Model: SPLADE-v3 (using ONNX for on-the-fly query encoding)
This page describes regression experiments, integrated into Anserini's regression testing framework, using SPLADE-v3 on BEIR (v1.0.0) — TREC-NEWS. The model itself can be download here. See the official SPLADE repo and the following paper for more details:
Carlos Lassance, Hervé Déjean, Thibault Formal, and Stéphane Clinchant. SPLADE-v3: New baselines for SPLADE. arXiv:2403.06789.
In these experiments, we are using ONNX to perform query encoding on the fly.
The exact configurations for these regressions are stored in this YAML file.
Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead and then run bin/build.sh to rebuild the documentation.
From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:
python src/main/python/run_regression.py --index --verify --search --regression beir-v1.0.0-trec-news.splade-v3.onnx
All the BEIR corpora, encoded by the SPLADE-v3 model, are available for download:
wget https://rgw.cs.uwaterloo.ca/pyserini/data/beir-v1.0.0-splade-v3.tar -P collections/
tar xvf collections/beir-v1.0.0-splade-v3.tar -C collections/The tarball is 55 GB and has MD5 checksum 37f294610af763ce48eed03afd9455df.
After download and unpacking the corpora, the run_regression.py command above should work without any issue.
Sample indexing command:
bin/run.sh io.anserini.index.IndexCollection \
-threads 16 \
-collection JsonVectorCollection \
-input /path/to/beir-v1.0.0-trec-news.splade-v3 \
-generator DefaultLuceneDocumentGenerator \
-index indexes/lucene-inverted.beir-v1.0.0-trec-news.splade-v3/ \
-impact -pretokenized \
>& logs/log.beir-v1.0.0-trec-news.splade-v3 &
The important indexing options to note here are -impact -pretokenized: the first tells Anserini not to encode BM25 doclengths into Lucene's norms (which is the default) and the second option says not to apply any additional tokenization on the pre-encoded tokens.
For additional details, see explanation of common indexing options.
Topics and qrels are stored here, which is linked to the Anserini repo as a submodule.
After indexing has completed, you should be able to perform retrieval as follows:
bin/run.sh io.anserini.search.SearchCollection \
-index indexes/lucene-inverted.beir-v1.0.0-trec-news.splade-v3/ \
-topics tools/topics-and-qrels/topics.beir-v1.0.0-trec-news.test.tsv.gz \
-topicReader TsvString \
-output runs/run.beir-v1.0.0-trec-news.splade-v3.splade-v3-onnx.topics.beir-v1.0.0-trec-news.test.txt \
-impact -pretokenized -removeQuery -hits 1000 -encoder SpladeV3 &
Evaluation can be performed using trec_eval:
bin/trec_eval -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.beir-v1.0.0-trec-news.test.txt runs/run.beir-v1.0.0-trec-news.splade-v3.splade-v3-onnx.topics.beir-v1.0.0-trec-news.test.txt
bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.beir-v1.0.0-trec-news.test.txt runs/run.beir-v1.0.0-trec-news.splade-v3.splade-v3-onnx.topics.beir-v1.0.0-trec-news.test.txt
bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.beir-v1.0.0-trec-news.test.txt runs/run.beir-v1.0.0-trec-news.splade-v3.splade-v3-onnx.topics.beir-v1.0.0-trec-news.test.txt
With the above commands, you should be able to reproduce the following results:
| nDCG@10 | SPLADE-v3 |
|---|---|
| BEIR (v1.0.0): TREC-NEWS | 0.4365 |
| R@100 | SPLADE-v3 |
| BEIR (v1.0.0): TREC-NEWS | 0.4737 |
| R@1000 | SPLADE-v3 |
| BEIR (v1.0.0): TREC-NEWS | 0.7404 |