Skip to content

Latest commit

 

History

History
548 lines (461 loc) · 38.1 KB

fatjar-regressions-v0.39.0.md

File metadata and controls

548 lines (461 loc) · 38.1 KB

Anserini Fatjar Regresions (v0.39.0)

Fetch the fatjar:

wget https://repo1.maven.org/maven2/io/anserini/anserini/0.39.0/anserini-0.39.0-fatjar.jar

Let's start out by setting the ANSERINI_JAR and the OUTPUT_DIR:

export ANSERINI_JAR="anserini-0.39.0-fatjar.jar"
export OUTPUT_DIR="."

❗ Anserini ships with a number of prebuilt indexes, which it'll automagically download for you. This is a great feature, but the indexes can take up a lot of space. See this guide on prebuilt indexes for more details.

Webapp and REST API

Anserini has a built-in webapp for interactive querying along with a REST API that can be used by other applications. To start the REST API:

java -cp $ANSERINI_JAR io.anserini.server.Application --server.port=8081

And then navigate to http://localhost:8081/ in your browser.

Here's a specific example of using the REST API to issue the query "How does the process of digestion and metabolism of carbohydrates start" to msmarco-v2.1-doc-segmented:

curl -X GET "http://localhost:8081/api/v1.0/indexes/msmarco-v2.1-doc-segmented/search?query=How%20does%20the%20process%20of%20digestion%20and%20metabolism%20of%20carbohydrates%20start"

The json results are the same as the output of the -outputRerankerRequests option in SearchCollection, described below for "MS MARCO V2.1 + TREC RAG". Use the hits parameter to specify the number of hits to return, e.g., hits=1000 to return the top 1000 hits.

Details of the built-in webapp and REST API can be found here.

❗ Beware, the above commands will trigger automatic downloading of prebuilt indexes, which take up a lot of space. The msmarco-v2.1-doc prebuilt index is 63 GB uncompressed. The msmarco-v2.1-doc-segmented prebuilt index is 84 GB uncompressed. See this guide on prebuilt indexes for more details.

MS MARCO V2.1 + TREC RAG

For the TREC RAG Track, Anserini so far has only BM25 baselines. The evaluation uses the MS MARCO V2.1 corpora, which has two "variants", documents and segmented documents:

  • The segmented documents corpus (segments = passages) is the one actually used for the TREC RAG evaluations. It contains 113,520,750 passages.
  • The documents corpus is the source of the segments and useful as a point of reference. It contains 10,960,555 documents.

❗ Beware, you need lots of space to run these experiments. The msmarco-v2.1-doc prebuilt index is 63 GB uncompressed. The msmarco-v2.1-doc-segmented prebuilt index is 84 GB uncompressed. Both indexes will be downloaded automatically. See this guide on prebuilt indexes for more details.

This release of Anserini comes with bindings for the test topics for the TREC 2024 RAG track (-topics rag24.test). To generate a standard TREC run file for these topics (top-1000 hits, BM25), issue the following command:

java -cp $ANSERINI_JAR io.anserini.search.SearchCollection \
  -index msmarco-v2.1-doc-segmented \
  -topics rag24.test \
  -output $OUTPUT_DIR/run.msmarco-v2.1-doc-segmented.bm25.rag24.test.txt \
  -bm25 -hits 1000

The UMBRELA qrels are included in this release. To evaluate using them:

java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 rag24.test-umbrela-all \
  $OUTPUT_DIR/run.msmarco-v2.1-doc-segmented.bm25.rag24.test.txt

The expected results:

ndcg_cut_10           	all	0.3290

To generate jsonl output containing the raw documents that can be reranked and further processed, use the -outputRerankerRequests option to specify an output file. For example:

java -cp $ANSERINI_JAR io.anserini.search.SearchCollection \
  -index msmarco-v2.1-doc-segmented \
  -topics rag24.test \
  -output $OUTPUT_DIR/run.msmarco-v2.1-doc-segmented.bm25.rag24.test.txt \
  -bm25 -hits 20 \
  -outputRerankerRequests $OUTPUT_DIR/results.msmarco-v2.1-doc-segmented.bm25.rag24.test.jsonl

