保留正文与补采附件。源页面当前未重新核验；具体限制见交付清单。

Revisit consent button We value your privacy We use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking "Accept All", you consent to our use of cookies. Customise Reject All Accept All Customise Consent Preferences We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The cookies that are categorised as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more NecessaryAlways Active Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

Analytics Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

Performance Performance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.

Advertisement Advertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.

Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle SearchToggle Mobile Navigation Publish AI, ML & data-science insights to a global community of data professionals. Sign inSubmit an Article LatestEditor's PicksNewsletterShipAI

Write For TDS LinkedInXToggle Search Artificial Intelligence

How to Find the Best Multilingual Embedding Model for Your RAG

Optimize the Embedding Space for Improving RAG Iulia Brezeanu January 27, 20248 min read Image by author. AI generated. Image by author. AI generated. Embeddings are vector representations that capture the semantic meaning of words or sentences. Besides having quality data, choosing a good embedding model is the most important and underrated step for optimizing your RAG application. Multilingual models are especially challenging as most are pre-trained on English data. The right embeddings make a huge difference - don't just grab the first model you see! The semantic space determines the relationships between words and concepts. An accurate semantic space improves retrieval performance. Inaccurate embeddings lead to irrelevant chunks or missing information. A better model directly improves your RAG system's capabilities. In this article, we will create a question-answer dataset from PDF documents in order to find the best model for our task and language. During RAG, if the expected answer is retrieved, it means the embedding model positioned the question and answer close enough in the semantic space. While we focus on French and Italian, the process can be adapted to any language because the best embeddings might differ. Learn this step by step with the interactive AI Engineer and AI and Data Scientist roadmaps.

Embedding Models

There are two main types of embedding models: static and dynamic. Static embeddings like word2vec generate a vector for each word. The vectors are combined, often by averaging, to create a final embedding. These types of embeddings are not often used in production anymore because they don't consider how a word's meaning can change in function to the surrounding words. Dynamic embeddings are based on Transformers like BERT, which incorporate context awareness through self-attention layers, allowing them to represent words based on the surrounding context. Most current fine-tuned models use contrastive learning. The model learns semantic similarity by seeing both positive and negative text pairs during training. Process of optimizing the Embedding Space. Image by author. Process of optimizing the Embedding Space. Image by author. In an accurate semantic space, words, and phrases with similar meanings are close together, while contradicting ones are far apart. Below, you can see a two-dimensional PCA of sentences embedded using bge-base-en-v1.5-angle. The original embeddings had 768 dimensions, and PCA reduced it to 2 dimensions. Example of 2D PCA of Sentence Embeddings. Image by author. Example of 2D PCA of Sentence Embeddings. Image by author. The graph demonstrates how the semantic space organizes sentences with related meanings close together. Note how the sentences That new sports car is sick! and I feel sick , which have positive and negative connotations of the word sick , are positioned far apart. The embedding model understands the meaning of the word from the surrounding context. During RAG, the retrieval mechanism uses semantic similarity to identify documents close to the user query. So, an inaccurate semantic space that causes irrelevant documents to be in the vicinity of the query would lead to poor answers. The wide range of pre-trained transformers has created many possible text embedding models to explore. It's not easy to determine the optimal model for specific needs.

Massive Text Embedding Benchmark

An overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. 3 An overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. [3] Massive Text Embedding Benchmark (MTEB) evaluates embedding models across 8 tasks and 58 datasets. 10 of these datasets are multilingual, covering 112 languages. The tasks are bitext mining, classification, clustering, pair classification, reranking, retrieval, semantic textual similarity (STS), and summarization. We focus only on the retrieval task because it is the most relevant for RAG. In order to evaluate it, each dataset has a corpus, queries, and query-document mappings. The queries and corpus are embedded to find relevant documents using cosine similarity. Model performance is measured by Normalized Discounted Cumulative Gain at 10 (nDCG@10). This metric accounts for the position of relevant documents in the ranking. We won't go into too much detail about nDCG, but we recommend this in-depth article if you want to read more. Unfortunately, the initial MTEB retrieval datasets were only in English. Recently, some were added in Polish and Swedish, but multilingual support is still limited [2]. MTEB Leaderboard 1 MTEB Leaderboard [1] We will evaluate the current top 5 multilingual embedding models from the MTEB Leaderboard against a baseline Sentence Transformers model. This analysis will reveal which models perform best for retrieval across French and Italian, but you can customize the evaluation easily for any language. First, let's look at some key model properties: sequence length and embedding dimensions. The sequence length refers to the maximum number of tokens the model can process at once. Anything longer than the context window gets truncated. The embedding dimension refers to the vector size. Larger vectors can capture more meaning from sentences but may be less storage-efficient. Comparison of Embedding Models Specifications. Image by author. Comparison of Embedding Models Specifications. Image by author.

