{ "source_file": "0930", "source_platform": "Medium", "author": "Iulia Brezeanu", "title_listed": "How to Find the Best Multilingual Embedding Model for Your RAG", "keywords": "", "domain": "文本处理", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/how-to-find-the-best-multilingual-embedding-model-for-your-rag-40325c308ebb/", "publish_date": "2024-01-27", "corpus_stage": "", "notes": "", "id": "60697603f78e", "collected_at": "2026-10-02T09:45:55", "title": "How to Find the Best Multilingual Embedding Model for Your RAG | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# How to Find the Best Multilingual Embedding Model for Your RAG\nOptimize the Embedding Space for Improving RAG\n[Iulia Brezeanu](https://towardsdatascience.com/author/brezeanu.iulia/)\nJanuary 27, 20248 min read\n![Image by author. AI generated.](../images/000.jpg)\nImage by author. AI generated.\nEmbeddings 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!\nThe 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.\nIn 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.\nWhile we focus on French and Italian, the process can be adapted to any language because the best embeddings might differ.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) and [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmaps.\n### Embedding Models\nThere 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.\n**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.\nMost current fine-tuned models use contrastive learning. The model learns semantic similarity by seeing both positive and negative text pairs during training.\n![Process of optimizing the Embedding Space. Image by author.](../images/001.png)\nProcess of optimizing the Embedding Space. Image by author.\nIn 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.\n![Example of 2D PCA of Sentence Embeddings. Image by author.](../images/002.png)\nExample of 2D PCA of Sentence Embeddings. Image by author.\nThe graph demonstrates how the semantic space organizes sentences with related meanings close together.\nNote 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.\nDuring 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.\nThe 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.\n## Massive Text Embedding Benchmark\n![An overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. \\[3\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1-b0dVM3qcdTF6YKF4dUzcw.png)\nAn overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. [3]\nMassive 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.\nWe 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.\nModel 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](https://towardsdatascience.com/demystifying-ndcg-bee3be58cfe0) if you want to read more.\nUnfortunately, the initial MTEB retrieval datasets were only in English. Recently, some were added in Polish and Swedish, but multilingual support is still limited [2].\n![MTEB Leaderboard \\[1\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1S_ChBXidQj0EQ2VcKd3Gkg.png)\nMTEB Leaderboard [1]\nWe 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.\nFirst, 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.\n![Comparison of Embedding Models Specifications. Image by author.](../images/005.png)\nComparison of Embedding Models Specifications. Image by author.\n### Cohere-embed-multilingual-v3.0 & light-v3.0\nCohere 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].\nThe light version of the multilingual Cohere model has an embedding dimension of only 384 [7].\n### intfloat/multilingual-e5-large\nThe 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.\n![CCPairs \\(Colossal Clean text Pairs\\) \\[5\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1iC5FT_HmOIyAp5M2agN1HA.png)\nCCPairs (Colossal Clean text Pairs) [5]\n### text-embedding-3-large\nOpenAI 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].\n### sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\nWe consider this model as a baseline because it's one of the most downloaded Sentence Transformers models from Hugging Face.\n![sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads \\[8\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1HFUUN2rUb0am_SLBA3ltCQ.png)\nsentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads [8]\nIt 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.\n## Generating QA pairs\nIn 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.\nWe 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.\npython\n\n```\nprompt_it = \"\"\"\nLe informazioni sul contesto sono riportate di seguito.\n\n---------------------\n{context_str}\n---------------------\n\nDate le informazioni sul contesto e non le conoscenze pregresse.\ngenerare solo domande basate sulla domanda seguente.\n\nSiete un insegnante/professore. Il vostro compito è quello di impostare \n{num_questions_per_chunk} per un prossimo quiz.\nLe domande devono essere di natura diversa nel documento. \nLimitare le domande alle informazioni di contesto fornite. \nLe domande devono essere in italiano.\"\n\"\"\"\n\nprompt_fr = \"\"\"\nLes informations contextuelles se trouvent ci-dessous.\n\n---------------------\n{context_str}\n---------------------\n\nCompte tenu des informations contextuelles et sans connaissances préalables, générer uniquement des questions basées sur la requête ci-dessous.