In the above command, we only fetch the top-20 hits. To examine the output, pipe through jq to pretty-print:

$ head -n 1 $OUTPUT_DIR/results.msmarco-v2.1-doc-segmented.bm25.rag24.test.jsonl | jq
{
  "query": {
    "qid": "2024-105741",
    "text": "is it dangerous to have wbc over 15,000 without treatment?"
  },
  "candidates": [
    {
      "docid": "msmarco_v2.1_doc_16_287012450#4_490828734",
      "score": 15.8199,
      "doc": {
        "url": "https://emedicine.medscape.com/article/961169-treatment",
        "title": "Bacteremia Treatment & Management: Medical Care",
        "headings": "Bacteremia Treatment & Management\nBacteremia Treatment & Management\nMedical Care\nHow well do low-risk criteria work?\nEmpiric antibiotics: How well do they work?\nTreatment algorithms\n",
        "segment": "band-to-neutrophil ratio\n< 0.2\n< 20,000/μL\n5-15,000/μL; ABC < 1,000\n5-15,000/μL; ABC < 1,000\nUrine assessment\n< 10 WBCs per HPF; Negative for bacteria\n< 10 WBCs per HPF; Leukocyte esterase negative\n< 10 WBCs per HPF\n< 5 WBCs per HPF\nCSF assessment\n< 8 WBCs per HPF; Negative for bacteria\n< 10 WBCs per HPF\n< 10-20 WBCs per HPF\n…\nChest radiography\nNo infiltrate\nWithin reference range, if obtained\nWithin reference range, if obtained\n…\nStool culture\n< 5 WBCs per HPF\n…\n< 5 WBCs per HPF\n…\n* Acute illness observation score\nHow well do low-risk criteria work? The above guidelines are presented to define a group of febrile young infants who can be treated without antibiotics. Statistically, this translates into a high NPV (ie, a very high proportion of true negative cultures is observed in patients deemed to be at low risk). The NPV of various low-risk criteria for serious bacterial infection and occult bacteremia are as follows [ 10, 14, 16, 19, 74, 75, 76] : Philadelphia NPV - 95-100%\nBoston NPV - 95-98%\nRochester NPV - 98.3-99%\nAAP 1993 - 99-99.8%\nIn basic terms, even by the most stringent criteria, somewhere between 1 in 100 and 1 in 500 low-risk, but bacteremic, febrile infants are missed.",
        "start_char": 2846,
        "end_char": 4049
      }
    },
    {
      "docid": "msmarco_v2.1_doc_16_287012450#3_490827079",
      "score": 15.231,
      "doc": {
        "url": "https://emedicine.medscape.com/article/961169-treatment",
        "title": "Bacteremia Treatment & Management: Medical Care",
        "headings": "Bacteremia Treatment & Management\nBacteremia Treatment & Management\nMedical Care\nHow well do low-risk criteria work?\nEmpiric antibiotics: How well do they work?\nTreatment algorithms\n",
        "segment": "73] Since then, numerous studies have evaluated combinations of age, temperature, history, examination findings, and laboratory results to determine which young infants are at a low risk for bacterial infection. [ 10, 66, 74, 75, 76]\nThe following are the low-risk criteria established by groups from Philadelphia, Boston, and Rochester and the 1993 American Academy of Pediatrics (AAP) guideline. Table 11. Low-Risk Criteria for Infants Younger than 3 Months [ 10, 74, 75, 76] (Open Table in a new window)\nCriterion\nPhiladelphia\nBoston\nRochester\nAAP 1993\nAge\n1-2 mo\n1-2 mo\n0-3 mo\n1-3 mo\nTemperature\n38.2°C\n≥38°C\n≥38°C\n≥38°C\nAppearance\nAIOS * < 15\nWell\nAny\nWell\nHistory\nImmune\nNo antibiotics in the last 24 h; No immunizations in the last 48 h\nPreviously healthy\nPreviously healthy\nExamination\nNonfocal\nNonfocal\nNonfocal\nNonfocal\nWBC count\n< 15,000/μL; band-to-neutrophil ratio\n< 0.2\n< 20,000/μL\n5-15,000/μL; ABC < 1,000\n5-15,000/μL; ABC < 1,000\nUrine assessment\n< 10 WBCs per HPF; Negative for bacteria\n< 10 WBCs per HPF; Leukocyte esterase negative\n< 10 WBCs per HPF\n< 5 WBCs per HPF\nCSF assessment\n< 8 WBCs per HPF;",
        "start_char": 1993,
        "end_char": 3111
      }
    },
    ...
  ]
}

Replace -index msmarco-v2.1-doc with -index msmarco-v2.1-doc-segemented if you want to search over the doc segments instead of the full docs.

