m2-bert-80M-32k-retrieval
Maintainer: togethercomputer
117
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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 m2-bert-80M-32k-retrieval model, developed by Together Computer, is an 80 million parameter checkpoint of M2-BERT that has been pretrained with a sequence length of 32,768 and fine-tuned for long-context retrieval tasks. This model builds upon the Monarch Mixer architecture, which aims to improve upon the standard BERT model for handling long sequences.
Similar models include the all-mpnet-base-v2 from the Sentence-Transformers library, which maps sentences and paragraphs to a 768-dimensional vector space for tasks like clustering and semantic search, and the LLaMA-2-7B-32K model, which also extends the context length to 32,768 tokens.
Model inputs and outputs
Inputs
- Text: The model can take in single sentences or longer passages of text up to 32,768 tokens in length.
Outputs
- Sentence embeddings: The model generates 768-dimensional vector representations of the input text, which can be used for tasks like retrieval, clustering, or similarity search.
Capabilities
The m2-bert-80M-32k-retrieval model is particularly well-suited for long-context tasks that require understanding and relating large amounts of text. Its extended 32,768 token context length allows it to capture and leverage relationships between distant parts of a document or corpus.
What can I use it for?
This model can be useful for applications that involve searching, ranking, or clustering large text corpora, such as academic papers, book chapters, or long-form web content. The long-context embeddings it generates could power semantic search engines, content recommendation systems, or document organization tools.
Things to try
One interesting aspect of this model is its ability to handle very long input sequences. You could experiment with feeding it excerpts from novels, technical manuals, or other long-form content and see how the model's understanding and representations of the text evolve as the context length increases. This could provide insights into the model's reasoning and help identify its strengths and limitations for real-world applications.
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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