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Install MS MARCO Passage V1 Parquet regressions (#2640)
+ Performed Faiss to Parquet conversion of dense vectors + Set up regressions using Parquet + Added documentation + Fixed errors in old jsonl documentation
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#!/bin/sh | ||
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java -cp `ls target/*-fatjar.jar` -Xms512M -Xmx64G --add-modules jdk.incubator.vector $@ | ||
java -cp `ls target/*-fatjar.jar` -Xms512M -Xmx192G --add-modules jdk.incubator.vector $@ |
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...gressions/regressions-dl19-passage.bge-base-en-v1.5.parquet.flat-int8.cached.md
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# Anserini Regressions: TREC 2019 Deep Learning Track (Passage) | ||
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**Model**: [BGE-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) with quantized flat indexes (using cached queries) | ||
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This page describes regression experiments, integrated into Anserini's regression testing framework, using the [BGE-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) model on the [TREC 2019 Deep Learning Track passage ranking task](https://trec.nist.gov/data/deep2019.html), as described in the following paper: | ||
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> Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. [C-Pack: Packaged Resources To Advance General Chinese Embedding.](https://arxiv.org/abs/2309.07597) _arXiv:2309.07597_, 2023. | ||
In these experiments, we are using cached 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.bge-base-en-v1.5.parquet.flat-int8.cached.yaml). | ||
Note that this page is automatically generated from [this template](../../src/main/resources/docgen/templates/dl19-passage.bge-base-en-v1.5.parquet.flat-int8.cached.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.bge-base-en-v1.5.parquet.flat-int8.cached | ||
``` | ||
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We make available a version of the MS MARCO Passage Corpus that has already been encoded by the BGE-base-en-v1.5 model. | ||
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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.bge-base-en-v1.5.parquet.flat-int8.cached | ||
``` | ||
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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-bge-base-en-v1.5.parquet.tar -P collections/ | ||
tar xvf collections/msmarco-passage-bge-base-en-v1.5.parquet.tar -C collections/ | ||
``` | ||
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To confirm, `msmarco-passage-bge-base-en-v1.5.parquet.tar` is 39 GB and has MD5 checksum `b235e19ec492c18a18057b30b8b23fd4`. | ||
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.bge-base-en-v1.5.parquet.flat-int8.cached \ | ||
--corpus-path collections/msmarco-passage-bge-base-en-v1.5.parquet | ||
``` | ||
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## Indexing | ||
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Sample indexing command, building quantized flat indexes: | ||
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```bash | ||
bin/run.sh io.anserini.index.IndexFlatDenseVectors \ | ||
-threads 16 \ | ||
-collection ParquetDenseVectorCollection \ | ||
-input /path/to/msmarco-passage-bge-base-en-v1.5.parquet \ | ||
-generator ParquetDenseVectorDocumentGenerator \ | ||
-index indexes/lucene-flat-int8.msmarco-v1-passage.bge-base-en-v1.5/ \ | ||
-quantize.int8 \ | ||
>& logs/log.msmarco-passage-bge-base-en-v1.5.parquet & | ||
``` | ||
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The path `/path/to/msmarco-passage-bge-base-en-v1.5.parquet/` should point to the corpus downloaded above. | ||
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 | ||
bin/run.sh io.anserini.search.SearchFlatDenseVectors \ | ||
-index indexes/lucene-flat-int8.msmarco-v1-passage.bge-base-en-v1.5/ \ | ||
-topics tools/topics-and-qrels/topics.dl19-passage.bge-base-en-v1.5.jsonl.gz \ | ||
-topicReader JsonIntVector \ | ||
-output runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-int8-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt \ | ||
-hits 1000 -threads 16 & | ||
``` | ||
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Evaluation can be performed using `trec_eval`: | ||
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```bash | ||
bin/trec_eval -m map -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-int8-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt | ||
bin/trec_eval -m ndcg_cut.10 -c tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-int8-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt | ||
bin/trec_eval -m recall.100 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-int8-cached.topics.dl19-passage.bge-base-en-v1.5.jsonl.txt | ||
bin/trec_eval -m recall.1000 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-bge-base-en-v1.5.parquet.bge-flat-int8-cached.topics.dl19-passage.bge-base-en-v1.5.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** | **BGE-base-en-v1.5**| | ||
|:-------------------------------------------------------------------------------------------------------------|-----------| | ||
| [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.4435 | | ||
| **nDCG@10** | **BGE-base-en-v1.5**| | ||
| [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.7065 | | ||
| **R@100** | **BGE-base-en-v1.5**| | ||
| [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.6171 | | ||
| **R@1000** | **BGE-base-en-v1.5**| | ||
| [DL19 (Passage)](https://trec.nist.gov/data/deep2020.html) | 0.8472 | | ||
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The above figures are from running brute-force search with cached queries on non-quantized indexes. | ||
With cached queries on quantized indexes, results may differ slightly. | ||
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❗ Retrieval metrics here are computed to depth 1000 hits per query (as opposed to 100 hits per query for document ranking). | ||
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.bge-base-en-v1.5.parquet.flat-int8.cached.template) and run `bin/build.sh` to rebuild the documentation. |
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