The experiments below capture document-level qrels originally targeted at the V2 documents corpus, but have been "projected" over to the V2.1 documents corpus. These can be viewed as dev topics for the TREC 2024 RAG Track (and were released prior to the evaluation). The table below reports effectiveness (dev in terms of RR@100, DL21-DL23, RAGgy in terms of nDCG@10):

dev dev2 DL21 DL22 DL23 RAGgy
BM25 doc (k1=0.9, b=0.4) 0.1654 0.1732 0.5183 0.2991 0.2914 0.3631
BM25 doc-segmented (k1=0.9, b=0.4) 0.1973 0.2000 0.5778 0.3576 0.3356 0.4227

The follow command will reproduce the above experiments:

java -cp $ANSERINI_JAR io.anserini.reproduce.RunMsMarco -collection msmarco-v2.1
Manual runs and evaluation

The following snippet will generate the complete set of results that corresponds to the above table:

# doc condition
TOPICS=(msmarco-v2-doc.dev msmarco-v2-doc.dev2 dl21-doc dl22-doc dl23-doc rag24.raggy-dev); for t in "${TOPICS[@]}"
do
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index msmarco-v2.1-doc -topics $t -output $OUTPUT_DIR/run.msmarco-v2.1.doc.${t}.txt -threads 16 -bm25
done

# doc-segmented condition
TOPICS=(msmarco-v2-doc.dev msmarco-v2-doc.dev2 dl21-doc dl22-doc dl23-doc rag24.raggy-dev); for t in "${TOPICS[@]}"
do
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index msmarco-v2.1-doc-segmented -topics $t -output $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.${t}.txt -threads 16 -bm25 -hits 10000 -selectMaxPassage -selectMaxPassage.delimiter "#" -selectMaxPassage.hits 1000
done

And here's the snippet of code to perform the evaluation (which will yield the results above):

# doc condition
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank msmarco-v2.1-doc.dev $OUTPUT_DIR/run.msmarco-v2.1.doc.msmarco-v2-doc.dev.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank msmarco-v2.1-doc.dev2 $OUTPUT_DIR/run.msmarco-v2.1.doc.msmarco-v2-doc.dev2.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl21-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl22-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc.dl23-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc.rag24.raggy-dev.txt

# doc-segmented condition
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank msmarco-v2.1-doc.dev $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.msmarco-v2-doc.dev.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank msmarco-v2.1-doc.dev2 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.msmarco-v2-doc.dev2.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl21-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl21-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl21-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl22-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl22-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl22-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl23-doc.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 dl23-doc-msmarco-v2.1 $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.dl23-doc.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m map rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -M 100 -m recip_rank -c -m ndcg_cut.10 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.100 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.rag24.raggy-dev.txt
java -cp $ANSERINI_JAR trec_eval -c -m recall.1000 rag24.raggy-dev $OUTPUT_DIR/run.msmarco-v2.1.doc-segmented.rag24.raggy-dev.txt

And these are the complete set of expected scores:

# doc condition
recip_rank            	all	0.1654
recip_rank            	all	0.1732

map                   	all	0.2281
recip_rank            	all	0.8466
ndcg_cut_10           	all	0.5183
recall_100            	all	0.3502
recall_1000           	all	0.6915

map                   	all	0.0841
recip_rank            	all	0.6623
ndcg_cut_10           	all	0.2991
recall_100            	all	0.1866
recall_1000           	all	0.4254

map                   	all	0.1089
recip_rank            	all	0.5783
ndcg_cut_10           	all	0.2914
recall_100            	all	0.2604
recall_1000           	all	0.5383

map                   	all	0.1251
recip_rank            	all	0.7060
ndcg_cut_10           	all	0.3631
recall_100            	all	0.2433
recall_1000           	all	0.5317

