Anserini Fatjar Regresions (v0.36.1)

Fetch the fatjar:

wget https://repo1.maven.org/maven2/io/anserini/anserini/0.36.1/anserini-0.36.1-fatjar.jar

Note that prebuilt indexes will be downloaded to ~/.cache/pyserini/indexes/. Currently, this path is hard-coded (see Anserini #2322). If you want to change the download location, the current workaround is to use symlinks, i.e., symlink ~/.cache/pyserini/indexes/ to the actual path you desire.

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

export ANSERINI_JAR="anserini-0.36.1-fatjar.jar"
export OUTPUT_DIR="."

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:

curl -X GET "http://localhost:8081/api/collection/msmarco-v2.1-doc/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 TREC 2024 RAG. Use the hits parameter to specify the number of hits to return, e.g., hits=1000 to return the top 1000 hits.

TREC 2024 RAG

❗ 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.

For the TREC 2024 RAG track, we have thus far only implemented BM25 baselines on the MS MARCO V2.1 document corpus (both the doc and doc segmented variants). Current results are based existing qrels that have been "projected" over from MS MARCO V2.0 passage judgments. The table below reports effectiveness (dev in terms of RR@10, 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

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 \
  -topics dl23-doc \
  -output $OUTPUT_DIR/run.msmarco-v2.1-doc.bm25.dl23-doc.txt \
  -bm25 -hits 20 \
  -outputRerankerRequests $OUTPUT_DIR/results.msmarco-v2.1-doc.bm25.dl23-doc.jsonl

And the output looks something like:

$ head -n 1 $OUTPUT_DIR/results.msmarco-v2.1-doc.bm25.dl23-doc.jsonl | jq
{
  "query": {
    "text": "How does the process of digestion and metabolism of carbohydrates start",
    "qid": 2000138
  },
  "candidates": [
    {
      "docid": "msmarco_v2.1_doc_15_390497775",
      "score": 14.3364,
      "doc": {
        "url": "https://diabetestalk.net/blood-sugar/conversion-of-carbohydrates-to-glucose",
        "title": "Conversion Of Carbohydrates To Glucose | DiabetesTalk.Net",
        "headings": "...",
        "body": "..."
      }
    },
    {
      "docid": "msmarco_v2.1_doc_15_416962410",
      "score": 14.2271,
      "doc": {
        "url": "https://diabetestalk.net/insulin/how-is-starch-converted-to-glucose-in-the-body",
        "title": "How Is Starch Converted To Glucose In The Body? | DiabetesTalk.Net",
        "headings": "...",
        "body": "..."
      }
    },
    ...
  ]
}

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 fp32 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cos-dpr-distil -topics ${t}.cos-dpr-distil -output $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.cached_q.${t}.cos-dpr-distil.txt -threads 16 -efSearch 1000
    # Using fp32 index, ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cos-dpr-distil -topics ${t} -encoder CosDprDistil -output $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.onnx.${t}.txt -threads 16 -efSearch 1000
    # Using int8 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cos-dpr-distil.quantized -topics ${t}.cos-dpr-distil -output $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.cached_q.${t}.cos-dpr-distil.txt -threads 16 -efSearch 1000
    # Using int8 index, ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cos-dpr-distil.quantized -topics ${t} -encoder CosDprDistil -output $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.onnx.${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 fp32 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5 -topics ${t}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.fp32.cached_q.${t}.bge-base-en-v1.5.txt -threads 16 -efSearch 1000
    # Using fp32 index, ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5 -topics ${t} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.fp32.onnx.${t}.txt -threads 16 -efSearch 1000
    # Using int8 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.quantized -topics ${t}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.int8.cached_q.${t}.bge-base-en-v1.5.txt -threads 16 -efSearch 1000
    # Using int8 index, ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.bge-base-en-v1.5.quantized -topics ${t} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.int8.onnx.${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 fp32 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cohere-embed-english-v3.0 -topics ${t}.cohere-embed-english-v3.0 -output $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.fp32.cached_q.${t}.cohere-embed-english-v3.0.txt -threads 16 -efSearch 1000
    # Using int8 index, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index msmarco-v1-passage.cohere-embed-english-v3.0.quantized -topics ${t}.cohere-embed-english-v3.0 -output $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.int8.cached_q.${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-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-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-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-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.cached_q.msmarco-v1-passage.dev.cos-dpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.cached_q.dl19-passage.cos-dpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.cached_q.dl20-passage.cos-dpr-distil.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.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.cos-dpr-distil.fp32.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.fp32.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.cached_q.msmarco-v1-passage.dev.cos-dpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl19-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.cached_q.dl19-passage.cos-dpr-distil.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.cached_q.dl20-passage.cos-dpr-distil.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.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.cos-dpr-distil.int8.onnx.dl19-passage.txt
java -cp $ANSERINI_JAR trec_eval -m ndcg_cut.10 -c dl20-passage $OUTPUT_DIR/run.msmarco-v1-passage.cos-dpr-distil.int8.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.fp32.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.fp32.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.fp32.cached_q.dl20-passage.bge-base-en-v1.5.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.fp32.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.fp32.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.fp32.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.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.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.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-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.bge-base-en-v1.5.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.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.int8.onnx.dl20-passage.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.fp32.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.fp32.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.fp32.cached_q.dl20-passage.cohere-embed-english-v3.0.txt
echo ''
java -cp $ANSERINI_JAR trec_eval -c -M 10 -m recip_rank msmarco-passage.dev $OUTPUT_DIR/run.msmarco-v1-passage.cohere-embed-english-v3.0.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.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.int8.cached_q.dl20-passage.cohere-embed-english-v3.0.txt

