Summary of Mteb-french: Resources For French Sentence Embedding Evaluation and Analysis, by Mathieu Ciancone et al.
MTEB-French: Resources for French Sentence Embedding Evaluation and Analysis
by Mathieu Ciancone, Imene Kerboua, Marion Schaeffer, Wissam Siblini
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an extension to the Massive Text Embedding Benchmark (MTEB) by creating the first massive benchmark of sentence embeddings for French. The authors gather 15 existing datasets in an easy-to-use interface and create three new French datasets for a global evaluation of 8 task categories. They compare 51 carefully selected embedding models on a large scale, conduct comprehensive statistical tests, and analyze the correlation between model performance and many of their characteristics. The results show that even if no model is the best on all tasks, large multilingual models pre-trained on sentence similarity perform exceptionally well. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand which language models are good at understanding sentences in French. It takes existing datasets and adds three new ones to test 51 different models. They compare how well each model does on many different tasks and find that some big models that can understand many languages work really well for this task. |
Keywords
» Artificial intelligence » Embedding
