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beir-v1.0.0-cqadupstack-mathematica.bge-base-en-v1.5.flat.cached.template
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beir-v1.0.0-cqadupstack-mathematica.bge-base-en-v1.5.flat.cached.template
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# Anserini Regressions: BEIR (v1.0.0) — CQADupStack-mathematica
**Model**: [BGE-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) with flat indexes (using cached queries)
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 [BEIR (v1.0.0) — CQADupStack-mathematica](http://beir.ai/), as described in the following paper:
> 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).
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}
```
All the BEIR corpora, encoded by the BGE-base-en-v1.5 model, are available for download:
```bash
wget https://rgw.cs.uwaterloo.ca/pyserini/data/beir-v1.0.0-bge-base-en-v1.5.tar -P collections/
tar xvf collections/beir-v1.0.0-bge-base-en-v1.5.tar -C collections/
```
The tarball is 294 GB and has MD5 checksum `e4e8324ba3da3b46e715297407a24f00`.
After download and unpacking the corpora, the `run_regression.py` command above should work without any issue.
## Indexing
Sample indexing command, building flat indexes:
```
${index_cmds}
```
The path `/path/to/${corpus}/` should point to the corpus downloaded above.
## 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}
Note that since we're running brute-force search with cached queries on non-quantized flat indexes, the results should be reproducible _exactly_.