# doc-segmented condition
recip_rank            	all	0.1973
recip_rank            	all	0.2000

map                   	all	0.2609
recip_rank            	all	0.9026
ndcg_cut_10           	all	0.5778
recall_100            	all	0.3811
recall_1000           	all	0.7115

map                   	all	0.1079
recip_rank            	all	0.7213
ndcg_cut_10           	all	0.3576
recall_100            	all	0.2330
recall_1000           	all	0.4790

map                   	all	0.1391
recip_rank            	all	0.6519
ndcg_cut_10           	all	0.3356
recall_100            	all	0.3049
recall_1000           	all	0.5852

map                   	all	0.1561
recip_rank            	all	0.7465
ndcg_cut_10           	all	0.4227
recall_100            	all	0.2807
recall_1000           	all	0.5745

MS MARCO V1 Passage

❗ Beware, the (automatically downloaded) indexes for running these experiments take up 200 GB in total.

Currently, Anserini provides support for the following models:

  • BM25
  • SPLADE++ EnsembleDistil: cached queries and ONNX query encoding
  • cosDPR-distil: cached queries and ONNX query encoding
  • bge-base-en-v1.5: cached queries and ONNX query encoding
  • cohere-embed-english-v3.0: cached queries and ONNX query encoding

The table below reports the effectiveness of the models (dev in terms of RR@10, DL19 and DL20 in terms of nDCG@10):

dev DL19 DL20
BM25 (k1=0.9, b=0.4) 0.1840 0.5058 0.4796
SPLADE++ EnsembleDistil (cached queries) 0.3830 0.7317 0.7198
SPLADE++ EnsembleDistil (ONNX) 0.3828 0.7308 0.7197
cosDPR-distil w/ HNSW fp32 (cached queries) 0.3887 0.7250 0.7025
cosDPR-distil w/ HNSW fp32 (ONNX) 0.3887 0.7250 0.7025
cosDPR-distil w/ HNSW int8 (cached queries) 0.3897 0.7240 0.7004
cosDPR-distil w/ HNSW int8 (ONNX) 0.3899 0.7247 0.6996
bge-base-en-v1.5 w/ HNSW fp32 (cached queries) 0.3574 0.7065 0.6780
bge-base-en-v1.5 w/ HNSW fp32 (ONNX) 0.3575 0.7016 0.6768
bge-base-en-v1.5 w/ HNSW int8 (cached queries) 0.3572 0.7016 0.6738
bge-base-en-v1.5 w/ HNSW int8 (ONNX) 0.3575 0.7017 0.6767
cohere-embed-english-v3.0 w/ HNSW fp32 (cached queries) 0.3647 0.6956 0.7245
cohere-embed-english-v3.0 w/ HNSW int8 (cached queries) 0.3656 0.6955 0.7262

The follow command will reproduce the above experiments:

java -cp $ANSERINI_JAR io.anserini.reproduce.RunMsMarco -collection msmarco-v1-passage
Manual runs and evaluation

The following snippet will generate the complete set of results that corresponds to the above table:

# BM25
TOPICS=(msmarco-v1-passage.dev dl19-passage dl20-passage); for t in "${TOPICS[@]}"
do
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index msmarco-v1-passage -topics ${t} -output $OUTPUT_DIR/run.msmarco-v1-passage.bm25.${t}.txt -threads 16 -bm25
done

# SPLADE++ ED
TOPICS=(msmarco-v1-passage.dev dl19-passage dl20-passage); for t in "${TOPICS[@]}"
do
    # Using cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index msmarco-v1-passage.splade-pp-ed -topics ${t}.splade-pp-ed -output $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.cached_q.${t}.splade-pp-ed.txt -threads 16 -impact -pretokenized
    # Using ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index msmarco-v1-passage.splade-pp-ed -topics ${t} -encoder SpladePlusPlusEnsembleDistil -output $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.onnx.${t}.txt -threads 16 -impact -pretokenized
done

