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@ -214,10 +214,11 @@ print(model.compute_score(sentence_pairs,
## Evaluation
**Currently, the results of BM25 on non-English data are incorrect.
We will review our testing process and update the paper as soon as possible.
For more powerful BM25, you can refer to this [repo](https://github.com/carlos-lassance/bm25_mldr).
Thanks to the community for the reminder and to carlos-lassance for providing the results.**
We compare BGE-M3 with some popular methods, including BM25, openAI embedding, etc.
We utilized Pyserini to implement BM25, and the test results can be reproduced by this [script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#bm25-baseline).
To make the BM25 and BGE-M3 more comparable, in the experiment,
BM25 used the same tokenizer as BGE-M3 (i.e., the tokenizer of XLM-Roberta).
Using the same vocabulary can also ensure that both approaches have the same retrieval latency.
- Multilingual (Miracl dataset)