e5-large-v2

Maintainer: intfloat

Total Score

188

Last updated 5/19/2024

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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 e5-large-v2 model is a text embedding model developed by intfloat. It is part of the E5 family of text embedding models, which are designed for tasks like passage retrieval, semantic similarity, and paraphrase detection. The e5-large-v2 model has 24 layers and an embedding size of 1024, making it a larger and more powerful version compared to the e5-base-v2 and e5-small-v2 models.

The model was pre-trained using a weakly-supervised contrastive learning approach on a variety of datasets, including filtered mC4, CC News, NLLB, Wikipedia, Reddit, S2ORC, Stackexchange, and xP3. It was then fine-tuned on supervised datasets like MS MARCO, NQ, Trivia QA, and others. This combination of pre-training and fine-tuning helps the model capture both general and task-specific text understanding capabilities.

Compared to the similar e5-large model, the e5-large-v2 has been updated with better performance. Users are recommended to switch to the e5-large-v2 model going forward.

Model inputs and outputs

Inputs

  • Text: The model accepts text inputs that should be prefixed with either "query: " or "passage: " depending on the task. For tasks other than retrieval, the "query: " prefix can be used.

Outputs

  • Text embeddings: The model outputs fixed-size vector representations (embeddings) of the input text. These embeddings can be used for a variety of downstream tasks like text retrieval, semantic similarity, and clustering.

Capabilities

The e5-large-v2 model is capable of generating high-quality text embeddings that capture the semantic meaning of the input text. These embeddings can be used for tasks like passage retrieval, where the model can find the most relevant passages given a query, or for semantic similarity, where the model can identify texts with similar meanings. The model's performance has been benchmarked on datasets like BEIR and MTEB, where it has shown strong results.

What can I use it for?

The e5-large-v2 model can be used for a variety of natural language processing tasks that involve text understanding and representation. Some potential use cases include:

  • Information retrieval: Use the model to find the most relevant passages or documents given a query, for applications like open-domain question answering or enterprise search.
  • Semantic similarity: Leverage the model's text embeddings to identify similar texts, for applications like paraphrase detection or document clustering.
  • Text classification: Use the model's embeddings as features for training custom text classification models, for applications like sentiment analysis or topic classification.

Things to try

One interesting aspect of the e5-large-v2 model is the way it handles the input text prefixes. The model is specifically trained to expect "query: " and "passage: " prefixes, even for non-retrieval tasks. This can help the model better capture the relationship between the query and passage, leading to improved performance.

You can experiment with different ways of using these prefixes, such as using "query: " for symmetric tasks like semantic similarity, or using the prefixes even when using the embeddings as features for other downstream models. The model's performance may vary depending on the specific task and dataset, so it's worth trying out different approaches to see what works best.



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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Total Score

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