# cosDPR-distil
TOPICS=(msmarco-v1-passage.dev dl19-passage dl20-passage); for t in "${TOPICS[@]}"
do
    # Using HNSW (fp32) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cosdpr-distil.hnsw -topics ${t}.cosdpr-distil -output $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.cached_q.${t}.cosdpr-distil.txt -threads 16 -efSearch 1000
    # Using HNSW (fp32) index with ONNX encoding
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cosdpr-distil.hnsw -topics ${t} -encoder CosDprDistil -output $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.onnx.${t}.txt -threads 16 -efSearch 1000
    # Using HNSW (int8) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cosdpr-distil.hnsw-int8 -topics ${t}.cosdpr-distil -output $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.cached_q.${t}.cosdpr-distil.txt -threads 16 -efSearch 1000
    # Using HNSW (int8) index with ONNX encoding
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cosdpr-distil.hnsw-int8 -topics ${t} -encoder CosDprDistil -output $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.onnx.${t}.txt -threads 16 -efSearch 1000
done

# bge-base-en-v1.5
TOPICS=(msmarco-v1-passage.dev dl19-passage dl20-passage); for t in "${TOPICS[@]}"
do
    # Using HNSW (fp32) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.hnsw -topics ${t}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.cached_q.${t}.bge-base-en-v1.5.txt -threads 16 -efSearch 1000
    # Using HNSW (fp32) index with ONNX encoding
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.hnsw -topics ${t} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.onnx.${t}.txt -threads 16 -efSearch 1000
    # Using HNSW (int8) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8 -topics ${t}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.cached_q.${t}.bge-base-en-v1.5.txt -threads 16 -efSearch 1000
    # Using HNSW (int8) index with ONNX encoding
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8 -topics ${t} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.onnx.${t}.txt -threads 16 -efSearch 1000
done

# cohere-embed-english-v3.0
TOPICS=(msmarco-v1-passage.dev dl19-passage dl20-passage); for t in "${TOPICS[@]}"
do
    # Using HNSW (fp32) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cohere-embed-english-v3.0.hnsw -topics ${t}.cohere-embed-english-v3.0 -output $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw.cached_q.${t}.cohere-embed-english-v3.0.txt -threads 16 -efSearch 1000
    # Using HNSW (int8) index with cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cohere-embed-english-v3.0.hnsw-int8 -topics ${t}.cohere-embed-english-v3.0 -output $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw-int8.cached_q.${t}.cohere-embed-english-v3.0.txt -threads 16 -efSearch 1000
done

And here's the snippet of code to perform the evaluation (which will yield the scores above):

java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bm25.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.bm25.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.bm25.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.cached_q.msmarco-v1-passage.dev.splade-pp-ed.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.cached_q.dl19-passage.splade-pp-ed.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.cached_q.dl20-passage.splade-pp-ed.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.onnx.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.splade-pp-ed.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.cached_q.msmarco-v1-passage.dev.cosdpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.cached_q.dl19-passage.cosdpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.cached_q.dl20-passage.cosdpr-distil.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.onnx.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.cached_q.msmarco-v1-passage.dev.cosdpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.cached_q.dl19-passage.cosdpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.cached_q.dl20-passage.cosdpr-distil.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.onnx.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cosdpr-distil.hnsw-int8.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.cached_q.msmarco-v1-passage.dev.bge-base-en-v1.5.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.cached_q.dl19-passage.bge-base-en-v1.5.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.cached_q.dl20-passage.bge-base-en-v1.5.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.onnx.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.cached_q.msmarco-v1-passage.dev.bge-base-en-v1.5.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.cached_q.dl19-passage.bge-base-en-v1.5.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.cached_q.dl20-passage.bge-base-en-v1.5.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.onnx.msmarco-v1-passage.dev.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.hnsw-int8.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw.cached_q.msmarco-v1-passage.dev.cohere-embed-english-v3.0.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw.cached_q.dl19-passage.cohere-embed-english-v3.0.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw.cached_q.dl20-passage.cohere-embed-english-v3.0.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-v1-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw-int8.cached_q.msmarco-v1-passage.dev.cohere-embed-english-v3.0.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw-int8.cached_q.dl19-passage.cohere-embed-english-v3.0.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.hnsw-int8.cached_q.dl20-passage.cohere-embed-english-v3.0.txt

BEIR

❗ Beware, the (automatically downloaded) indexes for running these experiments take up 374 GB in total.

