be adjusted from 64 to 768 dimensions, allowing for a tradeoff between size and performance.
Capabilities
The nomic-embed-text-v1.5 model leverages Matryoshka Representation Learning to produce high-quality text embeddings that maintain performance even as the embedding size is reduced. This makes it versatile for applications that have different requirements around embedding size and performance.
What can I use it for?
The nomic-embed-text-v1.5 model is well-suited for a variety of natural language processing tasks that require text embeddings, such as:
- Information retrieval: Use the embeddings to perform efficient nearest-neighbor search and ranking of documents or web pages in response to search queries.
- Text classification: Train classification models using the embeddings as input features to categorize text into different classes.
- Clustering: Group similar text documents together by clustering the embeddings.
The Nomic Embedding API provides an easy way to generate embeddings with this model, without the need to host or fine-tune it yourself.
Things to try
One interesting aspect of the nomic-embed-text-v1.5 model is the ability to adjust the embedding dimensionality. Try experimenting with different dimensionalities to see how it impacts the performance and size of your applications. The model maintains high quality even at lower dimensions like 128 or 256, which could be useful for mobile or edge deployments with memory constraints.
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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