multilingual-e5-base

Maintainer: intfloat

Total Score

193

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-base is a text embedding model developed by researcher intfloat. It is a 12-layer model with an embedding size of 768, initialized from the xlm-roberta-base model and further trained on a mixture of multilingual datasets. This model supports 100 languages, although performance may degrade for low-resource languages.

The model was trained in two stages. In the first stage, it underwent contrastive pre-training with weak supervision, using a 1 billion text pair dataset filtered from the mC4 corpus. In the second stage, it was fine-tuned on various labeled datasets, including MS MARCO, NQ, Trivia QA, NLI from SimCSE, ELI5, DuReader Retrieval, KILT Fever, KILT HotpotQA, SQuAD, Quora, and multilingual datasets like Mr. TyDi and MIRACL.

Similar models include the [object Object] model, which has 24 layers and a 1024 embedding size, as well as the xlm-roberta-base model, a multilingual BERT model pre-trained on 2.5TB of filtered CommonCrawl data.

Model Inputs and Outputs

Inputs

  • Text: The model accepts text inputs, which should start with either "query: " or "passage: " prefixes, even for non-English texts. For tasks other than retrieval, you can simply use the "query: " prefix.

Outputs

  • Text embeddings: The model outputs 768-dimensional text embeddings that capture the semantic information of the input text. These embeddings can be used for a variety of downstream tasks, such as text retrieval, semantic similarity, and classification.

Capabilities

The multilingual-e5-base model can be used for a wide range of text-to-text tasks, thanks to its multilingual and robust text encoding capabilities. It has shown strong performance on benchmark tasks like passage ranking, as evidenced by its high MRR@10 scores on the Mr. TyDi dataset, outperforming baselines like BM25 and mDPR.

What can I use it for?

The multilingual-e5-base model can be used for a variety of applications, such as:

  • Information Retrieval: The model can be used to encode queries and passages for passage ranking tasks, enabling cross-lingual and multilingual information retrieval.
  • Semantic Similarity: The text embeddings produced by the model can be used to compute semantic similarity between text inputs, which can be useful for tasks like duplicate detection, paraphrase identification, and clustering.
  • Text Classification: The model's text embeddings can be used as features for training text classification models, such as topic classification or sentiment analysis.

Things to try

One interesting aspect of the multilingual-e5-base model is its ability to handle non-English texts. Try experimenting with inputs in various languages and observe how the model performs. You can also explore the model's performance on different downstream tasks, such as cross-lingual question answering or multilingual document retrieval, to better understand its capabilities.

Another interesting experiment would be to compare the performance of the multilingual-e5-base model to the larger multilingual-e5-large model, or to the xlm-roberta-base model, to see how the model size and training data impact the results on 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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The e5-base-v2 model is a text embedding model developed by the researcher intfloat. This model has 12 layers and an embedding size of 768, and was trained using a novel technique called "Text Embeddings by Weakly-Supervised Contrastive Pre-training". The model can be used for a variety of text-related tasks, and compares favorably to similar models like the e5-large and multilingual-e5-base models. Model inputs and outputs The e5-base-v2 model takes in text inputs and outputs text embeddings. The embeddings can be used for a variety of downstream tasks such as passage retrieval, semantic similarity, and text classification. Inputs Text inputs, which can be either "query: " or "passage: " prefixed Outputs Text embeddings, which are 768-dimensional vectors Capabilities The e5-base-v2 model is capable of producing high-quality text embeddings that can be used for a variety of tasks. The model was trained on a large, diverse corpus of text data, and has been shown to perform well on a number of benchmarks, including the BEIR and MTEB benchmarks. What can I use it for? The e5-base-v2 model can be used for a variety of text-related tasks, including: Passage retrieval**: The model can be used to retrieve relevant passages given a query, which can be useful for building search engines or question-answering systems. Semantic similarity**: The model can be used to compute the semantic similarity between two pieces of text, which can be useful for tasks like paraphrase detection or document clustering. Text classification**: The model's embeddings can be used as features for training text classification models, which can be useful for a variety of applications like sentiment analysis or topic modeling. Things to try One interesting thing to try with the e5-base-v2 model is to explore the different training datasets and techniques used to create the model. The paper describing the model provides details on the weakly-supervised contrastive pre-training approach, which is a novel technique that could be worth exploring further. Another interesting avenue to explore is the model's performance on different benchmarks and tasks, particularly in comparison to similar models like the e5-large and multilingual-e5-base models. Understanding the strengths and weaknesses of each model could help inform the choice of which model to use for a particular application.

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Total Score

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The multilingual-e5-large-instruct model is a large-scale multilingual text embedding model developed by the team at intfloat. This model is an extension of the multilingual-e5-large model, with additional fine-tuning on instructional datasets to enable more versatile text understanding and generation capabilities. The model has 24 layers and an embedding size of 1024, and is initialized from the xlm-roberta-large model. It is then continuously trained on a diverse set of multilingual datasets, including web content, news, translated text, and task-oriented data, to develop robust cross-lingual text representations. Compared to the base multilingual-e5-large model, the multilingual-e5-large-instruct version incorporates additional fine-tuning on instructional datasets, allowing it to better understand and generate task-oriented text. 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