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Add docs for SPLADE++ ED w/ ONNX (castorini#2369)
+ Tweaked scores for ONNX (before, it was scores just copied from pre-encoded version). + Completed template docs and links from main README.
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docs/regressions/regressions-beir-v1.0.0-arguana-splade-pp-ed-onnx.md
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| # Anserini Regressions: BEIR (v1.0.0) — ArguAna | ||
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| **Model**: [SPLADE++ (CoCondenser-EnsembleDistil)](https://arxiv.org/abs/2205.04733) (using ONNX for on-the-fly query encoding) | ||
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| This page describes regression experiments, integrated into Anserini's regression testing framework, using [SPLADE++ (CoCondenser-EnsembleDistil)](https://arxiv.org/abs/2205.04733) on [BEIR (v1.0.0) — ArguAna](http://beir.ai/). | ||
| The model itself can be download [here](https://huggingface.co/naver/splade-cocondenser-ensembledistil). | ||
| See the [official SPLADE repo](https://github.com/naver/splade) and the following paper for more details: | ||
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| > Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. [From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective.](https://dl.acm.org/doi/10.1145/3477495.3531857) _Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval_, pages 2353–2359. | ||
| In these experiments, we are using ONNX to perform query encoding on the fly. | ||
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| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/beir-v1.0.0-arguana-splade-pp-ed-onnx.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/beir-v1.0.0-arguana-splade-pp-ed-onnx.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. | ||
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| From one of our Waterloo servers (e.g., `orca`), the following command will perform the complete regression, end to end: | ||
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| ``` | ||
| python src/main/python/run_regression.py --index --verify --search --regression beir-v1.0.0-arguana-splade-pp-ed-onnx | ||
| ``` | ||
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| All the BEIR corpora, encoded by the SPLADE++ CoCondenser-EnsembleDistil model, are available for download: | ||
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| ```bash | ||
| wget https://rgw.cs.uwaterloo.ca/pyserini/data/beir-v1.0.0-splade-pp-ed.tar -P collections/ | ||
| tar xvf collections/beir-v1.0.0-splade-pp-ed.tar -C collections/ | ||
| ``` | ||
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| The tarball is 42 GB and has MD5 checksum `9c7de5b444a788c9e74c340bf833173b`. | ||
| After download and unpacking the corpora, the `run_regression.py` command above should work without any issue. | ||
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| ## Indexing | ||
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| Sample indexing command: | ||
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| ``` | ||
| target/appassembler/bin/IndexCollection \ | ||
| -collection JsonVectorCollection \ | ||
| -input /path/to/beir-v1.0.0-arguana-splade-pp-ed \ | ||
| -generator DefaultLuceneDocumentGenerator \ | ||
| -index indexes/lucene-index.beir-v1.0.0-arguana-splade-pp-ed/ \ | ||
| -threads 16 -impact -pretokenized \ | ||
| >& logs/log.beir-v1.0.0-arguana-splade-pp-ed & | ||
| ``` | ||
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| 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](../../docs/common-indexing-options.md). | ||
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| ## Retrieval | ||
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| Topics and qrels are stored [here](https://github.com/castorini/anserini-tools/tree/master/topics-and-qrels), which is linked to the Anserini repo as a submodule. | ||
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| After indexing has completed, you should be able to perform retrieval as follows: | ||
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| ``` | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.beir-v1.0.0-arguana-splade-pp-ed/ \ | ||
| -topics tools/topics-and-qrels/topics.beir-v1.0.0-arguana.test.tsv.gz \ | ||
| -topicReader TsvString \ | ||
| -output runs/run.beir-v1.0.0-arguana-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-arguana.test.txt \ | ||
| -impact -pretokenized -removeQuery -hits 1000 -encoder SpladePlusPlusEnsembleDistil & | ||
| ``` | ||
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| Evaluation can be performed using `trec_eval`: | ||
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| ``` | ||
| target/appassembler/bin/trec_eval -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.beir-v1.0.0-arguana.test.txt runs/run.beir-v1.0.0-arguana-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-arguana.test.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.beir-v1.0.0-arguana.test.txt runs/run.beir-v1.0.0-arguana-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-arguana.test.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.beir-v1.0.0-arguana.test.txt runs/run.beir-v1.0.0-arguana-splade-pp-ed.splade-pp-ed.topics.beir-v1.0.0-arguana.test.txt | ||
| ``` | ||
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| ## Effectiveness | ||
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| With the above commands, you should be able to reproduce the following results: | ||
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| | **nDCG@10** | **SPLADE++ (CoCondenser-EnsembleDistil)**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------| | ||
| | BEIR (v1.0.0): ArguAna | 0.5218 | | ||
| | **R@100** | **SPLADE++ (CoCondenser-EnsembleDistil)**| | ||
| | BEIR (v1.0.0): ArguAna | 0.9758 | | ||
| | **R@1000** | **SPLADE++ (CoCondenser-EnsembleDistil)**| | ||
| | BEIR (v1.0.0): ArguAna | 0.9950 | |
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docs/regressions/regressions-beir-v1.0.0-arguana-splade-pp-ed.md
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