|
conditions: |
|
- name: bm25-doc-tuned |
|
display: BM25 doc (k1=4.46, b=0.82) |
|
display-html: BM25 doc (<i>k<sub><small>1</small></sub></i>=4.46, <i>b</i>=0.82) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2767 |
|
R@1K: 0.9357 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2336 |
|
nDCG@10: 0.5233 |
|
R@1K: 0.6757 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3581 |
|
nDCG@10: 0.5061 |
|
R@1K: 0.7776 |
|
- name: bm25-doc-default |
|
display: BM25 doc (k1=0.9, b=0.4) |
|
display-html: BM25 doc (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (1a)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2299 |
|
R@1K: 0.8856 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2434 |
|
nDCG@10: 0.5176 |
|
R@1K: 0.6966 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3793 |
|
nDCG@10: 0.5286 |
|
R@1K: 0.8085 |
|
- name: bm25-doc-segmented-tuned |
|
display: BM25 doc segmented (k1=2.16, b=0.61) |
|
display-html: BM25 doc segmented (<i>k<sub><small>1</small></sub></i>=2.16, <i>b</i>=0.61) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2756 |
|
R@1K: 0.9311 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2398 |
|
nDCG@10: 0.5389 |
|
R@1K: 0.6565 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3458 |
|
nDCG@10: 0.5213 |
|
R@1K: 0.7725 |
|
- name: bm25-doc-segmented-default |
|
display: BM25 doc segmented (k1=0.9, b=0.4) |
|
display-html: BM25 doc segmented (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (1b)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2684 |
|
R@1K: 0.9178 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2449 |
|
nDCG@10: 0.5302 |
|
R@1K: 0.6871 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3586 |
|
nDCG@10: 0.5281 |
|
R@1K: 0.7755 |
|
- name: bm25-rm3-doc-tuned |
|
display: BM25+RM3 doc (k1=4.46, b=0.82) |
|
display-html: BM25+RM3 doc (<i>k<sub><small>1</small></sub></i>=4.46, <i>b</i>=0.82) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 --rm3 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2227 |
|
R@1K: 0.9303 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2638 |
|
nDCG@10: 0.5526 |
|
R@1K: 0.7188 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3610 |
|
nDCG@10: 0.5195 |
|
R@1K: 0.8180 |
|
- name: bm25-rm3-doc-default |
|
display: BM25+RM3 doc (k1=0.9, b=0.4) |
|
display-html: BM25+RM3 doc (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (1c)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 --rm3 --k1 0.9 --b 0.4 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.1618 |
|
R@1K: 0.8783 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2773 |
|
nDCG@10: 0.5174 |
|
R@1K: 0.7507 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4015 |
|
nDCG@10: 0.5254 |
|
R@1K: 0.8259 |
|
- name: bm25-rm3-doc-segmented-tuned |
|
display: BM25+RM3 doc segmented (k1=2.16, b=0.61) |
|
display-html: BM25+RM3 doc segmented (<i>k<sub><small>1</small></sub></i>=2.16, <i>b</i>=0.61) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --rm3 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2448 |
|
R@1K: 0.9359 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2655 |
|
nDCG@10: 0.5392 |
|
R@1K: 0.7037 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3471 |
|
nDCG@10: 0.5030 |
|
R@1K: 0.8056 |
|
- name: bm25-rm3-doc-segmented-default |
|
display: BM25+RM3 doc segmented (k1=0.9, b=0.4) |
|
display-html: BM25+RM3 doc segmented (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (1d)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --rm3 --k1 0.9 --b 0.4 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2413 |
|
R@1K: 0.9351 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2892 |
|
nDCG@10: 0.5684 |
|
R@1K: 0.7368 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3792 |
|
nDCG@10: 0.5202 |
|
R@1K: 0.8023 |
|
- name: bm25-rocchio-doc-tuned |
|
display: BM25+Rocchio doc (k1=4.46, b=0.82) |
|
display-html: BM25+Rocchio doc (<i>k<sub><small>1</small></sub></i>=4.46, <i>b</i>=0.82) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 --rocchio |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2242 |
|
R@1K: 0.9314 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2657 |
|
nDCG@10: 0.5584 |
|
R@1K: 0.7299 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3628 |
|
nDCG@10: 0.5199 |
|
R@1K: 0.8217 |
|
- name: bm25-rocchio-doc-default |
|
display: BM25+Rocchio doc (k1=0.9, b=0.4) |
|
display-html: BM25+Rocchio doc (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc --topics $topics --output $output --bm25 --rocchio --k1 0.9 --b 0.4 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.1624 |
|
R@1K: 0.8789 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2811 |
|
nDCG@10: 0.5256 |
|
R@1K: 0.7546 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4089 |
|
nDCG@10: 0.5192 |
|
R@1K: 0.8273 |
|
- name: bm25-rocchio-doc-segmented-tuned |
|
display: BM25+Rocchio doc segmented (k1=2.16, b=0.61) |
|
display-html: BM25+Rocchio doc segmented (<i>k<sub><small>1</small></sub></i>=2.16, <i>b</i>=0.61) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --rocchio --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2475 |
|
R@1K: 0.9395 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2672 |
|
nDCG@10: 0.5421 |
|
R@1K: 0.7115 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3521 |
|
nDCG@10: 0.4997 |
|
R@1K: 0.8042 |
|
- name: bm25-rocchio-doc-segmented-default |
|
display: BM25+Rocchio doc segmented (k1=0.9, b=0.4) |
|
display-html: BM25+Rocchio doc segmented (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented --topics $topics --output $output --bm25 --rocchio --k1 0.9 --b 0.4 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2447 |
|
R@1K: 0.9351 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2889 |
|
nDCG@10: 0.5570 |
|
