m3e-large

Maintainer: moka-ai

Total Score

185

Last updated 5/28/2024

🛸

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The m3e-large model is part of the M3E (Moka Massive Mixed Embedding) series of text embedding models developed by the Moka AI team. The M3E models are large-scale multilingual text embedding models that can be used for a variety of natural language processing tasks. The m3e-large model is the largest in the series, with 340 million parameters and a 768-dimensional embedding size.

The M3E models are designed to provide strong performance on a range of benchmarks, including the MTEB-zh Chinese language benchmark. Compared to similar models like multilingual-e5-large, bge-large-en-v1.5, and moe-llava, the M3E models leverage a massive, mixed-domain training dataset to learn rich and generalizable text representations.

The m3e-base model in this series has also shown strong performance, outperforming OpenAI's text-embedding-ada-002 model on several MTEB-zh tasks.

Model inputs and outputs

Inputs

  • Text sequences: The m3e-large model can accept single sentences or longer text passages as input.

Outputs

  • Text embeddings: The model outputs fixed-length vector representations (embeddings) of the input text. These embeddings can be used for a variety of downstream tasks, such as semantic search, text classification, and clustering.

Capabilities

The m3e-large model demonstrates strong performance on a variety of text-based tasks, especially those involving semantic understanding and retrieval. For example, it has achieved a 0.6231 accuracy score on the sentence-to-sentence (s2s) task and a 0.7974 NDCG@10 score on the sentence-to-passage (s2p) task in the MTEB-zh benchmark.

What can I use it for?

The m3e-large model can be used for a wide range of natural language processing applications, such as:

  • Semantic search: The rich text embeddings produced by the model can be used to build powerful semantic search engines, allowing users to find relevant information based on the meaning of their queries rather than just keyword matching.

  • Text classification: The model's embeddings can be used as features for training high-performance text classification models, such as those for sentiment analysis, topic categorization, or intent detection.

  • Recommendation systems: The semantic understanding of the m3e-large model can be leveraged to build advanced recommendation systems that suggest relevant content or products based on user preferences and behavior.

Things to try

One interesting aspect of the m3e-large model is its potential for domain-specific fine-tuning. By further training the model on task-specific data using tools like the uniem library, you can likely achieve even stronger performance on specialized applications.

Additionally, the model's large size and diverse training data make it a promising starting point for exploring few-shot and zero-shot learning approaches, where the model can leverage its broad knowledge to quickly adapt to new tasks with limited additional training.



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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