\n\nVous êtes enseignant/professeur. Votre tâche consiste à mettre en place \n{num_questions_per_chunk} pour un quiz à venir. \nLes questions doivent être de nature variée sur l'ensemble du document. \nLimitez les questions aux informations contextuelles fournies. \nLes questions doivent être en français.\"\n\"\"\"\n```\n\nWe already split the text into chunks of 1000 characters and ingested them into nodes, so now we go straight to creating the dataset.\nmakefile\n\n```\nfr_dataset = generate_qa_embedding_pairs(\n llm=OpenAI(model=\"gpt-3.5-turbo\"), nodes=fr_nodes, qa_generate_prompt_tmpl = prompt_fr\n)\nfr_dataset.save_json(\"fr_dataset.json\")\n\nit_dataset = generate_qa_embedding_pairs(\n llm=OpenAI(model=\"gpt-3.5-turbo\"), nodes=it_nodes, qa_generate_prompt_tmpl = prompt_it\n)\n\nit_dataset.save_json(\"it_dataset.json\")\n```\n\nThe 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.\n![First query from the Italian dataset. Image by author.](../images/008.png)\nFirst query from the Italian dataset. Image by author.\n## Evaluation metrics\nWe'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.\nMRR 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.\n## Models' performance\n![Models' performance. Image by author.](../images/009.png)\nModels' performance. Image by author.\nThe 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.\nWe also tried to reduce the embeddings of _text-embedding-3-large_ to 256, and the performance was pretty impressive, considering the small dimension.\nThe other embedding model from OpenAI, _ada-002_ , scored considerably lower than their newest one, showing that the update brought impressive improvements.\nIntfloat'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.\nThe results from _paraphrase-multilingual-MiniLM-L12-v2_ are disappointing. While it was very fast, we had hoped for a better performance.\n## Conclusion\nThe 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.\nAs 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.\n. . .\n_If you enjoyed this article, join_[ _**Text Generation**_](https://textgeneration.substack.com/) _- our newsletter has two weekly posts with the latest insights on Generative AI and Large Language Models._\n_You can find the full code for this project on_[ _GitHub_](https://github.com/partycia/multilingual-embedding-models) _._\n_You can also find me on_[ _LinkedIn_](https://www.linkedin.com/in/ibrezeanu/) _._\n. . .\n## References\n 1. \n 2. \n 3. [MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)\n 4. \n 5. \n 6. \n 7. \n 8. \n 9. \n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# How to Find the Best Multilingual Embedding Model for Your RAG\nOptimize the Embedding Space for Improving RAG\n[Iulia Brezeanu](https://towardsdatascience.com/author/brezeanu.iulia/)\nJanuary 27, 20248 min read\n![Image by author. AI generated.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/0Ln4p2tHZSghTlKH7.jpg)\nImage by author. AI generated.\nEmbeddings 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!\nThe 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.\nIn 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.\nWhile we focus on French and Italian, the process can be adapted to any language because the best embeddings might differ.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) and [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmaps.\n### Embedding Models\nThere 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.\n**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.\nMost current fine-tuned models use contrastive learning. The model learns semantic similarity by seeing both positive and negative text pairs during training.\n![Process of optimizing the Embedding Space. Image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1U3Xi-Qhir3wo9z_Bh1PR6g.png)\nProcess of optimizing the Embedding Space. Image by author.\nIn 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.\n![Example of 2D PCA of Sentence Embeddings. Image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/12zjIKrkhxSCCtG87Qa5-Tw.png)\nExample of 2D PCA of Sentence Embeddings. Image by author.\nThe graph demonstrates how the semantic space organizes sentences with related meanings close together.\nNote 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.\nDuring 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.\nThe 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.\n## Massive Text Embedding Benchmark\n![An overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. \\[3\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1-b0dVM3qcdTF6YKF4dUzcw.png)\nAn overview of tasks and datasets in MTEB. Multilingual datasets are marked with a purple shade. [3]\nMassive 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.\nWe 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.\nModel 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](https://towardsdatascience.com/demystifying-ndcg-bee3be58cfe0) if you want to read more.\nUnfortunately, the initial MTEB retrieval datasets were only in English. Recently, some were added in Polish and Swedish, but multilingual support is still limited [2].