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README.md

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+# Dataset for HybRank
+
+You can download preprocessed data from [HuggingFace Repo](https://huggingface.co/datasets/ustc-zhangzm/HybRank)
+
+Note that `train_scores.hdf5` of `MS MARCO` dataset files are split via
+```bash
+split -d -b 3G train_scores.hdf5 train_scores.hdf5.
+```
+
+Run following command to concatenate these files after all shards have been downloaded
+```bash
+cat train_scores.hdf5.* > train_scores.hdf5
+```
+
+Or you can generate data by yourself via the following steps:
+
+## Dependencies
+```
+java 11.0.16
+maven 3.8.6
+anserini 0.14.3
+faiss-cpu 1.7.2
+pyserini 0.17.1
+```
+
+
+## Natural Questions
+
+### 1. Download raw data (Refer to [DPR](https://github.com/facebookresearch/DPR) for more details of the dataset)
+```shell
+python download_DPR_data.py --resource data.wikipedia_split.psgs_w100
+python download_DPR_data.py --resource data.retriever.nq
+python download_DPR_data.py --resource data.retriever.qas.nq
+mkdir -p raw && mv downloads raw/NQ
+```
+
+### 2. Convert collections to jsonl format for Pyserini
+```shell
+python convert_NQ_collection_to_jsonl.py --collection-path raw/NQ/data/wikipedia_split/psgs_w100.tsv --output-folder pyserini/collections/NQ
+```
+
+### 3. Build Lucene indexes via Pyserini
+```shell
+python -m pyserini.index.lucene \
+--collection JsonCollection \
+--input pyserini/collections/NQ \
+--index pyserini/indexes/NQ \
+--generator DefaultLuceneDocumentGenerator \
+--threads 1 \
+--storePositions --storeDocvectors --storeRaw
+```
+
+### 4. Generate data
+```shell
+RETRIEVERS=("DPR-Multi" "DPR-Single" "ANCE" "FiD-KD" "RocketQA-retriever" "RocketQAv2-retriever" "RocketQA-reranker" "RocketQAv2-reranker")
+
+for RETRIEVER in ${RETRIEVERS[@]}; do
+  python generate_NQ_data.py --retriever $RETRIEVER
+done
+```
+Note that before generate data for retriever `RocketQA*`, please generate the retrieval results following the instructions in `data/RocketQA_baselines/README.md`. Data for other retrievers can be generated directly.
+
+
+## MS MARCO & TREC 2019/2020
+
+### 1. Download raw data (Refer to [MS MARCO](https://microsoft.github.io/msmarco/) for more details of the dataset)
+  * Download and uncompress MSMARCO Passage Ranking Collections and Queries [collectionandqueries.tar.gz](https://msmarco.blob.core.windows.net/msmarcoranking/collectionandqueries.tar.gz) to `data/raw/MSMARCO/`
+  * TREC DL Test Queries and Qrels
+    * [TREC DL-2019](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html)
+      * Download and uncompress [msmarco-test2019-queries.tsv](https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-test2019-queries.tsv.gz)
+      * Download [2019qrels-pass.txt](https://trec.nist.gov/data/deep/2019qrels-pass.txt)
+    * [TREC DL-2020](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2020)
+      * Download and uncompress [msmarco-test2019-queries.tsv](https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-test2020-queries.tsv.gz)
+      * Download [2019qrels-pass.txt](https://trec.nist.gov/data/deep/2020qrels-pass.txt)
+    * Put them into `data/raw/TRECDL/`
+
+### 2. Convert collections to jsonl format for Pyserini
+
+```shell
+python convert_MSMARCO_collection_to_jsonl.py --collection-path raw/MSMARCO/collection.tsv --output-folder pyserini/collections/MSMARCO
+```
+
+### 3. Build Lucene indexes via Pyserini
+
+```shell
+python -m pyserini.index.lucene \
+  --collection JsonCollection \
+  --input pyserini/collections/MSMARCO \
+  --index pyserini/indexes/MSMARCO \
+  --generator DefaultLuceneDocumentGenerator \
+  --threads 1 \
+  --storePositions --storeDocvectors --storeRaw
+```
+
+### 4. Generate data
+```shell
+RETRIEVERS=("ANCE" "DistilBERT-KD" "TAS-B" "TCT-ColBERT-v1" "TCT-ColBERT-v2" "RocketQA-retriever" "RocketQAv2-retriever" "RocketQA-reranker" "RocketQAv2-reranker")
+
+for RETRIEVER in ${RETRIEVERS[@]}; do
+  python generate_MSMARCO_data.py --retriever $RETRIEVER
+done
+```
+
+```shell
+RETRIEVERS=("ANCE" "DistilBERT-KD" "TAS-B" "TCT-ColBERT-v1" "TCT-ColBERT-v2" "RocketQA-retriever" "RocketQAv2-retriever" "RocketQA-reranker" "RocketQAv2-reranker")
+
+SPLITS=("2019" "2020")
+
+for RETRIEVER in ${RETRIEVERS[@]}; do
+  for SPLIT in ${SPLITS[@]}; do
+    python generate_TRECDL_data.py --split $SPLIT --retriever $RETRIEVER
+  done
+done
+```