conditions:
  - name: bm25-rocchio-d2q-t5-tuned
    display: BM25+Rocchio w/ doc2query-T5 (k1=2.18, b=0.86)
    display-html: BM25+Rocchio w/ doc2query-T5 (<i>k<sub><small>1</small></sub></i>=2.18, <i>b</i>=0.86)
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-d2q-t5-docvectors --topics $topics --output $output --bm25 --rocchio
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2395
            R@1K: 0.9535
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4339
            nDCG@10: 0.6559
            R@1K: 0.8465
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4376
            nDCG@10: 0.6224
            R@1K: 0.8641
  - name: bm25-rocchio-d2q-t5-default
    display: BM25+Rocchio w/ doc2query-T5 (k1=0.9, b=0.4)
    display-html: BM25+Rocchio w/ doc2query-T5 (<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-passage-d2q-t5-docvectors --topics $topics --output $output --bm25 --rocchio --k1 0.9 --b 0.4
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2158
            R@1K: 0.9467
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4469
            nDCG@10: 0.6538
            R@1K: 0.8855
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4246
            nDCG@10: 0.6102
            R@1K: 0.8675
  - name: bm25-rocchio-default
    display: BM25+Rocchio (k1=0.9, b=0.4)
    display-html: BM25+Rocchio (<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-passage --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 --rocchio
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1595
            R@1K: 0.8620
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3474
            nDCG@10: 0.5275
            R@1K: 0.8007
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.3115
            nDCG@10: 0.4910
            R@1K: 0.8156
  - name: bm25-rocchio-tuned
    display: BM25+Rocchio (k1=0.82, b=0.68)
    display-html: BM25+Rocchio (<i>k<sub><small>1</small></sub></i>=0.82, <i>b</i>=0.68)
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage --topics $topics --output $output --bm25 --rocchio
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1684
            R@1K: 0.8726
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3396
            nDCG@10: 0.5275
            R@1K: 0.7948
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.3120
            nDCG@10: 0.4908
            R@1K: 0.8327
  - name: distilbert-kd-tasb-pytorch
    display: "DistilBERT KD TASB: query inference with PyTorch"
    display-html: "DistilBERT KD TASB: query inference with PyTorch"
    display-row: "[5]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.distilbert-dot-tas_b-b256 --topics $topics --encoder sebastian-hofstaetter/distilbert-dot-tas_b-b256-msmarco --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3444
            R@1K: 0.9771
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4590
            nDCG@10: 0.7210
            R@1K: 0.8406
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4698
            nDCG@10: 0.6854
            R@1K: 0.8727
  - name: distilbert-kd-tasb
    display: "DistilBERT KD TASB: pre-encoded"
    display-html: "DistilBERT KD TASB: pre-encoded queries"
    display-row: "[5]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.distilbert-dot-tas_b-b256 --topics $topics --encoded-queries distilbert_tas_b-$topics --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3444
            R@1K: 0.9771
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4590
            nDCG@10: 0.7210
            R@1K: 0.8406
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4698
            nDCG@10: 0.6854
            R@1K: 0.8727
  - name: distilbert-kd-pytorch
    display: "DistilBERT KD: query inference with PyTorch"
    display-html: "DistilBERT KD: query inference with PyTorch"
    display-row: "[4]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.distilbert-dot-margin-mse-t2 --topics $topics --encoder sebastian-hofstaetter/distilbert-dot-margin_mse-T2-msmarco --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3251
            R@1K: 0.9553
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4053
            nDCG@10: 0.6994
            R@1K: 0.7653
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4159
            nDCG@10: 0.6447
            R@1K: 0.7953
  - name: distilbert-kd
    display: "DistilBERT KD: pre-encoded"
    display-html: "DistilBERT KD: pre-encoded queries"
    display-row: "[4]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.distilbert-dot-margin-mse-t2 --topics $topics --encoded-queries distilbert_kd-$topics --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3251
            R@1K: 0.9553
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4053
            nDCG@10: 0.6994
            R@1K: 0.7653
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4159
            nDCG@10: 0.6447
            R@1K: 0.7953
  - name: ance-pytorch
    display: "ANCE: query inference with PyTorch"
    display-html: "ANCE: query inference with PyTorch"
    display-row: "[3]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.ance --topics $topics --encoder castorini/ance-msmarco-passage --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3302
            R@1K: 0.9587
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3710
            nDCG@10: 0.6452
