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docs/regressions/regressions-dl19-passage-cos-dpr-distil-fw.md
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| # Anserini Regressions: TREC 2019 Deep Learning Track (Passage) | ||
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| **Model**: cosDPR-distil with inverted indexes using the "fake-words" technique (q=40); pre-encoded queries | ||
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| This page describes regression experiments, integrated into Anserini's regression testing framework, using the cosDPR-distil model on the [TREC 2019 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html). | ||
| In these experiments, we are using pre-encoded queries (i.e., cached results of query encoding). | ||
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| Note that the NIST relevance judgments provide far more relevant passages per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). | ||
| For additional instructions on working with MS MARCO passage collection, refer to [this page](experiments-msmarco-passage.md). | ||
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| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/dl19-passage-cos-dpr-distil-fw.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl19-passage-cos-dpr-distil-fw.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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| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-cos-dpr-distil-fw | ||
| ``` | ||
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| We make available a version of the MS MARCO Passage Corpus that has already been encoded with cosDPR-distil. | ||
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| From any machine, the following command will download the corpus and perform the complete regression, end to end: | ||
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| ```bash | ||
| python src/main/python/run_regression.py --download --index --verify --search --regression dl19-passage-cos-dpr-distil-fw | ||
| ``` | ||
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| The `run_regression.py` script automates the following steps, but if you want to perform each step manually, simply copy/paste from the commands below and you'll obtain the same regression results. | ||
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| ## Corpus Download | ||
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| Download the corpus and unpack into `collections/`: | ||
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| ```bash | ||
| wget https://rgw.cs.uwaterloo.ca/pyserini/data/msmarco-passage-cos-dpr-distil.tar -P collections/ | ||
| tar xvf collections/msmarco-passage-cos-dpr-distil.tar -C collections/ | ||
| ``` | ||
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| To confirm, `msmarco-passage-cos-dpr-distil.tar` is 57 GB and has MD5 checksum `e20ffbc8b5e7f760af31298aefeaebbd`. | ||
| With the corpus downloaded, the following command will perform the remaining steps below: | ||
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| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-cos-dpr-distil-fw \ | ||
| --corpus-path collections/msmarco-passage-cos-dpr-distil | ||
| ``` | ||
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| ## Indexing | ||
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| Sample indexing command, applying inverted indexes to dense vectors using the "fake-words" technique: | ||
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| ```bash | ||
| target/appassembler/bin/IndexInvertedDenseVectors \ | ||
| -collection JsonDenseVectorCollection \ | ||
| -input /path/to/msmarco-passage-cos-dpr-distil \ | ||
| -index indexes/lucene-index.msmarco-passage-cos-dpr-distil.fw-40/ \ | ||
| -generator InvertedDenseVectorDocumentGenerator \ | ||
| -threads 16 -encoding fw -fw.q 40 \ | ||
| >& logs/log.msmarco-passage-cos-dpr-distil & | ||
| ``` | ||
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| The path `/path/to/msmarco-passage-cos-dpr-distil/` should point to the corpus downloaded above. | ||
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| Upon completion, we should have an index with 8,841,823 documents. | ||
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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. | ||
| The regression experiments here evaluate on the 43 topics for which NIST has provided judgments as part of the TREC 2019 Deep Learning Track. | ||
| The original data can be found [here](https://trec.nist.gov/data/deep2019.html). | ||
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| After indexing has completed, you should be able to perform retrieval as follows: | ||
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| ```bash | ||
| target/appassembler/bin/SearchInvertedDenseVectors \ | ||
| -index indexes/lucene-index.msmarco-passage-cos-dpr-distil.fw-40/ \ | ||
| -topics tools/topics-and-qrels/topics.dl19-passage.cos-dpr-distil.jsonl.gz \ | ||
| -topicreader JsonIntVector \ | ||
| -output runs/run.msmarco-passage-cos-dpr-distil.cos-dpr-distil-fw-40.topics.dl19-passage.cos-dpr-distil.jsonl.txt \ | ||
| -topicfield vector -encoding fw -fw.q 40 -hits 1000 & | ||
| ``` | ||
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| Evaluation can be performed using `trec_eval`: | ||
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| ```bash | ||
| tools/eval/trec_eval.9.0.4/trec_eval -m map -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-cos-dpr-distil.cos-dpr-distil-fw-40.topics.dl19-passage.cos-dpr-distil.jsonl.txt | ||
| tools/eval/trec_eval.9.0.4/trec_eval -m ndcg_cut.10 -c tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-cos-dpr-distil.cos-dpr-distil-fw-40.topics.dl19-passage.cos-dpr-distil.jsonl.txt | ||
| tools/eval/trec_eval.9.0.4/trec_eval -m recall.100 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-cos-dpr-distil.cos-dpr-distil-fw-40.topics.dl19-passage.cos-dpr-distil.jsonl.txt | ||
| tools/eval/trec_eval.9.0.4/trec_eval -m recall.1000 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-cos-dpr-distil.cos-dpr-distil-fw-40.topics.dl19-passage.cos-dpr-distil.jsonl.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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| | **AP@1000** | **cosDPR-distill**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.4271 | | ||
| | **nDCG@10** | **cosDPR-distill**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.6857 | | ||
| | **R@100** | **cosDPR-distill**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.5766 | | ||
| | **R@1000** | **cosDPR-distill**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.7902 | | ||
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| Note that due to the non-deterministic nature of HNSW indexing, results may differ slightly between each experimental run. | ||
| Nevertheless, scores are generally within 0.005 of the reference values recorded in [our YAML configuration file](../../src/main/resources/regression/dl19-passage-cos-dpr-distil-fw.yaml). | ||
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| Also note that retrieval metrics are computed to depth 1000 hits per query (as opposed to 100 hits per query for document ranking). | ||
| Also, for computing nDCG, remember that we keep qrels of _all_ relevance grades, whereas for other metrics (e.g., AP), relevance grade 1 is considered not relevant (i.e., use the `-l 2` option in `trec_eval`). | ||
| The experimental results reported here are directly comparable to the results reported in the [track overview paper](https://arxiv.org/abs/2003.07820). | ||
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| ## Reproduction Log[*](reproducibility.md) | ||
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| To add to this reproduction log, modify [this template](../../src/main/resources/docgen/templates/dl19-passage-cos-dpr-distil-fw.template) and run `bin/build.sh` to rebuild the documentation. |
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