Currently, Anserini provides support for the following models:

  • Flat = BM25, "flat" bag-of-words baseline
  • MF = BM25, "multifield" bag-of-words baseline
  • S = SPLADE++ EnsembleDistil:
    • cached queries (Sp)
    • ONNX query encoding (So)
  • Bf = bge-base-en-v1.5 (flat)
    • cached queries (Bfc)
    • ONNX query encoding (Bfo)
  • Bh = bge-base-en-v1.5 (HNSW)
    • cached queries (Bhc)
    • ONNX query encoding (Bhc)

The table below reports the effectiveness of the models (nDCG@10):

Corpus Flat MF Sp So Bfc Bfo Bhc Bho
trec-covid 0.5947 0.6559 0.7274 0.7270 0.7814 0.7815 0.7834 0.7835
bioasq 0.5225 0.4646 0.4980 0.4980 0.4149 0.4148 0.4042 0.4042
nfcorpus 0.3218 0.3254 0.3470 0.3473 0.3735 0.3735 0.3735 0.3735
nq 0.3055 0.3285 0.5378 0.5372 0.5413 0.5415 0.5413 0.5415
hotpotqa 0.6330 0.6027 0.6868 0.6868 0.7259 0.7259 0.7242 0.7241
fiqa 0.2361 0.2361 0.3475 0.3473 0.4065 0.4065 0.4065 0.4065
signal1m 0.3304 0.3304 0.3008 0.3006 0.2886 0.2886 0.2869 0.2869
trec-news 0.3952 0.3977 0.4152 0.4169 0.4425 0.4424 0.4411 0.4410
robust04 0.4070 0.4070 0.4679 0.4651 0.4465 0.4435 0.4467 0.4437
arguana 0.3970 0.4142 0.5203 0.5218 0.6361 0.6228 0.6361 0.6228
webis-touche2020 0.4422 0.3673 0.2468 0.2464 0.2570 0.2571 0.2570 0.2571
cqadupstack-android 0.3801 0.3709 0.3904 0.3898 0.5075 0.5076 0.5075 0.5076
cqadupstack-english 0.3453 0.3321 0.4079 0.4078 0.4857 0.4857 0.4855 0.4855
cqadupstack-gaming 0.4822 0.4418 0.4957 0.4959 0.5965 0.5967 0.5965 0.5967
cqadupstack-gis 0.2901 0.2904 0.3150 0.3148 0.4127 0.4131 0.4129 0.4133
cqadupstack-mathematica 0.2015 0.2046 0.2377 0.2379 0.3163 0.3163 0.3163 0.3163
cqadupstack-physics 0.3214 0.3248 0.3599 0.3597 0.4722 0.4724 0.4722 0.4724
cqadupstack-programmers 0.2802 0.2963 0.3401 0.3399 0.4242 0.4238 0.4242 0.4238
cqadupstack-stats 0.2711 0.2790 0.2990 0.2980 0.3732 0.3728 0.3732 0.3728
cqadupstack-tex 0.2244 0.2086 0.2530 0.2529 0.3115 0.3115 0.3115 0.3115
cqadupstack-unix 0.2749 0.2788 0.3167 0.3170 0.4219 0.4220 0.4219 0.4220
cqadupstack-webmasters 0.3059 0.3008 0.3167 0.3166 0.4065 0.4072 0.4065 0.4072
cqadupstack-wordpress 0.2483 0.2562 0.2733 0.2718 0.3547 0.3547 0.3547 0.3547
quora 0.7886 0.7886 0.8343 0.8344 0.8890 0.8876 0.8890 0.8876
dbpedia-entity 0.3180 0.3128 0.4366 0.4374 0.4074 0.4073 0.4077 0.4076
scidocs 0.1490 0.1581 0.1591 0.1588 0.2170 0.2172 0.2170 0.2172
fever 0.6513 0.7530 0.7882 0.7879 0.8630 0.8629 0.8620 0.8620
climate-fever 0.1651 0.2129 0.2297 0.2298 0.3119 0.3117 0.3119 0.3117
scifact 0.6789 0.6647 0.7041 0.7036 0.7408 0.7408 0.7408 0.7408