            R@1K: 0.7554
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4076
            nDCG@10: 0.6458
            R@1K: 0.7764
  - name: ance
    display: "ANCE: pre-encoded"
    display-html: "ANCE: pre-encoded queries"
    display-row: "[3]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.ance --topics $topics --encoded-queries ance-$topics --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3302
            R@1K: 0.9584
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3710
            nDCG@10: 0.6452
            R@1K: 0.7554
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4076
            nDCG@10: 0.6458
            R@1K: 0.7764
  - name: bm25-tuned
    display: BM25 (k1=0.82, b=0.68)
    display-html: BM25 (<i>k<sub><small>1</small></sub></i>=0.82, <i>b</i>=0.68)
    command: python -m pyserini.search.lucene --topics $topics --index msmarco-v1-passage --output $output --bm25
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1875
            R@1K: 0.8573
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.2903
            nDCG@10: 0.4973
            R@1K: 0.7450
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.2876
            nDCG@10: 0.4876
            R@1K: 0.8031
  - name: bm25-rm3-tuned
    display: BM25+RM3 (k1=0.82, b=0.68)
    display-html: BM25+RM3 (<i>k<sub><small>1</small></sub></i>=0.82, <i>b</i>=0.68)
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage --topics $topics --output $output --bm25 --rm3
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1646
            R@1K: 0.8704
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3339
            nDCG@10: 0.5147
            R@1K: 0.7950
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.3017
            nDCG@10: 0.4924
            R@1K: 0.8292
  - name: bm25-default
    display: BM25 (k1=0.9, b=0.4)
    display-html: BM25 (<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>] &mdash; (1a)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage --topics $topics --output $output --bm25 --k1 0.9 --b 0.4
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1840
            R@1K: 0.8526
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3013
            nDCG@10: 0.5058
            R@1K: 0.7501
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.2856
            nDCG@10: 0.4796
            R@1K: 0.7863
  - name: bm25-rm3-default
    display: BM25+RM3 (k1=0.9, b=0.4)
    display-html: BM25+RM3 (<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>] &mdash; (1b)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage --topics $topics --output $output --bm25 --k1 0.9 --b 0.4 --rm3
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.1566
            R@1K: 0.8606
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.3416
            nDCG@10: 0.5216
            R@1K: 0.8136
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.3006
            nDCG@10: 0.4896
            R@1K: 0.8236
  - name: bm25-d2q-t5-tuned
    display: BM25 w/ doc2query-T5 (k1=2.18, b=0.86)
    display-html: BM25 w/ doc2query-T5 (<i>k<sub><small>1</small></sub></i>=2.18, <i>b</i>=0.86)
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-d2q-t5 --topics $topics --output $output --bm25
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2816
            R@1K: 0.9506
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4046
            nDCG@10: 0.6336
            R@1K: 0.8134
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4171
            nDCG@10: 0.6265
            R@1K: 0.8393
  - name: bm25-d2q-t5-default
    display: BM25 w/ doc2query-T5 (k1=0.9, b=0.4)
    display-html: BM25 w/ doc2query-T5 (<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>] &mdash; (2a)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-d2q-t5 --topics $topics --output $output --bm25 --k1 0.9 --b 0.4
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2723
            R@1K: 0.9470
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4034
            nDCG@10: 0.6417
            R@1K: 0.8310
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4074
            nDCG@10: 0.6187
            R@1K: 0.8452
  - name: bm25-rm3-d2q-t5-tuned
    display: BM25+RM3 w/ doc2query-T5 (k1=2.18, b=0.86)
    display-html: BM25+RM3 w/ doc2query-T5 (<i>k<sub><small>1</small></sub></i>=2.18, <i>b</i>=0.86)
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2382
            R@1K: 0.9528
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4377
            nDCG@10: 0.6537
            R@1K: 0.8443
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4348
            nDCG@10: 0.6235
            R@1K: 0.8605
  - name: bm25-rm3-d2q-t5-default
    display: BM25+RM3 w/ doc2query-T5 (k1=0.9, b=0.4)
    display-html: BM25+RM3 w/ doc2query-T5 (<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>] &mdash; (2b)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-d2q-t5-docvectors --topics $topics --output $output --bm25 --rm3 --k1 0.9 --b 0.4
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.2139
            R@1K: 0.9460
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4483
            nDCG@10: 0.6586
            R@1K: 0.8863
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4286
            nDCG@10: 0.6131
            R@1K: 0.8700