Cohere-embed-multilingual-v3.0 & light-v3.0

Cohere offers a proprietary embedding model accessible through an API at the cost of $0.10/1M tokens, which is the same price as for ada-002. Besides embedding, the model can rank the most relevant documents at the top during retrieval by assessing how well a query matches a document's topic. They also implemented compression-aware training for more efficient storage [4]. The light version of the multilingual Cohere model has an embedding dimension of only 384 [7].

intfloat/multilingual-e5-large

The E5 family of models was trained using weak supervision in a contrastive manner using the CCPair dataset [5]. The dataset was created by extracting text pairs from various sources, including Reddit, StackExchange, Wikipedia, scientific papers, Common Crawl, and news articles. The authors compiled text pairs consisting of a query q and a corresponding passage p , as shown below. CCPairs Colossal Clean text Pairs 5 CCPairs (Colossal Clean text Pairs) [5]

text-embedding-3-large

OpenAI claims that this is their most performant model with higher multilingual performance than ada-002. An interesting improvement is adding the possibility of reducing the embedding dimensions from 3072 to as low as you want. While there is an accuracy-cost tradeoff associated with lower dimensionality, having the flexibility to use smaller embeddings can provide helpful savings on memory and storage requirements [6].

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

We consider this model as a baseline because it's one of the most downloaded Sentence Transformers models from Hugging Face. sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads 8 sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads [8] It is based on the SBERT architecture, which introduced the idea of a triple network structure. The network takes three inputs - an anchor, a positive example, and a negative example. Then, it is trained so that the anchor is closer to the positive than the negative example in the embedding space.

Generating QA pairs

In order to have as little bias between languages as possible, our chosen documents are the 2023 European Semester Country Reports for France and Italy [9]. They have the same number of pages and a very similar structure, being official reports from the European Commission. We provide each text chunk from the corpus as context to GPT-3.5-turbo and prompt it to generate a question based on that context. LLMs typically respond more accurately when both the prompt and the desired response are in the same language, so I translated the prompts into Italian and French. python

prompt_it = """
Le informazioni sul contesto sono riportate di seguito.

---------------------
{context_str}
---------------------

Date le informazioni sul contesto e non le conoscenze pregresse.
generare solo domande basate sulla domanda seguente.

Siete un insegnante/professore. Il vostro compito è quello di impostare 
{num_questions_per_chunk} per un prossimo quiz.
Le domande devono essere di natura diversa nel documento. 
Limitare le domande alle informazioni di contesto fornite. 
Le domande devono essere in italiano."
"""

prompt_fr = """
Les informations contextuelles se trouvent ci-dessous.

---------------------
{context_str}
---------------------

Compte tenu des informations contextuelles et sans connaissances préalables, générer uniquement des questions basées sur la requête ci-dessous.

Vous êtes enseignant/professeur. Votre tâche consiste à mettre en place 
{num_questions_per_chunk} pour un quiz à venir. 
Les questions doivent être de nature variée sur l'ensemble du document. 
Limitez les questions aux informations contextuelles fournies. 
Les questions doivent être en français."
"""

We already split the text into chunks of 1000 characters and ingested them into nodes, so now we go straight to creating the dataset. makefile

fr_dataset = generate_qa_embedding_pairs(
    llm=OpenAI(model="gpt-3.5-turbo"), nodes=fr_nodes, qa_generate_prompt_tmpl = prompt_fr
)
fr_dataset.save_json("fr_dataset.json")

it_dataset = generate_qa_embedding_pairs(
    llm=OpenAI(model="gpt-3.5-turbo"), nodes=it_nodes, qa_generate_prompt_tmpl = prompt_it
)

it_dataset.save_json("it_dataset.json")

The resulting dataset has 4 keys: queries, corpus, relevant_docs, and mode equal to text. Each query ID has an associated relevant document node ID. This is an example of a query from the Italian dataset and the corresponding text chunk. First query from the Italian dataset. Image by author. First query from the Italian dataset. Image by author.