BEIR

❗ Beware, the (automatically downloaded) indexes for running these experiments take up 246 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)
D = bge-base-en-v1.5
- cached queries (Dp)
- ONNX query encoding (Do)

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

Corpus	Flat	MF	Sp	So	Dp	Do
`trec-covid`	0.5947	0.6559	0.7274	0.7270	0.7834	0.7835
`bioasq`	0.5225	0.4646	0.4980	0.4980	0.4042	0.4042
`nfcorpus`	0.3218	0.3254	0.3470	0.3473	0.3735	0.3738
`nq`	0.3055	0.3285	0.5378	0.5372	0.5413	0.5415
`hotpotqa`	0.6330	0.6027	0.6868	0.6868	0.7242	0.7241
`fiqa`	0.2361	0.2361	0.3475	0.3473	0.4065	0.4065
`signal1m`	0.3304	0.3304	0.3008	0.3006	0.2869	0.2869
`trec-news`	0.3952	0.3977	0.4152	0.4169	0.4411	0.4410
`robust04`	0.4070	0.4070	0.4679	0.4651	0.4467	0.4437
`arguana`	0.3970	0.4142	0.5203	0.5218	0.6361	0.6228
`webis-touche2020`	0.4422	0.3673	0.2468	0.2464	0.2570	0.2571
`cqadupstack-android`	0.3801	0.3709	0.3904	0.3898	0.5075	0.5076
`cqadupstack-english`	0.3453	0.3321	0.4079	0.4078	0.4855	0.4855
`cqadupstack-gaming`	0.4822	0.4418	0.4957	0.4959	0.5965	0.5967
`cqadupstack-gis`	0.2901	0.2904	0.3150	0.3148	0.4129	0.4133
`cqadupstack-mathematica`	0.2015	0.2046	0.2377	0.2379	0.3163	0.3163
`cqadupstack-physics`	0.3214	0.3248	0.3599	0.3597	0.4722	0.4724
`cqadupstack-programmers`	0.2802	0.2963	0.3401	0.3399	0.4242	0.4238
`cqadupstack-stats`	0.2711	0.2790	0.2990	0.2980	0.3731	0.3728
`cqadupstack-tex`	0.2244	0.2086	0.2530	0.2529	0.3115	0.3115
`cqadupstack-unix`	0.2749	0.2788	0.3167	0.3170	0.4219	0.4220
`cqadupstack-webmasters`	0.3059	0.3008	0.3167	0.3166	0.4065	0.4072
`cqadupstack-wordpress`	0.2483	0.2562	0.2733	0.2718	0.3547	0.3547
`quora`	0.7886	0.7886	0.8343	0.8344	0.8890	0.8876
`dbpedia-entity`	0.3180	0.3128	0.4366	0.4374	0.4077	0.4076
`scidocs`	0.1490	0.1581	0.1591	0.1588	0.2170	0.2172
`fever`	0.6513	0.7530	0.7882	0.7879	0.8620	0.8620
`climate-fever`	0.1651	0.2129	0.2297	0.2298	0.3119	0.3117
`scifact`	0.6789	0.6647	0.7041	0.7036	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 $ANSERINI_JAR io.anserini.search.SearchCollection -index beir-v1.0.0-${c}.flat -topics beir-${c} -output $OUTPUT_DIR/run.beir.flat.${c}.txt -bm25 -removeQuery
    # "multifield" indexes
    java -cp $ANSERINI_JAR io.anserini.search.SearchCollection -index beir-v1.0.0-${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 $ANSERINI_JAR io.anserini.search.SearchCollection -index beir-v1.0.0-${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 $ANSERINI_JAR io.anserini.search.SearchCollection -index beir-v1.0.0-${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, cached queries
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5 -topics beir-${c}.bge-base-en-v1.5 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.cached_q.${c}.txt -threads 16 -efSearch 1000 -removeQuery
    # BGE-base-en-v1.5, ONNX
    java -cp $ANSERINI_JAR io.anserini.search.SearchHnswDenseVectors -index beir-v1.0.0-${c}.bge-base-en-v1.5 -topics beir-${c} -encoder BgeBaseEn15 -output $OUTPUT_DIR/run.beir.bge-base-en-v1.5.onnx.${c}.txt -threads 16 -efSearch 1000 -removeQuery
done

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 $OUTPUT_DIR/run.beir.bge-base-en-v1.5.cached_q.${c}.txt
    java -cp $ANSERINI_JAR trec_eval -c -m ndcg_cut.10 qrels.beir-v1.0.0-${c}.test.txt $OUTPUT_DIR/run.beir.bge-base-en-v1.5.onnx.${c}.txt
done

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

fatjar-regressions-v0.36.1.md

fatjar-regressions-v0.36.1.md

Anserini Fatjar Regresions (v0.36.1)

Webapp and REST API

TREC 2024 RAG

MS MARCO V1 Passage

BEIR

Files

fatjar-regressions-v0.36.1.md

Latest commit

History

fatjar-regressions-v0.36.1.md

File metadata and controls

Anserini Fatjar Regresions (v0.36.1)

Webapp and REST API

TREC 2024 RAG

MS MARCO V1 Passage

BEIR