nomic-embed-text-v1.5
Maintainer: nomic-ai
204
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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
nomic-embed-text-v1.5 is an improvement upon Nomic Embed that utilizes Matryoshka Representation Learning. This gives developers the flexibility to trade off the embedding size for a negligible reduction in performance. The model can generate embeddings with varying dimensionality, allowing users to choose the size that best fits their use case.
The model is developed by Nomic AI, a prominent AI research company. Similar text embedding models include bge-large-en-v1.5, gpt4all-13b-snoozy, and gpt4all-j.
Model inputs and outputs
Inputs
- Text sequences: The model can process text sequences up to 8192 tokens in length.
Outputs
- Text embeddings: The model outputs text embeddings of varying dimensionality, ranging from 64 to 768 dimensions. Users can choose the size that best fits their use case, with a tradeoff between performance and embedding size.
Capabilities
The nomic-embed-text-v1.5 model can generate high-quality text embeddings that capture the semantic meaning of input text. These embeddings can be used for a variety of downstream natural language processing tasks, such as text similarity, document retrieval, and text classification.
What can I use it for?
The flexible embedding size of nomic-embed-text-v1.5 makes it suitable for a wide range of applications. For example, users can use the 768-dimensional embeddings for high-performance tasks, or the smaller 128-dimensional embeddings for memory-constrained environments. The model can be used for tasks like:
- Semantic search: Find relevant documents or content based on query embeddings.
- Recommendation systems: Recommend similar content or products based on user preferences.
- Text classification: Categorize text into predefined classes using the embeddings.
- Multimodal applications: Combine the text embeddings with other modalities, such as images or audio, for tasks like image-text retrieval.
Things to try
One interesting aspect of nomic-embed-text-v1.5 is the ability to trade off embedding size and performance. Users can experiment with different dimensionalities to find the right balance for their specific use case. For example, you could try generating embeddings at 256 dimensions and evaluate the performance on your task, then compare it to the 128-dimension or 512-dimension versions to see how the size-performance tradeoff plays out.
Another thing to try is using the model in conjunction with other Nomic AI models, such as gpt4all-13b-snoozy or gpt4all-j, to create more advanced natural language processing pipelines.
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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