The follow command will reproduce the above experiments:

java -cp $ANSERINI_JAR io.anserini.reproduce.RunBeir
Manual runs and evaluation

The following snippet will generate the complete set of results that corresponds to the above table:

CORPORA=(trec-covid bioasq nfcorpus nq hotpotqa fiqa signal1m trec-news robust04 arguana webis-touche2020 cqadupstack-android cqadupstack-english cqadupstack-gaming cqadupstack-gis cqadupstack-mathematica cqadupstack-physics cqadupstack-programmers cqadupstack-stats cqadupstack-tex cqadupstack-unix cqadupstack-webmasters cqadupstack-wordpress quora dbpedia-entity scidocs fever climate-fever scifact); for c in "${CORPORA[@]}"
do
    # "flat" indexes
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchCollection -index beir-v1.0.0-${c}.flat -topics beir-${c} -output $OUTPUT_DIR/run.beir.flat.${c}.txt -bm25 -removeQuery
    # "multifield" indexes
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchCollection -index beir-v1.0.0-${c}.multifield -topics beir-${c} -output $OUTPUT_DIR/run.beir.multifield.${c}.txt -bm25 -removeQuery -fields contents=1.0 title=1.0
    # SPLADE++ ED, cached queries
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchCollection -index beir-v1.0.0-${c}.splade-pp-ed -topics beir-${c}.splade-pp-ed -output $OUTPUT_DIR/run.beir.splade-pp-ed.cached_q.${c}.txt -impact -pretokenized -removeQuery
    # SPLADE++ ED, ONNX
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchCollection -index beir-v1.0.0-${c}.splade-pp-ed -topics beir-${c} -encoder SpladePlusPlusEnsembleDistil -output $OUTPUT_DIR/run.beir.splade-pp-ed.onnx.${c}.txt -impact -pretokenized -removeQuery
    # BGE-base-en-v1.5, flat dense vector index, cached queries
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchFlatDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5.flat -topics beir-${c}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.flat.cached_q.${c}.txt -threads 16 -removeQuery
    # BGE-base-en-v1.5, flat dense vector index, ONNX
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchFlatDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5.flat -topics beir-${c} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.flat.onnx.${c}.txt -threads 16 -removeQuery
    # BGE-base-en-v1.5, HNSW index, cached queries
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchHnswDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5.hnsw -topics beir-${c}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.hnsw.cached_q.${c}.txt -threads 16 -efSearch 1000 -removeQuery
    # BGE-base-en-v1.5, HNSW index, ONNX
    java -cp $ANSERINI_JAR --add-modules jdk.incubator.vector io.anserini.search.SearchHnswDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5.hnsw -topics beir-${c} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.hnsw.onnx.${c}.txt -threads 16 -efSearch 1000 -removeQuery
done

Note that --add-modules jdk.incubator.vector enables OpenJDK's Panama Vector API, which accelerates vector search. However, this is not a score-preserving optimization. Similarity scores are slightly different in some cases, which leads to slightly different nDCG@10 scores for some BEIR collection.

And here's the snippet of code to perform the evaluation (which will yield the scores above):

CORPORA=(trec-covid bioasq nfcorpus nq hotpotqa fiqa signal1m trec-news robust04 arguana webis-touche2020 cqadupstack-android cqadupstack-english cqadupstack-gaming cqadupstack-gis cqadupstack-mathematica cqadupstack-physics cqadupstack-programmers cqadupstack-stats cqadupstack-tex cqadupstack-unix cqadupstack-webmasters cqadupstack-wordpress quora dbpedia-entity scidocs fever climate-fever scifact); for c in "${CORPORA[@]}"
do
    echo $c
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.flat.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.multifield.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.splade-pp-ed.cached_q.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.splade-pp-ed.onnx.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.bge-base-en-v1.5.flat.cached_q.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.bge-base-en-v1.5.flat.onnx.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.bge-base-en-v1.5.hnsw.cached_q.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.bge-base-en-v1.5.hnsw.onnx.${c}.txt
done