mirror of
https://github.com/deepset-ai/haystack.git
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1cebcb7dda
* add time and perf benchmark for es * Add retriever benchmarking * Add Reader benchmarking * add nq to squad conversion * add conversion stats * clean benchmarks * Add link to dataset * Update imports * add first support for neg psgs * Refactor test * set max_seq_len * cleanup benchmark * begin retriever speed benchmarking * Add support for retriever query index benchmarking * improve reader eval, retriever speed benchmarking * improve retriever speed benchmarking * Add retriever accuracy benchmark * Add neg doc shuffling * Add top_n * 3x speedup of SQL. add postgres docker run. make shuffle neg a param. add more logging * Add models to sweep * add option for faiss index type * remove unneeded line * change faiss to faiss_flat * begin automatic benchmark script * remove existing postgres docker for benchmarking * Add data processing scripts * Remove shuffle in script bc data already shuffled * switch hnsw setup from 256 to 128 * change es similarity to dot product by default * Error includes stack trace * Change ES default timeout * remove delete_docs() from timing for indexing * Add support for website export * update website on push to benchmarks * add complete benchmarks results * new json format * removed NaN as is not a valid json token * fix benchmarking for faiss hnsw queries. do sql calls in update_embeddings() as batches * update benchmarks for hnsw 128,20,80 * don't delete full index in delete_all_documents() * update texts for charts * update recall column for retriever * change scale and add units to desc * add units to legend * add axis titles. update desc * add html tags Co-authored-by: deepset <deepset@Crenolape.localdomain> Co-authored-by: Malte Pietsch <malte.pietsch@deepset.ai> Co-authored-by: PiffPaffM <markuspaff.mp@gmail.com>
105 lines
1.9 KiB
JSON
105 lines
1.9 KiB
JSON
{
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"chart_type": "LineChart",
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"title": "Retriever Speed",
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"subtitle": "Query Speed at different number of docs",
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"description": "Here you can see how the query speed of different Retriever / DocumentStore combinations scale as the number of documents increases. The set up is the same as the above querying benchmark except that a varying number of negative documents are used to fill the document store.",
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"columns": [
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"n_docs",
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"BM25 / ElasticSearch",
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"DPR / ElasticSearch",
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"DPR / FAISS (flat)",
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"DPR / FAISS (HSNW)"
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],
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"axis": [
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{ "x": "Number of docs", "y": "Docs/sec" }
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],
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"data":
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[
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[
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"model",
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"n_docs",
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"query_speed"
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],
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[
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"DPR / ElasticSearch",
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1000,
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40.802
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],
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[
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"BM25 / ElasticSearch",
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1000,
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232.97799999999998
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],
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[
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"BM25 / ElasticSearch",
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10000,
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167.81
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],
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[
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"DPR / ElasticSearch",
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10000,
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27.006999999999998
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],
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[
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"DPR / ElasticSearch",
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100000,
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6.5360000000000005
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],
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[
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"BM25 / ElasticSearch",
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100000,
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162.996
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],
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[
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"DPR / FAISS (flat)",
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1000,
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40.048
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],
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[
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"DPR / FAISS (flat)",
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10000,
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23.976999999999997
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],
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[
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"DPR / FAISS (flat)",
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100000,
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5.044
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],
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[
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"DPR / FAISS (HSNW)",
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1000,
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37.884
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],
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[
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"DPR / FAISS (HSNW)",
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10000,
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33.421
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],
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[
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"DPR / FAISS (HSNW)",
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100000,
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12.815
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],
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[
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"DPR / ElasticSearch",
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500000,
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1.514
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],
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[
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"DPR / FAISS (flat)",
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500000,
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1.091
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],
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[
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"BM25 / ElasticSearch",
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500000,
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95.491
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],
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[
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"DPR / FAISS (HSNW)",
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500000,
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3.259
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]
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]
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} |