use 10k queries per set

README.md

@@ -53,18 +53,18 @@ configs:
   - config_name: "corpus-e5-base-v2-1M"
     data_files: "data/e5-base-v2/1M/corpus.jsonl.gz"
 
-  - config_name: "query-e5-large-v2-1K"
-    data_files: "data/e5-large-v2/1K/queries.jsonl.gz"
-  - config_name: "corpus-e5-large-v2-1K"
-    data_files: "data/e5-large-v2/1K/corpus.jsonl.gz"
-  - config_name: "query-e5-large-v2-100K"
-    data_files: "data/e5-large-v2/100K/queries.jsonl.gz"
-  - config_name: "corpus-e5-large-v2-100K"
-    data_files: "data/e5-large-v2/100K/corpus.jsonl.gz"
-  - config_name: "query-e5-large-v2-1M"
-    data_files: "data/e5-large-v2/1M/queries.jsonl.gz"
-  - config_name: "corpus-e5-large-v2-1M"
-    data_files: "data/e5-large-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"
 
 ---
 
@@ -75,11 +75,11 @@ 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)
+    * 2560 dims: [Qwen3-Embedding-4B](TODO)
 * Splits:
-    * `1K`: 1K documents, 1K queries
-    * `100K`: 100K documents, 1K queries
-    * `1M`: 1M documents, 1K queries
+    * `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`
@@ -105,9 +105,9 @@ where 2nd argument to `load_dataset` is a config name, consisting of the followi
 ```
 
 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)
+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
+Apache 2.0

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