initial commit

README.md

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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.zst"
+    default: true
+  - config_name: "corpus-all-MiniLM-L6-v2-1K"
+    data_files: "data/all-MiniLM-L6-v2/1K/corpus.jsonl.zst"
+  - config_name: "query-all-MiniLM-L6-v2-100K"
+    data_files: "data/all-MiniLM-L6-v2/100K/queries.jsonl.zst"
+  - config_name: "corpus-all-MiniLM-L6-v2-100K"
+    data_files: "data/all-MiniLM-L6-v2/100K/corpus.jsonl.zst"
+  - config_name: "query-all-MiniLM-L6-v2-1M"
+    data_files: "data/all-MiniLM-L6-v2/1M/queries.jsonl.zst"
+  - config_name: "corpus-all-MiniLM-L6-v2-1M"
+    data_files: "data/all-MiniLM-L6-v2/1M/corpus.jsonl.zst"
+
+  - config_name: "query-e5-base-v2-1K"
+    data_files: "data/e5-base-v2/1K/queries.jsonl.zst"
+  - config_name: "corpus-e5-base-v2-1K"
+    data_files: "data/e5-base-v2/1K/corpus.jsonl.zst"
+  - config_name: "query-e5-base-v2-100K"
+    data_files: "data/e5-base-v2/100K/queries.jsonl.zst"
+  - config_name: "corpus-e5-base-v2-100K"
+    data_files: "data/e5-base-v2/100K/corpus.jsonl.zst"
+  - config_name: "query-e5-base-v2-1M"
+    data_files: "data/e5-base-v2/1M/queries.jsonl.zst"
+  - config_name: "corpus-e5-base-v2-1M"
+    data_files: "data/e5-base-v2/1M/corpus.jsonl.zst"
+
+  - config_name: "query-e5-large-v2-1K"
+    data_files: "data/e5-large-v2/1K/queries.jsonl.zst"
+  - config_name: "corpus-e5-large-v2-1K"
+    data_files: "data/e5-large-v2/1K/corpus.jsonl.zst"
+  - config_name: "query-e5-large-v2-100K"
+    data_files: "data/e5-large-v2/100K/queries.jsonl.zst"
+  - config_name: "corpus-e5-large-v2-100K"
+    data_files: "data/e5-large-v2/100K/corpus.jsonl.zst"
+  - config_name: "query-e5-large-v2-1M"
+    data_files: "data/e5-large-v2/1M/queries.jsonl.zst"
+  - config_name: "corpus-e5-large-v2-1M"
+    data_files: "data/e5-large-v2/1M/corpus.jsonl.zst"
+
 ---
+
+# 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)
+    * 1024 dims: [intfloat-e5-large-v2](TODO)
+* Splits:
+    * `1K`: 1K documents, 1K queries
+    * `100K`: 100K documents, 1K queries
+    * `1M`: 1M documents, 1K 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 `e5-large-v2` (1024 dims)
+3. `size`: sample size, `1K`, `100K`, `1M`
+
+## License
+
+Apache 2.0

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example.py

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+from datasets import load_dataset
+
+query = load_dataset("hseb-benchmark/msmarco", "query-all-MiniLM-L6-v2-1K", split="train")
+print(f"query: {query[0]}")
+
+corpus = load_dataset("hseb-benchmark/msmarco", "corpus-all-MiniLM-L6-v2-1K", split="train")
+print(f"corpus: {corpus[0]}")