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## Introduction |
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This respository introduces how to reproduce the `Dense`, `Sparse`, and `Dense+Sparse` evaluation results of the paper [BGE-M3](https://arxiv.org/pdf/2402.03216.pdf) on the [MIRACL](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering) dev split. |
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## Requirements |
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```bash |
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# Install Java (Linux) |
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apt update |
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apt install openjdk-21-jdk |
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# Install Pyserini |
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pip install pyserini |
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# Install Faiss |
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## CPU version |
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conda install -c conda-forge faiss-cpu |
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## GPU version |
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conda install -c conda-forge faiss-gpu |
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``` |
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**It should be noted that** the Pyserini code needs to be modified to support the multiple alpha settings in `pyserini/fusion`. I have already submitted a pull request to the official repository to support this feature. You can refer to this [PR](https://github.com/castorini/pyserini/pull/1858) to modify the code. |
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## 2CR |
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### Download and Unzip |
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```bash |
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# Download |
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## MIRACL topics and qrels |
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git clone https://huggingface.co/datasets/miracl/miracl |
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mv miracl/*/*/* topics-and-qrels |
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## Dense and Sparse Index |
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git lfs install |
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git clone https://huggingface.co/datasets/hanhainebula/bge-m3_miracl_2cr |
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cat bge-m3_miracl_2cr/dense/en.tar.gz.part_* > bge-m3_miracl_2cr/dense/en.tar.gz |
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cat bge-m3_miracl_2cr/dense/de.tar.gz.part_* > bge-m3_miracl_2cr/dense/de.tar.gz |
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# Unzip |
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languages=(ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo) |
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## Dense |
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for lang in ${languages[@]}; do |
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tar -zxvf bge-m3_miracl_2cr/dense/${lang}.tar.gz -C bge-m3_miracl_2cr/dense/ |
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done |
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## Sparse |
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for lang in ${languages[@]}; do |
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tar -zxvf bge-m3_miracl_2cr/sparse/${lang}.tar.gz -C bge-m3_miracl_2cr/sparse/ |
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done |
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``` |
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### Reproduction |
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#### Dense |
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```bash |
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# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo |
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lang=zh |
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# Generate run |
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python -m pyserini.search.faiss \ |
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--threads 16 --batch-size 512 \ |
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--encoder-class auto \ |
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--encoder BAAI/bge-m3 \ |
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--pooling cls --l2-norm \ |
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--topics topics-and-qrels/topics.miracl-v1.0-${lang}-dev.tsv \ |
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--index bge-m3_miracl_2cr/dense/${lang} \ |
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--output bge-m3_miracl_2cr/dense/runs/${lang}.txt \ |
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--hits 1000 |
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# Evaluate |
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## nDCG@10 |
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python -m pyserini.eval.trec_eval \ |
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-c -M 100 -m ndcg_cut.10 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/dense/runs/${lang}.txt |
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## Recall@100 |
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python -m pyserini.eval.trec_eval \ |
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-c -m recall.100 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/dense/runs/${lang}.txt |
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``` |
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#### Sparse |
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```bash |
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# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo |
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lang=zh |
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# Generate run |
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python -m pyserini.search.lucene \ |
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--threads 16 --batch-size 128 \ |
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--topics bge-m3_miracl_2cr/sparse/${lang}/query_embd.tsv \ |
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--index bge-m3_miracl_2cr/sparse/${lang}/index \ |
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--output bge-m3_miracl_2cr/sparse/runs/${lang}.txt \ |
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--output-format trec \ |
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--impact --hits 1000 |
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# Evaluate |
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## nDCG@10 |
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python -m pyserini.eval.trec_eval \ |
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-c -M 100 -m ndcg_cut.10 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/sparse/runs/${lang}.txt |
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## Recall@100 |
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python -m pyserini.eval.trec_eval \ |
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-c -m recall.100 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/sparse/runs/${lang}.txt |
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``` |
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#### Dense+Sparse |
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**Note**: You should first merge this [PR](https://github.com/castorini/pyserini/pull/1858) to support the multiple alpha settings in `pyserini/fusion`. |
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```bash |
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# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo |
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lang=zh |
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# Generate dense run and sparse run |
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python -m pyserini.search.faiss \ |
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--threads 16 --batch-size 512 \ |
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--encoder-class auto \ |
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--encoder BAAI/bge-m3 \ |
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--pooling cls --l2-norm \ |
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--topics topics-and-qrels/topics.miracl-v1.0-${lang}-dev.tsv \ |
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--index bge-m3_miracl_2cr/dense/${lang} \ |
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--output bge-m3_miracl_2cr/dense/runs/${lang}.txt \ |
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--hits 1000 |
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python -m pyserini.search.lucene \ |
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--threads 16 --batch-size 128 \ |
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--topics bge-m3_miracl_2cr/sparse/${lang}/query_embd.tsv \ |
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--index bge-m3_miracl_2cr/sparse/${lang}/index \ |
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--output bge-m3_miracl_2cr/sparse/runs/${lang}.txt \ |
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--output-format trec \ |
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--impact --hits 1000 |
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# Generate dense+sparse run |
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mkdir -p bge-m3_miracl_2cr/fusion/runs |
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python -m pyserini.fusion \ |
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--method interpolation \ |
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--runs bge-m3_miracl_2cr/dense/runs/${lang}.txt bge-m3_miracl_2cr/sparse/runs/${lang}.txt \ |
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--alpha 1 3e-5 \ |
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--output bge-m3_miracl_2cr/fusion/runs/${lang}.txt \ |
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--depth 1000 --k 1000 |
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# Evaluation |
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## nDCG@10 |
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python -m pyserini.eval.trec_eval \ |
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-c -M 100 -m ndcg_cut.10 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/fusion/runs/${lang}.txt |
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## Recall@100 |
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python -m pyserini.eval.trec_eval \ |
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-c -m recall.100 \ |
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topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \ |
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bge-m3_miracl_2cr/fusion/runs/${lang}.txt |
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``` |
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Note: |
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- The hybrid method we used for MIRACL in BGE-M3 paper is: `s_dense + 0.3 * s_sparse`. But when the sparse score is calculated, it has already been multiplied by 100^2, so the alpha for sparse run here is 3e-5, instead of 0.3. |
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