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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,106 @@ | ||
| # Anserini Regressions: TREC 2022 Deep Learning Track (Document) | ||
|
|
||
| **Models**: BM25 on complete documents with doc2query-T5 expansions | ||
|
|
||
| This page describes experiments, integrated into Anserini's regression testing framework, on the [TREC 2022 Deep Learning Track document ranking task](https://trec.nist.gov/data/deep2022.html) using the MS MARCO V2 document corpus (with doc2query-T5 expansions). | ||
| For additional instructions on working with the MS MARCO V2 document corpus, refer to [this page](../../docs/experiments-msmarco-v2.md). | ||
|
|
||
| Note that the NIST relevance judgments provide far more relevant documents per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). | ||
|
|
||
| Note that there are four different bag-of-words regression conditions for this task, and this page describes the following: | ||
|
|
||
| + **Indexing Condition:** each document in the MS MARCO V2 document corpus is treated as a unit of indexing | ||
| + **Expansion Condition:** doc2query-T5 | ||
|
|
||
| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/dl22-doc-d2q-t5.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl22-doc-d2q-t5.template) as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead. | ||
|
|
||
| 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 dl22-doc-d2q-t5 | ||
| ``` | ||
|
|
||
| ## Indexing | ||
|
|
||
| Typical indexing command: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/IndexCollection \ | ||
| -collection MsMarcoV2DocCollection \ | ||
| -input /path/to/msmarco-v2-doc-d2q-t5 \ | ||
| -generator DefaultLuceneDocumentGenerator \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-d2q-t5/ \ | ||
| -threads 24 -storeRaw \ | ||
| >& logs/log.msmarco-v2-doc-d2q-t5 & | ||
| ``` | ||
|
|
||
| The value of `-input` should be a directory containing the compressed `jsonl` files that comprise the corpus. | ||
| See [this page](../../docs/experiments-msmarco-v2.md) for additional details. | ||
|
|
||
| For additional details, see explanation of [common indexing options](../../docs/common-indexing-options.md). | ||
|
|
||
| ## Retrieval | ||
|
|
||
| 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 57 topics for which NIST has provided judgments as part of the [TREC 2022 Deep Learning Track](https://trec.nist.gov/data/deep2022.html). | ||
|
|
||
| After indexing has completed, you should be able to perform retrieval as follows: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-d2q-t5.bm25-default.topics.dl22.txt \ | ||
| -hits 1000 -bm25 & | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rm3.topics.dl22.txt \ | ||
| -hits 1000 -bm25 -rm3 -collection MsMarcoV2DocCollection & | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rocchio.topics.dl22.txt \ | ||
| -hits 1000 -bm25 -rocchio -collection MsMarcoV2DocCollection & | ||
| ``` | ||
|
|
||
| Evaluation can be performed using `trec_eval`: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| ``` | ||
|
|
||
| ## Effectiveness | ||
|
|
||
| With the above commands, you should be able to reproduce the following results: | ||
|
|
||
| | **MAP@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------|-----------|-----------| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.0977 | 0.0904 | 0.0944 | | ||
| | **MRR@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.6666 | 0.5657 | 0.5700 | | ||
| | **nDCG@10** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.3539 | 0.2758 | 0.2797 | | ||
| | **R@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.2041 | 0.1957 | 0.2022 | | ||
| | **R@1000** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.4301 | 0.4263 | 0.4338 | |
106 changes: 106 additions & 0 deletions
106
docs/regressions/regressions-dl22-doc-segmented-d2q-t5.md
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,106 @@ | ||
| # Anserini Regressions: TREC 2022 Deep Learning Track (Document) | ||
|
|
||
| **Models**: BM25 on segmented documents with doc2query-T5 expansions | ||
|
|
||
| This page describes experiments, integrated into Anserini's regression testing framework, on the [TREC 2022 Deep Learning Track document ranking task](https://trec.nist.gov/data/deep2022.html) using the MS MARCO V2 _segmented_ document corpus (with doc2query-T5 expansions). | ||
