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Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 138, in compute return CompleteJobResult(compute_split_names_from_info_response(dataset=self.dataset, config=self.config)) File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 117, in compute_split_names_from_info_response config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config) File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 498, in get_dataset_config_info for split_generator in builder._split_generators( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema File "pyarrow/table.pxi", line 5245, in pyarrow.lib.concat_tables File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Must pass at least one table The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response for split in get_dataset_split_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 571, in get_dataset_split_names info = get_dataset_config_info( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 503, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.
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Introduction
This respository introduces how to reproduce the Dense
, Sparse
, and Dense+Sparse
evaluation results of the paper BGE-M3 on the MIRACL dev split.
Requirements
# Install Java (Linux)
apt update
apt install openjdk-21-jdk
# Install Pyserini
pip install pyserini
# Install Faiss
## CPU version
conda install -c conda-forge faiss-cpu
## GPU version
conda install -c conda-forge faiss-gpu
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 to modify the code.
2CR
Download and Unzip
# Download
## MIRACL topics and qrels
git clone https://huggingface.co/datasets/miracl/miracl
mv miracl/*/*/* topics-and-qrels
## Dense and Sparse Index
git lfs install
git clone https://huggingface.co/datasets/hanhainebula/bge-m3_miracl_2cr
cat bge-m3_miracl_2cr/dense/en.tar.gz.part_* > bge-m3_miracl_2cr/dense/en.tar.gz
cat bge-m3_miracl_2cr/dense/de.tar.gz.part_* > bge-m3_miracl_2cr/dense/de.tar.gz
# Unzip
languages=(ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo)
## Dense
for lang in ${languages[@]}; do
tar -zxvf bge-m3_miracl_2cr/dense/${lang}.tar.gz -C bge-m3_miracl_2cr/dense/
done
## Sparse
for lang in ${languages[@]}; do
tar -zxvf bge-m3_miracl_2cr/sparse/${lang}.tar.gz -C bge-m3_miracl_2cr/sparse/
done
Reproduction
Dense
# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo
lang=zh
# Generate run
python -m pyserini.search.faiss \
--threads 16 --batch-size 512 \
--encoder-class auto \
--encoder BAAI/bge-m3 \
--pooling cls --l2-norm \
--topics topics-and-qrels/topics.miracl-v1.0-${lang}-dev.tsv \
--index bge-m3_miracl_2cr/dense/${lang} \
--output bge-m3_miracl_2cr/dense/runs/${lang}.txt \
--hits 1000
# Evaluate
## nDCG@10
python -m pyserini.eval.trec_eval \
-c -M 100 -m ndcg_cut.10 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/dense/runs/${lang}.txt
## Recall@100
python -m pyserini.eval.trec_eval \
-c -m recall.100 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/dense/runs/${lang}.txt
Sparse
# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo
lang=zh
# Generate run
python -m pyserini.search.lucene \
--threads 16 --batch-size 128 \
--topics bge-m3_miracl_2cr/sparse/${lang}/query_embd.tsv \
--index bge-m3_miracl_2cr/sparse/${lang}/index \
--output bge-m3_miracl_2cr/sparse/runs/${lang}.txt \
--output-format trec \
--impact --hits 1000
# Evaluate
## nDCG@10
python -m pyserini.eval.trec_eval \
-c -M 100 -m ndcg_cut.10 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/sparse/runs/${lang}.txt
## Recall@100
python -m pyserini.eval.trec_eval \
-c -m recall.100 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/sparse/runs/${lang}.txt
Dense+Sparse
Note: You should first merge this PR to support the multiple alpha settings in pyserini/fusion
.
# Avaliable Language: ar bn en es fa fi fr hi id ja ko ru sw te th zh de yo
lang=zh
# Generate dense run and sparse run
python -m pyserini.search.faiss \
--threads 16 --batch-size 512 \
--encoder-class auto \
--encoder BAAI/bge-m3 \
--pooling cls --l2-norm \
--topics topics-and-qrels/topics.miracl-v1.0-${lang}-dev.tsv \
--index bge-m3_miracl_2cr/dense/${lang} \
--output bge-m3_miracl_2cr/dense/runs/${lang}.txt \
--hits 1000
python -m pyserini.search.lucene \
--threads 16 --batch-size 128 \
--topics bge-m3_miracl_2cr/sparse/${lang}/query_embd.tsv \
--index bge-m3_miracl_2cr/sparse/${lang}/index \
--output bge-m3_miracl_2cr/sparse/runs/${lang}.txt \
--output-format trec \
--impact --hits 1000
# Generate dense+sparse run
mkdir -p bge-m3_miracl_2cr/fusion/runs
python -m pyserini.fusion \
--method interpolation \
--runs bge-m3_miracl_2cr/dense/runs/${lang}.txt bge-m3_miracl_2cr/sparse/runs/${lang}.txt \
--alpha 1 3e-5 \
--output bge-m3_miracl_2cr/fusion/runs/${lang}.txt \
--depth 1000 --k 1000
# Evaluation
## nDCG@10
python -m pyserini.eval.trec_eval \
-c -M 100 -m ndcg_cut.10 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/fusion/runs/${lang}.txt
## Recall@100
python -m pyserini.eval.trec_eval \
-c -m recall.100 \
topics-and-qrels/qrels.miracl-v1.0-${lang}-dev.tsv \
bge-m3_miracl_2cr/fusion/runs/${lang}.txt
Note:
- 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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