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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>
6 lines
864 B
CSV
6 lines
864 B
CSV
,EM,f1,top_n_accuracy,top_n,reader_time,seconds_per_query,passages_per_second,reader,error
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0,0.7589752233271532,0.8067985794671885,0.9671329849991572,5,133.79706027999998,0.011275666634080564,92.30397120949361,deepset/roberta-base-squad2,
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1,0.7359683128265633,0.7823306265318686,0.9714309792684982,5,125.22323393199997,0.010553112584864317,98.62387044489225,deepset/minilm-uncased-squad2,
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2,0.700825889094893,0.7490271600053505,0.9585369964604753,5,123.58959278499992,0.010415438461570867,99.92750782409666,deepset/bert-base-cased-squad2,
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3,0.7821506826226192,0.8264545708097472,0.9762346199224675,5,312.42233685099995,0.026329204184308102,39.529824033964466,deepset/bert-large-uncased-whole-word-masking-squad2,
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4,0.8099612337771785,0.8526275190954586,0.9772459126917242,5,314.3179854819998,0.026488958830439897,39.29142006004379,deepset/xlm-roberta-large-squad2, |