R@1K: 0.7423 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.3830 |
|
nDCG@10: 0.5226 |
|
R@1K: 0.8102 |
|
- name: bm25-d2q-t5-doc-tuned |
|
display: BM25 w/ doc2query-T5 doc (k1=4.68, b=0.87) |
|
display-html: BM25 w/ doc2query-T5 doc (<i>k<sub><small>1</small></sub></i>=4.68, <i>b</i>=0.87) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-d2q-t5 --topics $topics --output $output --bm25 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.3269 |
|
R@1K: 0.9553 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2620 |
|
nDCG@10: 0.5972 |
|
R@1K: 0.6867 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4099 |
|
nDCG@10: 0.5852 |
|
R@1K: 0.8105 |
|
- name: bm25-d2q-t5-doc-default |
|
display: BM25 w/ doc2query-T5 doc (k1=0.9, b=0.4) |
|
display-html: BM25 w/ doc2query-T5 doc (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (2a)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-d2q-t5 --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2880 |
|
R@1K: 0.9259 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2700 |
|
nDCG@10: 0.5968 |
|
R@1K: 0.7190 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4230 |
|
nDCG@10: 0.5885 |
|
R@1K: 0.8403 |
|
- name: bm25-d2q-t5-doc-segmented-tuned |
|
display: BM25 w/ doc2query-T5 doc segmented (k1=2.56, b=0.59) |
|
display-html: BM25 w/ doc2query-T5 doc segmented (<i>k<sub><small>1</small></sub></i>=2.56, <i>b</i>=0.59) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-d2q-t5 --topics $topics --output $output --bm25 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.3209 |
|
R@1K: 0.9530 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2658 |
|
nDCG@10: 0.6273 |
|
R@1K: 0.6707 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4047 |
|
nDCG@10: 0.5943 |
|
R@1K: 0.7968 |
|
- name: bm25-d2q-t5-doc-segmented-default |
|
display: BM25 w/ doc2query-T5 doc segmented (k1=0.9, b=0.4) |
|
display-html: BM25 w/ doc2query-T5 doc segmented (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (2b)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-d2q-t5 --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 --hits 10000 --max-passage-hits 1000 --max-passage |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.3179 |
|
R@1K: 0.9490 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2798 |
|
nDCG@10: 0.6119 |
|
R@1K: 0.7165 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4150 |
|
nDCG@10: 0.5957 |
|
R@1K: 0.8046 |
|
- name: bm25-rm3-d2q-t5-doc-tuned |
|
display: BM25+RM3 w/ doc2query-T5 doc (k1=4.68, b=0.87) |
|
display-html: BM25+RM3 w/ doc2query-T5 doc (<i>k<sub><small>1</small></sub></i>=4.68, <i>b</i>=0.87) |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.2623 |
|
R@1K: 0.9522 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.2813 |
|
nDCG@10: 0.6091 |
|
R@1K: 0.7184 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4100 |
|
nDCG@10: 0.5745 |
|
R@1K: 0.8238 |
|
- name: bm25-rm3-d2q-t5-doc-default |
|
display: BM25+RM3 w/ doc2query-T5 doc (k1=0.9, b=0.4) |
|
display-html: BM25+RM3 w/ doc2query-T5 doc (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
|
display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (2c)" |
|
command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3 --k1 0.9 --b 0.4 |
|
topics: |
|
- topic_key: msmarco-doc-dev |
|
eval_key: msmarco-doc-dev |
|
scores: |
|
- MRR@10: 0.1834 |
|
R@1K: 0.9126 |
|
- topic_key: dl19-doc |
|
eval_key: dl19-doc |
|
scores: |
|
- MAP: 0.3045 |
|
nDCG@10: 0.5904 |
|
R@1K: 0.7737 |
|
- topic_key: dl20 |
|
eval_key: dl20-doc |
|
scores: |
|
- MAP: 0.4230 |
|
nDCG@10: 0.5427 |
|
R@1K: 0.8631 |
|
- name: bm25-rm3-d2q-t5-doc-segmented-tuned |
|
display: BM25+RM3 w/ doc2query-T5 doc segmented (k1=2.56, b=0.59) |
|
display-html: BM25+RM3 w/ doc2query-T5 doc segmented (<i>k<sub><small>1</small></sub></i>=2.56, <i>b</i>=0.59) |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3 --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.2973 |
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R@1K: 0.9563 |
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- topic_key: dl19-doc |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.2892 |
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nDCG@10: 0.6247 |
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R@1K: 0.7069 |
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- topic_key: dl20 |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.4016 |
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nDCG@10: 0.5711 |
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R@1K: 0.8156 |
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- name: bm25-rm3-d2q-t5-doc-segmented-default |
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display: BM25+RM3 w/ doc2query-T5 doc segmented (k1=0.9, b=0.4) |
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display-html: BM25+RM3 w/ doc2query-T5 doc segmented (<i>k<sub><small>1</small></sub></i>=0.9, <i>b</i>=0.4) |
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display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (2d)" |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3 --k1 0.9 --b 0.4 --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.2803 |
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R@1K: 0.9551 |
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- topic_key: dl19-doc |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.3030 |
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nDCG@10: 0.6290 |
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R@1K: 0.7483 |
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- topic_key: dl20 |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.4271 |
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nDCG@10: 0.5851 |