  - name: unicoil-pytorch
    display: "uniCOIL (w/ doc2query-T5): query inference with PyTorch"
    display-html: "uniCOIL (w/ doc2query-T5): query inference with PyTorch"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil --topics $topics --encoder castorini/unicoil-msmarco-passage --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3509
            R@1K: 0.9581
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4617
            nDCG@10: 0.7027
            R@1K: 0.8291
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4429
            nDCG@10: 0.6745
            R@1K: 0.8433
  - name: unicoil-onnx
    display: "uniCOIL (w/ doc2query-T5): query inference with ONNX"
    display-html: "uniCOIL (w/ doc2query-T5): query inference with ONNX"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil --topics $topics --onnx-encoder UniCoil --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3509
            R@1K: 0.9581
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4617
            nDCG@10: 0.7027
            R@1K: 0.8291
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4429
            nDCG@10: 0.6745
            R@1K: 0.8433
  - name: unicoil
    display: "uniCOIL (w/ doc2query-T5): pre-encoded"
    display-html: "uniCOIL (w/ doc2query-T5): pre-encoded queries"
    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>] &mdash; (3b)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil --topics $topics --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset-unicoil
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3516
            R@1K: 0.9582
      - topic_key: dl19-passage-unicoil
        eval_key: dl19-passage
        scores:
          - MAP: 0.4612
            nDCG@10: 0.7024
            R@1K: 0.8292
      - topic_key: dl20-unicoil
        eval_key: dl20-passage
        scores:
          - MAP: 0.4430
            nDCG@10: 0.6745
            R@1K: 0.8430
  - name: unicoil-noexp-pytorch
    display: "uniCOIL (noexp): query inference with PyTorch"
    display-html: "uniCOIL (noexp): query inference with PyTorch"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil-noexp --topics $topics --encoder castorini/unicoil-noexp-msmarco-passage --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3153
            R@1K: 0.9239
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4033
            nDCG@10: 0.6434
            R@1K: 0.7752
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4022
            nDCG@10: 0.6524
            R@1K: 0.7861
  - name: unicoil-noexp-onnx
    display: "uniCOIL (noexp): query inference with ONNX"
    display-html: "uniCOIL (noexp): query inference with ONNX"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil-noexp --topics $topics --onnx-encoder UniCoil --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3119
            R@1K: 0.9239
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4061
            nDCG@10: 0.6531
            R@1K: 0.7809
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.3909
            nDCG@10: 0.6388
            R@1K: 0.7915
  - name: unicoil-noexp
    display: "uniCOIL (noexp): pre-encoded"
    display-html: "uniCOIL (noexp): pre-encoded queries"
    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>] &mdash; (3a)"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-unicoil-noexp --topics $topics --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset-unicoil-noexp
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3153
            R@1K: 0.9239
      - topic_key: dl19-passage-unicoil-noexp
        eval_key: dl19-passage
        scores:
          - MAP: 0.4033
            nDCG@10: 0.6433
            R@1K: 0.7752
      - topic_key: dl20-unicoil-noexp
        eval_key: dl20-passage
        scores:
          - MAP: 0.4021
            nDCG@10: 0.6523
            R@1K: 0.7861
  - name: splade-pp-ed-onnx
    display: "SPLADE++ EnsembleDistil: query inference with ONNX"
    display-html: "SPLADE++ EnsembleDistil: query inference with ONNX"
    display-row: "[2]"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-splade-pp-ed --topics $topics --onnx-encoder SpladePlusPlusEnsembleDistil --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3830
            R@1K: 0.9831
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.5054
            nDCG@10: 0.7320
            R@1K: 0.8724
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.5002
            nDCG@10: 0.7198
            R@1K: 0.8995
  - name: splade-pp-sd-onnx
    display: "SPLADE++ SelfDistil: query inference with ONNX"
    display-html: "SPLADE++ SelfDistil: query inference with ONNX"
    display-row: "[2]"
    command: python -m pyserini.search.lucene --threads 16 --batch-size 128 --index msmarco-v1-passage-splade-pp-sd --topics $topics --onnx-encoder SpladePlusPlusSelfDistil --output $output --hits 1000 --impact
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3778
            R@1K: 0.9846
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4997
            nDCG@10: 0.7356
            R@1K: 0.8758
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.5140
            nDCG@10: 0.7285
            R@1K: 0.9023   
  - name: tct_colbert-v2-hnp-pytorch
    display: "TCT_ColBERT-V2-HN+: query inference with PyTorch"
