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beir-v1.0.0-fiqa-splade-distil-cocodenser-medium.template
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beir-v1.0.0-fiqa-splade-distil-cocodenser-medium.template
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# Anserini Regressions: BEIR (v1.0.0) — FiQA-2018
**Model**: SPLADE-distil CoCodenser Medium
This page describes regression experiments, integrated into Anserini's regression testing framework, using SPLADE-distil CoCodenser Medium on [BEIR (v1.0.0) — FiQA-2018](http://beir.ai/).
The SPLADE-distil CoCodenser Medium model is open-sourced by [Naver Labs Europe](https://europe.naverlabs.com/research/machine-learning-and-optimization/splade-models).
The exact configurations for these regressions are stored in [this YAML file](${yaml}).
Note that this page is automatically generated from [this template](${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:
```
python src/main/python/run_regression.py --index --verify --search \
--regression ${test_name}
```
## Corpus
We make available a version of the BEIR-v1.0.0 fiqa corpus that has already been processed with SPLADE-distil CoCodenser Medium, i.e., gone through document expansion and term reweighting.
Thus, no neural inference is involved.
For details on how to train SPLADE-distil CoCodenser Medium and perform inference, please see [guide provided by Naver Labs Europe](https://github.com/naver/splade/tree/main/anserini_evaluation).
Download the corpus and unpack into `collections/`:
```
wget https://rgw.cs.uwaterloo.ca/JIMMYLIN-bucket0/data/beir-v1.0.0-splade_distil_cocodenser_medium-fiqa.tar -P collections/
tar xvf collections/beir-v1.0.0-splade_distil_cocodenser_medium-fiqa.tar -C collections/
```
To confirm, the tarball is 48 MB and has MD5 checksum `781f7683b6e73971afd01df1650756bf`.
With the corpus downloaded, the following command will perform the complete regression, end to end, on any machine:
```
python src/main/python/run_regression.py --index --verify --search \
--regression ${test_name} \
--corpus-path collections/beir-v1.0.0-splade_distil_cocodenser_medium-fiqa
```
Alternatively, you can simply copy/paste from the commands below and obtain the same results.
## Indexing
Sample indexing command:
```
${index_cmds}
```
The path `/path/to/${corpus}/` should point to the corpus downloaded above.
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.
Upon completion, we should have an index with 57,638 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.
After indexing has completed, you should be able to perform retrieval as follows:
```
${ranking_cmds}
```
Evaluation can be performed using `trec_eval`:
```
${eval_cmds}
```
## Effectiveness
With the above commands, you should be able to reproduce the following results:
${effectiveness}
## Reproduction Log[*](reproducibility.md)
To add to this reproduction log, modify [this template](${template}) and run `bin/build.sh` to rebuild the documentation.