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R@1K: 0.8266 |
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- name: unicoil-noexp-pytorch |
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display: "uniCOIL (noexp): query inference with PyTorch" |
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display-html: "uniCOIL (noexp): query inference with PyTorch" |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-unicoil-noexp --topics $topics --encoder castorini/unicoil-noexp-msmarco-passage --output $output --impact --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.3410 |
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R@1K: 0.9420 |
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- topic_key: dl19-doc |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.2661 |
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nDCG@10: 0.6347 |
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R@1K: 0.6385 |
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- topic_key: dl20 |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.3698 |
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nDCG@10: 0.5906 |
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R@1K: 0.7621 |
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- name: unicoil-noexp |
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display: "uniCOIL (noexp): pre-encoded" |
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display-html: "uniCOIL (noexp): pre-encoded queries" |
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display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (3a)" |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-unicoil-noexp --topics $topics --output $output --impact --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev-unicoil-noexp |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.3409 |
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R@1K: 0.9420 |
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- topic_key: dl19-doc-unicoil-noexp |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.2665 |
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nDCG@10: 0.6349 |
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R@1K: 0.6391 |
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- topic_key: dl20-unicoil-noexp |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.3698 |
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nDCG@10: 0.5893 |
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R@1K: 0.7623 |
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- name: unicoil-pytorch |
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display: "uniCOIL (w/ doc2query-T5): query inference with PyTorch" |
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display-html: "uniCOIL (w/ doc2query-T5): query inference with PyTorch" |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-unicoil --topics $topics --encoder castorini/unicoil-msmarco-passage --output $output --impact --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.3532 |
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R@1K: 0.9546 |
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- topic_key: dl19-doc |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.2789 |
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nDCG@10: 0.6396 |
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R@1K: 0.6654 |
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- topic_key: dl20 |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.3881 |
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nDCG@10: 0.6030 |
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R@1K: 0.7866 |
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- name: unicoil |
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display: "uniCOIL (w/ doc2query-T5): pre-encoded" |
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display-html: "uniCOIL (w/ doc2query-T5): pre-encoded queries" |
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display-row: "[<a href=\"#\" data-mdb-toggle=\"tooltip\" title=\"Ma et al. (SIGIR 2021) Document Expansions and Learned Sparse Lexical Representations for MS MARCO V1 and V2.\">1</a>] — (3b)" |
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command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-doc-segmented-unicoil --topics $topics --output $output --impact --hits 10000 --max-passage-hits 1000 --max-passage |
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topics: |
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- topic_key: msmarco-doc-dev-unicoil |
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eval_key: msmarco-doc-dev |
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scores: |
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- MRR@10: 0.3531 |
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R@1K: 0.9546 |
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- topic_key: dl19-doc-unicoil |
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eval_key: dl19-doc |
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scores: |
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- MAP: 0.2789 |
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nDCG@10: 0.6396 |
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R@1K: 0.6652 |
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- topic_key: dl20-unicoil |
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eval_key: dl20-doc |
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scores: |
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- MAP: 0.3882 |
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nDCG@10: 0.6033 |
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R@1K: 0.7869 |
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