\n![MTEB Leaderboard \\[1\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1S_ChBXidQj0EQ2VcKd3Gkg.png)\nMTEB Leaderboard [1]\nWe 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.\nFirst, 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.\n![Comparison of Embedding Models Specifications. Image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1-pzQPJYp5za_0Z5M489k3A.png)\nComparison of Embedding Models Specifications. Image by author.\n### Cohere-embed-multilingual-v3.0 & light-v3.0\nCohere 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].\nThe light version of the multilingual Cohere model has an embedding dimension of only 384 [7].\n### intfloat/multilingual-e5-large\nThe 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.\n![CCPairs \\(Colossal Clean text Pairs\\) \\[5\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1iC5FT_HmOIyAp5M2agN1HA.png)\nCCPairs (Colossal Clean text Pairs) [5]\n### text-embedding-3-large\nOpenAI 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].\n### sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\nWe consider this model as a baseline because it's one of the most downloaded Sentence Transformers models from Hugging Face.\n![sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads \\[8\\]](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1HFUUN2rUb0am_SLBA3ltCQ.png)\nsentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 number of downloads [8]\nIt 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.\n## Generating QA pairs\nIn 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.\nWe 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.\npython\n\n```\nprompt_it = \"\"\"\nLe informazioni sul contesto sono riportate di seguito.\n\n---------------------\n{context_str}\n---------------------\n\nDate le informazioni sul contesto e non le conoscenze pregresse.\ngenerare solo domande basate sulla domanda seguente.\n\nSiete un insegnante/professore. Il vostro compito è quello di impostare \n{num_questions_per_chunk} per un prossimo quiz.\nLe domande devono essere di natura diversa nel documento. \nLimitare le domande alle informazioni di contesto fornite. \nLe domande devono essere in italiano.\"\n\"\"\"\n\nprompt_fr = \"\"\"\nLes informations contextuelles se trouvent ci-dessous.\n\n---------------------\n{context_str}\n---------------------\n\nCompte tenu des informations contextuelles et sans connaissances préalables, générer uniquement des questions basées sur la requête ci-dessous.\n\nVous êtes enseignant/professeur. Votre tâche consiste à mettre en place \n{num_questions_per_chunk} pour un quiz à venir. \nLes questions doivent être de nature variée sur l'ensemble du document. \nLimitez les questions aux informations contextuelles fournies. \nLes questions doivent être en français.\"\n\"\"\"\n```\n\nWe already split the text into chunks of 1000 characters and ingested them into nodes, so now we go straight to creating the dataset.\nmakefile\n\n```\nfr_dataset = generate_qa_embedding_pairs(\n llm=OpenAI(model=\"gpt-3.5-turbo\"), nodes=fr_nodes, qa_generate_prompt_tmpl = prompt_fr\n)\nfr_dataset.save_json(\"fr_dataset.json\")\n\nit_dataset = generate_qa_embedding_pairs(\n llm=OpenAI(model=\"gpt-3.5-turbo\"), nodes=it_nodes, qa_generate_prompt_tmpl = prompt_it\n)\n\nit_dataset.save_json(\"it_dataset.json\")\n```\n\nThe 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.\n![First query from the Italian dataset. Image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1Omqb8udbB_u8a_mRtz95IA.png)\nFirst query from the Italian dataset. Image by author.\n## Evaluation metrics\nWe'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.\nMRR 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.\n## Models' performance\n![Models' performance. Image by author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/01/1hEtUxmV8ZEygC_Rgc-oAMA.png)\nModels' performance. Image by author.\nThe 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.\nWe also tried to reduce the embeddings of _text-embedding-3-large_ to 256, and the performance was pretty impressive, considering the small dimension.\nThe other embedding model from OpenAI, _ada-002_ , scored considerably lower than their newest one, showing that the update brought impressive improvements.\nIntfloat'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.\nThe results from _paraphrase-multilingual-MiniLM-L12-v2_ are disappointing. While it was very fast, we had hoped for a better performance.\n## Conclusion\nThe 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.\nAs 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.\n. . .\n_If you enjoyed this article, join_[ _**Text Generation**_](https://textgeneration.substack.com/) _- our newsletter has two weekly posts with the latest insights on Generative AI and Large Language Models._\n_You can find the full code for this project on_[ _GitHub_](https://github.com/partycia/multilingual-embedding-models) _._\n_You can also find me on_[ _LinkedIn_](https://www.linkedin.com/in/ibrezeanu/) _._\n. . .\n## References\n 1. \n 2. \n 3. [MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)\n 4. \n 5. \n 6. \n 7. \n 8. \n 9. \n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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