multilingual-e5-small

Maintainer: intfloat

Total Score

93

Last updated 5/28/2024

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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 multilingual-e5-small model is a text embedding model developed by intfloat. It is a smaller version of the larger multilingual-e5 models, with 12 layers and an embedding size of 384. The model is based on the Multilingual MiniLM and has been continually trained on a mixture of multilingual datasets to support 100 languages, although low-resource languages may see performance degradation.

The multilingual-e5-base and multilingual-e5-large models are larger versions of the multilingual-e5-small model, with 12 and 24 layers respectively, and embedding sizes of 768 and 1024. These larger models leverage the XLM-RoBERTa and XLM-RoBERTa-Large initializations and further training on a variety of multilingual datasets.

The multilingual-e5-large-instruct model is an even larger version with 24 layers and a 1024 embedding size. It is initialized from XLM-RoBERTa-Large and fine-tuned on various datasets, including some that provide task-specific instructions to the model.

Model inputs and outputs

Inputs

  • Text: The input text should start with either "query: " or "passage: ", even for non-English text. This is how the model was trained, and using the correct prefix is important for optimal performance.

Outputs

  • Text embeddings: The model outputs text embeddings, which are high-dimensional vector representations of the input text. These embeddings can be used for a variety of downstream tasks, such as semantic similarity, information retrieval, and text classification.

Capabilities

The multilingual-e5 models excel at multilingual text understanding and retrieval tasks. They have been shown to outperform other popular multilingual models like mDPR and BM25 on the Mr. TyDi benchmark, a multilingual question answering and passage retrieval dataset.

The multilingual-e5-large-instruct model further extends the capabilities of the multilingual-e5 models by allowing for customization through natural language instructions. This can be useful for tailoring the text embeddings to specific tasks or scenarios.

What can I use it for?

The multilingual-e5 models are well-suited for a variety of text-based applications that require multilingual support, such as:

  • Information retrieval: Use the text embeddings for semantic search and ranking of web pages, documents, or passages in response to user queries.
  • Question answering: Leverage the models for finding relevant passages that answer a given question, across multiple languages.
  • Text classification: Use the text embeddings as features for training classification models on multilingual datasets.
  • Semantic similarity: Calculate the similarity between text pairs, such as for paraphrase detection or bitext mining.

The multilingual-e5-large-instruct model can be particularly useful for applications that benefit from customized text embeddings, such as specialized search engines, personal assistants, or chatbots.

Things to try

One interesting aspect of the multilingual-e5 models is the use of a low temperature (0.01) for the InfoNCE contrastive loss during training. This results in the cosine similarity scores of the text embeddings being distributed around 0.7 to 1.0, rather than the more typical range of -1 to 1.

While this may seem counterintuitive at first, it's important to note that for tasks like text retrieval or semantic similarity, what matters is the relative order of the scores rather than the absolute values. The low temperature helps to amplify the differences between similar and dissimilar text pairs, which can be beneficial for these types of applications.

You can experiment with this behavior and see how it affects the performance of your specific use case.



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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