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---
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license: apache-2.0
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language:
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- en
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pretty_name: HSEB MSMARCO benchmarking dataset
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dataset_info:
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features:
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- name: text
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dtype: string
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- name: id
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dtype: int64
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- name: embedding
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sequence: float32
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- name: results_10_docs
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sequence: int64
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- name: results_10_scores
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sequence: float32
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- name: results_90_docs
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sequence: int64
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- name: results_90_scores
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sequence: float32
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- name: results_100_docs
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sequence: int64
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- name: results_100_scores
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sequence: float32
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- name: tag
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sequence: int64
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configs:
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- config_name: "query-all-MiniLM-L6-v2-1K"
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data_files: "data/all-MiniLM-L6-v2/1K/queries.jsonl.gz"
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default: true
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- config_name: "corpus-all-MiniLM-L6-v2-1K"
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data_files: "data/all-MiniLM-L6-v2/1K/corpus.jsonl.gz"
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- config_name: "query-all-MiniLM-L6-v2-100K"
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data_files: "data/all-MiniLM-L6-v2/100K/queries.jsonl.gz"
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- config_name: "corpus-all-MiniLM-L6-v2-100K"
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data_files: "data/all-MiniLM-L6-v2/100K/corpus.jsonl.gz"
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- config_name: "query-all-MiniLM-L6-v2-1M"
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data_files: "data/all-MiniLM-L6-v2/1M/queries.jsonl.gz"
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- config_name: "corpus-all-MiniLM-L6-v2-1M"
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data_files: "data/all-MiniLM-L6-v2/1M/corpus.jsonl.gz"
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- config_name: "query-e5-base-v2-1K"
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data_files: "data/e5-base-v2/1K/queries.jsonl.gz"
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- config_name: "corpus-e5-base-v2-1K"
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data_files: "data/e5-base-v2/1K/corpus.jsonl.gz"
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- config_name: "query-e5-base-v2-100K"
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data_files: "data/e5-base-v2/100K/queries.jsonl.gz"
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- config_name: "corpus-e5-base-v2-100K"
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data_files: "data/e5-base-v2/100K/corpus.jsonl.gz"
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- config_name: "query-e5-base-v2-1M"
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data_files: "data/e5-base-v2/1M/queries.jsonl.gz"
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- config_name: "corpus-e5-base-v2-1M"
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data_files: "data/e5-base-v2/1M/corpus.jsonl.gz"
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- config_name: "query-Qwen3-Embedding-4B-1K"
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data_files: "data/Qwen3-Embedding-4B/1K/queries.jsonl.gz"
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- config_name: "corpus-Qwen3-Embedding-4B-1K"
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data_files: "data/Qwen3-Embedding-4B/1K/corpus.jsonl.gz"
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- config_name: "query-Qwen3-Embedding-4B-100K"
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data_files: "data/Qwen3-Embedding-4B/100K/queries.jsonl.gz"
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- config_name: "corpus-Qwen3-Embedding-4B-100K"
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data_files: "data/Qwen3-Embedding-4B/100K/corpus.jsonl.gz"
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- config_name: "query-Qwen3-Embedding-4B-1M"
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data_files: "data/Qwen3-Embedding-4B/1M/queries.jsonl.gz"
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- config_name: "corpus-Qwen3-Embedding-4B-1M"
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data_files: "data/Qwen3-Embedding-4B/1M/corpus.jsonl.gz"
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---
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# HSEB MSMARCO benchmarking dataset
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This collection is based on [MSMARCO](TODO) dataset:
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* Embedding models:
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* 384 dims: [sentence-transformers/all-MiniLM-L6-v2](TODO)
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* 768 dims: [intfloat/e5-base-v2](TODO)
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* 2560 dims: [Qwen3-Embedding-4B](TODO)
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* Splits:
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* `1K`: 1K documents, 10K queries
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* `100K`: 100K documents, 10K queries
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* `1M`: 1M documents, 10K queries
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* Filter selectivity:
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* `10%` for high selectivity, `90%` for low selectivity, `100%` for no filters at all
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* each document has a tag based on sampled selectivity, so 10% of docs have a tag `10`, and 50% of docs have tag `50`
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* Exact match results:
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* for each selectivity level for each query there are precomputed exact k-NN search results for top-100 documents.
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## Dataset structure
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The dataset can be loaded with the [Huggingface datasets](TODO) library:
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```python
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from datasets import load_dataset
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query = load_dataset("hseb-benchmark/msmarco", "query-all-MiniLM-L6-v2-1M")
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corpus = load_dataset("hseb-benchmark/msmarco", "corpus-all-MiniLM-L6-v2-1M")
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```
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where 2nd argument to `load_dataset` is a config name, consisting of the following parts:
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```
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<query|corpus>-<model>-<size>
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```
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1. `query` | `corpus` - which side of the dataset you're loading
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2. `model`: embedding model, `all-MiniLM-L6-v2` (384 dims), `e5-base-v2` (768 dims) or `Qwen3-Embedding-4B` (2560 dims)
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3. `size`: sample size, `1K`, `100K`, `1M`
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## License
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Apache 2.0
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