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Best Embedding Model ๐ŸŒŸโ€Šโ€”โ€ŠOpenAI / Cohere / Google / E5 / BGE

๐ŸŒˆ Abstract

The article provides an in-depth comparison of various multilingual embedding models, including those from OpenAI, Cohere, Google, Microsoft (E5), and the Beijing Academy of Artificial Intelligence (BGE-M3). It evaluates the performance of these models across multiple languages using metrics like Cumulative Match Characteristic (CMC) and Inverse Mean Average Precision (IMAP).

๐Ÿ™‹ Q&A

[01] Models To Compare

1. What are the key multilingual embedding models compared in the article? The article compares the following multilingual embedding models:

  • OpenAI Embeddings
  • Cohere Embeddings
  • Google Embeddings
  • Microsoft's E5 Embeddings (small, base, large, and large-instruct versions)
  • BGE-M3 from the Beijing Academy of Artificial Intelligence

2. How are the different models used in the examples provided? The article provides example code snippets demonstrating how to use each of the embedding models, including the specific API calls, model names, and output dimensions.

[02] How to Evaluate Embedding Quality

1. What are the key distance functions used to evaluate the quality of the embeddings? The article discusses three main distance functions for evaluating embedding quality:

  • Euclidean Distance
  • Manhattan Distance
  • Cosine Similarity

2. Why is cosine similarity the preferred method for measuring semantic similarity between text data? Cosine similarity is preferred because it focuses on the orientation of the vectors rather than their magnitude, making it more suitable for comparing texts of varying lengths and content densities.

[03] Evaluation Dataset

1. What is the dataset used for evaluating the embedding models? The article uses a dataset of 200 sentences categorized under 50 different topics, which was translated into 10 languages (English, Spanish, French, German, Russian, Dutch, Portuguese, Norwegian, Swedish, and Finnish).

2. How was the dataset created? The dataset was created using GPT-4 to translate the original English sentences into the other 9 languages.

[04] Rank-Based Evaluation โ€” Top N

1. What is the Cumulative Match Characteristic (CMC) curve and how is it used in the evaluation? The CMC curve shows the probability of the correct topic being ranked within the top N results. It is used to assess the ranking precision of the embedding models.

2. How is the Inverse Mean Average Precision (IMAP) calculated and what does it represent? IMAP is calculated as 1 - MAP, where MAP is the Mean Average Precision. IMAP represents the error rate, with lower values indicating better performance.

[05] Results

1. What are the key findings from the overall performance comparison of the embedding models? The results show that OpenAI, Google, E5-Instruct, and Cohere are the top-performing models, with OpenAI slightly edging ahead due to its lower average error rates across all languages.

2. How do the individual language performances of the models compare? While Cohere outperforms in several languages, OpenAI demonstrates more consistent performance across the diverse set of languages tested. The article suggests that including more languages in future analyses could further highlight the models' strengths and adaptability.

[06] Conclusion

1. What are the key takeaways from the article's conclusion? The article highlights the competitive landscape of multilingual embedding technologies, with proprietary models from OpenAI, Cohere, and Google, as well as the impact of open-source models like E5 and BGE-M3. It acknowledges the contributions of the research teams behind the open-source models.

2. What does the article suggest for future research or analysis in this area? The article suggests that including a broader range of languages in future analyses could further reveal the strengths and adaptability of the different embedding models.

Shared by Daniel Chen ยท
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