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