| For additional instructions on working with the MS MARCO V2 document corpus, refer to [this page](../../docs/experiments-msmarco-v2.md). | ||
|
|
||
| Note that the NIST relevance judgments provide far more relevant documents per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). | ||
|
|
||
| Note that there are four different bag-of-words regression conditions for this task, and this page describes the following: | ||
|
|
||
| + **Indexing Condition:** each segment in the MS MARCO V2 _segmented_ document corpus is treated as a unit of indexing | ||
| + **Expansion Condition:** doc2query-T5 | ||
|
|
||
| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/dl22-doc-segmented-d2q-t5.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl22-doc-segmented-d2q-t5.template) as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead. | ||
|
|
||
| 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 dl22-doc-segmented-d2q-t5 | ||
| ``` | ||
|
|
||
| ## Indexing | ||
|
|
||
| Typical indexing command: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/IndexCollection \ | ||
| -collection MsMarcoV2DocCollection \ | ||
| -input /path/to/msmarco-v2-doc-segmented-d2q-t5 \ | ||
| -generator DefaultLuceneDocumentGenerator \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-segmented-d2q-t5/ \ | ||
| -threads 24 -storeRaw \ | ||
| >& logs/log.msmarco-v2-doc-segmented-d2q-t5 & | ||
| ``` | ||
|
|
||
| The value of `-input` should be a directory containing the compressed `jsonl` files that comprise the corpus. | ||
| See [this page](../../docs/experiments-msmarco-v2.md) for additional details. | ||
|
|
||
| For additional details, see explanation of [common indexing options](../../docs/common-indexing-options.md). | ||
|
|
||
| ## Retrieval | ||
|
|
||
| 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 57 topics for which NIST has provided judgments as part of the [TREC 2022 Deep Learning Track](https://trec.nist.gov/data/deep2022.html). | ||
|
|
||
| After indexing has completed, you should be able to perform retrieval as follows: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-segmented-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default.topics.dl22.txt \ | ||
| -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 -bm25 & | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-segmented-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rm3.topics.dl22.txt \ | ||
| -bm25 -rm3 -collection MsMarcoV2DocCollection -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 & | ||
| target/appassembler/bin/SearchCollection \ | ||
| -index indexes/lucene-index.msmarco-v2-doc-segmented-d2q-t5/ \ | ||
| -topics tools/topics-and-qrels/topics.dl22.txt \ | ||
| -topicReader TsvInt \ | ||
| -output runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rocchio.topics.dl22.txt \ | ||
| -bm25 -rocchio -collection MsMarcoV2DocCollection -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000 & | ||
| ``` | ||
|
|
||
| Evaluation can be performed using `trec_eval`: | ||
|
|
||
| ``` | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rm3.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m map tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.100 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -m recall.1000 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| target/appassembler/bin/trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 tools/topics-and-qrels/qrels.dl22-doc.txt runs/run.msmarco-v2-doc-segmented-d2q-t5.bm25-default+rocchio.topics.dl22.txt | ||
| ``` | ||
|
|
||
| ## Effectiveness | ||
|
|
||
| With the above commands, you should be able to reproduce the following results: | ||
|
|
||
| | **MAP@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------|-----------|-----------| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.1203 | 0.1319 | 0.1349 | | ||
| | **MRR@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.7367 | 0.7035 | 0.7226 | | ||
| | **nDCG@10** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.3975 | 0.3912 | 0.4012 | | ||
| | **R@100** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.2441 | 0.2467 | 0.2499 | | ||
| | **R@1000** | **BM25 (default)**| **+RM3** | **+Rocchio**| | ||
| | [DL22 (Doc)](https://microsoft.github.io/msmarco/TREC-Deep-Learning) | 0.4984 | 0.5188 | 0.5320 | |
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