-
Notifications
You must be signed in to change notification settings - Fork 457
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Add cos-dpr-distill regressions for TREC DL 19/20 (#2208)
- Loading branch information
Showing
10 changed files
with
576 additions
and
11 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
119 changes: 119 additions & 0 deletions
119
docs/regressions/regressions-dl19-passage-cos-dpr-distil.md
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| # Anserini Regressions: TREC 2019 Deep Learning Track (Passage) | ||
|
|
||
| **Model**: cosDPR-distil (using pre-encoded queries) with HNSW indexes | ||
|
|
||
| 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), as described in the following paper: | ||
|
|
||
| > Xueguang Ma, Tommaso Teofili, and Jimmy Lin. [Anserini Gets Dense Retrieval: Integration of Lucene's HNSW Indexes.](https://arxiv.org/abs/2304.12139) _arXiv:2304.12139_, 2023. | ||
| In these experiments, we are using pre-encoded queries (i.e., cached results of query encoding). | ||
|
|
||
| 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). | ||
|
|
||
| The exact configurations for these regressions are stored in [this YAML file](../../src/main/resources/regression/dl19-passage-cos-dpr-distil.yaml). | ||
| Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl19-passage-cos-dpr-distil.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: | ||
|
|
||
| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-cos-dpr-distil | ||
| ``` | ||
|
|
||
| We make available a version of the MS MARCO Passage Corpus that has already been encoded with cosDPR-distil. | ||
|
|
||
| From any machine, the following command will download the corpus and perform the complete regression, end to end: | ||
|
|
||
| ```bash | ||
| python src/main/python/run_regression.py --download --index --verify --search --regression dl19-passage-cos-dpr-distil | ||
| ``` | ||
|
|
||
| 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. | ||
|
|
||
| ## Corpus Download | ||
|
|
||
| Download the corpus and unpack into `collections/`: | ||
|
|
||
| ```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/ | ||
| ``` | ||
|
|
||
| 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: | ||
|
|
||
| ```bash | ||
| python src/main/python/run_regression.py --index --verify --search --regression dl19-passage-cos-dpr-distil \ | ||
| --corpus-path collections/msmarco-passage-cos-dpr-distil | ||
| ``` | ||
|
|
||
| ## Indexing | ||
|
|
||
| Sample indexing command, building HNSW indexes: | ||
|
|
||
| ```bash | ||
| target/appassembler/bin/IndexHnswDenseVectors \ | ||
| -collection JsonDenseVectorCollection \ | ||
| -input /path/to/msmarco-passage-cos-dpr-distil \ | ||
| -index indexes/lucene-hnsw.msmarco-passage-cos-dpr-distil/ \ | ||
| -generator LuceneDenseVectorDocumentGenerator \ | ||
| -threads 16 -M 16 -efC 100 \ | ||
| >& logs/log.msmarco-passage-cos-dpr-distil & | ||
| ``` | ||
|
|
||
| The path `/path/to/msmarco-passage-cos-dpr-distil/` should point to the corpus downloaded above. | ||
|
|
||
| Upon completion, we should have an index with 8,841,823 documents. | ||
|
|
||
| <!-- For additional details, see explanation of [common indexing options](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 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). | ||
|
|
||
| After indexing has completed, you should be able to perform retrieval as follows: | ||
|
|
||
| ```bash | ||
| target/appassembler/bin/SearchHnswDenseVectors \ | ||
| -index indexes/lucene-hnsw.msmarco-passage-cos-dpr-distil/ \ | ||
| -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.topics.dl19-passage.cos-dpr-distil.jsonl.txt \ | ||
| -querygenerator VectorQueryGenerator -topicfield vector -threads 16 -hits 1000 -efSearch 1000 & | ||
| ``` | ||
|
|
||
| Evaluation can be performed using `trec_eval`: | ||
|
|
||
| ```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.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.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.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.topics.dl19-passage.cos-dpr-distil.jsonl.txt | ||
| ``` | ||
|
|
||
| ## Effectiveness | ||
|
|
||
| With the above commands, you should be able to reproduce the following results: | ||
|
|
||
| | **AP@1000** | **cosDPR-distil**| | ||
| |:-------------------------------------------------------------------------------------------------------------|-----------| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.460 | | ||
| | **nDCG@10** | **cosDPR-distil**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.722 | | ||
| | **R@100** | **cosDPR-distil**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.609 | | ||
| | **R@1000** | **cosDPR-distil**| | ||
| | [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.807 | | ||
|
|
||
| Note that due to the non-deterministic nature of HNSW indexing, results may differ slightly between each experimental run. | ||
| Nevertheless, scores are generally stable to the third digit after the decimal point. | ||
|
|
||
| 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). | ||
|
|
||
| ## Reproduction Log[*](reproducibility.md) | ||
|
|
||
| To add to this reproduction log, modify [this template](../../src/main/resources/docgen/templates/dl19-passage-cos-dpr-distil.template) and run `bin/build.sh` to rebuild the documentation. |
Oops, something went wrong.