Evaluation metrics

We'll retrieve the top 5 documents and evaluate their relevance using Hit Rate and MRR. The Hit Rate checks whether the expected relevant document is among the top 5. So, the overall value would be the proportion of queries for which we have a hit. MRR is calculated as the reciprocal of the rank at which the relevant document is found (1/rank). If the relevant document is not found, MRR is 0 for that query. The final MRR would be the average of the MRR values calculated for each query.

Models' performance

Models performance. Image by author. Models' performance. Image by author. The newest model from OpenAI achieved the top score, which is expected since it was also the highest-performing multilingual model on the MTEB benchmark. The second place was taken by another proprietary system from Cohere. Their normal and light versions had similar results on the French dataset, but the normal version greatly outperformed the light one on the Italian dataset. We also tried to reduce the embeddings of text-embedding-3-large to 256, and the performance was pretty impressive, considering the small dimension. The other embedding model from OpenAI, ada-002 , scored considerably lower than their newest one, showing that the update brought impressive improvements. Intfloat's open-source multilingual-e5-large is the best open-source model, with significantly higher scores on the Italian dataset than on the French one. The results from paraphrase-multilingual-MiniLM-L12-v2 are disappointing. While it was very fast, we had hoped for a better performance.

Conclusion

The MTEB leaderboard offers a good initial benchmark for evaluating multilingual models. However, in order to improve retrieval, it is better to customize the evaluation to your needs. As we've seen, model performance varies significantly across languages. That's why it is important to have a system to quickly evaluate embedding models using one's own documents. . . . If you enjoyed this article, join Text Generation - our newsletter has two weekly posts with the latest insights on Generative AI and Large Language Models. You can find the full code for this project on GitHub . You can also find me on LinkedIn . . . .

References

  1. https://huggingface.co/spaces/mteb/leaderboard
  2. https://github.com/embeddings-benchmark/mteb?tab=readme-ov-file
  3. MTEB: Massive Text Embedding Benchmark
  4. https://txt.cohere.com/introducing-embed-v3/
  5. https://huggingface.co/intfloat/multilingual-e5-large
  6. https://openai.com/blog/new-and-improved-embedding-model
  7. https://huggingface.co/Cohere/Cohere-embed-multilingual-light-v3.0
  8. https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  9. https://economy-finance.ec.europa.eu/publications/2023-european-semester-country-reports_en

Register Now to Continue Reading

Unlock Towards Data Science with a Free Account

All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account

Written By

Iulia Brezeanu

See all from Iulia Brezeanu Artificial IntelligenceData ScienceGenaiLarge Language ModelsLlm Share This Article

Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. Write for TDS

Related Articles

Demystifying hypothesis testing with simple Python examples

Programming Hypothesis testing is the bread and butter of data science. We demo the concept using simple real-life examples using Python. Tirthajyoti Sarkar September 6, 201910 min read

LangChain for EDA: Build a CSV Sanity-Check Agent in Python

Agentic AI A practical LangChain tutorial for data scientists to inspect CSVs Sarah Schürch September 9, 202517 min read

How to Build a Neural Machine Translation System for a Low-Resource Language

Machine Learning An introduction to neural machine translation Kaixuan Chen January 24, 202613 min read

Data-driven organization with managers on board

Data Science A possible approach to reconcile data scientists and management behind the same objective Alexandre Allouin September 19, 20227 min read

Building a comprehensive set of Technical Indicators in Python for quantitative trading

Machine Learning Customizable, comprehensive indicators for Machine-learning and statistical algorithms Modishubham October 21, 20203 min read

Capturing a Training State in TensorFlow

Artificial Intelligence How to debug occurrences of a NaN loss in your training project Chaim Rand March 9, 202213 min read

WBF: Optimizing object detection - Fusing & Filtering predicted boxes

Artificial Intelligence Weighted boxes fusion has become the new SOTA to optimize object detection models Mostafa Ibrahim March 17, 20216 min read

Data Visualization Explained (Part 3): The Role of Color

Data Visualization A simple and powerful guide to using color for more impactful data stories. Murtaza Ali October 8, 20257 min read

Outlier Detection with Isolation Forest

Machine Learning Learn how to efficiently detect outliers! Eryk Lewinson July 2, 20186 min read

Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe to Our NewsletterWrite For TDS•About•Advertise•Privacy Policy•Terms of Use © Insight Media Group, LLC 2026•The New Stack•roadmap.sh•Creator Network

Recommended Articles

Close