Run detail

b5323edbfe15c86b

Dataset

beir-v1.0.0-fiqa

Method

Q2D (FS)

Model

gpt-4.1

Retriever

SPLADE++ (learned_sparse)

params_hash

ce9f77c4

Queries

648

Metrics

ndcg_cut_10	0.3446
recall_100	0.6890

Reproduce this run

Three steps: (1) reformulate the queries with QueryGym's example pipeline, (2) run retrieval with Pyserini, (3) evaluate with trec_eval.

1. reformulate

python examples/querygym_pyserini/pipeline.py \
    --dataset beir-v1.0.0-fiqa \
    --method query2doc \
    --model openai/gpt-4.1 \
    --steps reformulate \
    --temperature 1 \
    --max-tokens 128 \
    --method-params '{"mode":"fs","num_examples":4,"train_split":"train"}' \
    --output-dir outputs/reproduce

2. retrieve (SPLADE++)

python -m pyserini.search.lucene \
  --threads 16 --batch-size 128 \
  --index beir-v1.0.0-fiqa.splade-pp-ed \
  --topics outputs/reproduce/queries/reformulated_queries.tsv \
  --encoder naver/splade-cocondenser-ensembledistil \
  --output run.txt \
  --hits 1000 --impact

3. evaluate

python -m pyserini.eval.trec_eval -c -m ndcg.cut.10 -m recall.100 \
  beir-v1.0.0-fiqa-test run.txt

Artifacts

Config

config.json

{
  "method_params": {
    "mode": "fs",
    "num_examples": 4,
    "dataset_type": "msmarco",
    "collection_path": "/mnt/data/son/data/msmarco/collection.tsv",
    "train_queries_path": "/mnt/data/son/data/msmarco/queries.train.tsv",
    "train_qrels_path": "/mnt/data/son/data/msmarco/qrels.train.tsv",
    "train_split": "train"
  },
  "llm_config": {
    "temperature": 1,
    "max_tokens": 128
  },
  "dataset_config": {
    "topics": "beir-v1.0.0-fiqa-test",
    "index": "beir-v1.0.0-fiqa.flat",
    "num_queries": 648
  },
  "retrieval": {
    "retriever_id": "splade-pp",
    "paradigm": "learned_sparse",
    "params": {
      "model": "naver/splade-cocondenser-ensembledistil"
    }
  }
}