    display-html: "TCT_ColBERT-V2-HN+: query inference with PyTorch"
    display-row: "[6]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.tct_colbert-v2-hnp --topics $topics --encoder castorini/tct_colbert-v2-hnp-msmarco --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3584
            R@1K: 0.9695
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4469
            nDCG@10: 0.7204
            R@1K: 0.8261
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4754
            nDCG@10: 0.6882
            R@1K: 0.8429
  - name: tct_colbert-v2-hnp
    display: "TCT_ColBERT-V2-HN+: pre-encoded"
    display-html: "TCT_ColBERT-V2-HN+: pre-encoded queries"
    display-row: "[6]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.tct_colbert-v2-hnp --topics $topics --encoded-queries tct_colbert-v2-hnp-$topics --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3584
            R@1K: 0.9695
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4469
            nDCG@10: 0.7204
            R@1K: 0.8261
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4754
            nDCG@10: 0.6882
            R@1K: 0.8429
  - name: slimr
    display: "SLIM: query inference with PyTorch"
    display-html: "SLIM: query inference with PyTorch"
    display-row: "[7]"
    command: python -m pyserini.search.lucene --threads 16 --batch 128 --index msmarco-v1-passage-slimr --topics $topics --encoder castorini/slimr-msmarco-passage --encoded-corpus scipy-sparse-vectors.msmarco-v1-passage-slimr --output $output --output-format msmarco --hits 1000 --impact --min-idf 3
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3581
            R@1K: 0.9620
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4509
            nDCG@10: 0.7010
            R@1K: 0.8241
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4419
            nDCG@10: 0.6403
            R@1K: 0.8543
  - name: slimr-pp
    display: "SLIM++: query inference with PyTorch"
    display-html: "SLIM++: query inference with PyTorch"
    display-row: "[7]"
    command: python -m pyserini.search.lucene --threads 16 --batch 128 --index msmarco-v1-passage-slimr-pp --topics $topics --encoder castorini/slimr-pp-msmarco-passage --encoded-corpus scipy-sparse-vectors.msmarco-v1-passage-slimr-pp --output $output --output-format msmarco --hits 1000 --impact --min-idf 3
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.4032
            R@1K: 0.9680
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4687
            nDCG@10: 0.7140
            R@1K: 0.8415
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4906
            nDCG@10: 0.7021
            R@1K: 0.8551
  - name: aggretriever-distilbert-pytorch
    display: "Aggretriever-DistilBERT: query inference with PyTorch"
    display-html: "Aggretriever-DistilBERT: query inference with PyTorch"
    display-row: "[8]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.aggretriever-distilbert --topics $topics --encoder castorini/aggretriever-distilbert --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3412
            R@1K: 0.9604
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4301
            nDCG@10: 0.6816
            R@1K: 0.8023
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4329
            nDCG@10: 0.6726
            R@1K: 0.8351
  - name: aggretriever-cocondenser-pytorch
    display: "Aggretriever-coCondenser: query inference with PyTorch"
    display-html: "Aggretriever-coCondenser: query inference with PyTorch"
    display-row: "[8]"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 512 --index msmarco-v1-passage.aggretriever-cocondenser --topics $topics --encoder castorini/aggretriever-cocondenser --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3619
            R@1K: 0.9735
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4350
            nDCG@10: 0.6837
            R@1K: 0.8078
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4710
            nDCG@10: 0.6972
            R@1K: 0.8555
  - name: openai-ada2
    display: "OpenAI ada2: pre-encoded queries"
    display-html: "OpenAI ada2: pre-encoded queries"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 128 --index msmarco-v1-passage.openai-ada2 --topics $topics --encoded-queries openai-ada2-$topics --output $output
    topics:
      - topic_key: msmarco-passage-dev-subset
        eval_key: msmarco-passage-dev-subset
        scores:
          - MRR@10: 0.3435
            R@1K: 0.9858
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.4788
            nDCG@10: 0.7035
            R@1K: 0.8629
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4771
            nDCG@10: 0.6759
            R@1K: 0.8705
  - name: openai-ada2-hyde
    display: "HyDE-OpenAI ada2: pre-encoded queries"
    display-html: "HyDE-OpenAI ada2: pre-encoded queries"
    command: python -m pyserini.search.faiss --threads 16 --batch-size 128 --index msmarco-v1-passage.openai-ada2 --topics $topics --encoded-queries openai-ada2-$topics-hyde --output $output
    topics:
      - topic_key: dl19-passage
        eval_key: dl19-passage
        scores:
          - MAP: 0.5125
            nDCG@10: 0.7163
            R@1K: 0.9002
      - topic_key: dl20
        eval_key: dl20-passage
        scores:
          - MAP: 0.4938
            nDCG@10: 0.6666
            R@1K: 0.8919