Update README.md

README.md

@@ -42,17 +42,17 @@ Benchmark dataset for evaluating vector database performance on financial news d
 | Split | Samples | Description |
 |-------|---------|-------------|
 | `train` | 368,816 | Training embeddings (80% random sample from source) |
-| `test_1k` | 1,000 | Test query embeddings (from remaining 20%, non-overlapping) |
-| `neigbors_1k.parquet` | 1,000 | Top-1000 nearest neighbors for each test query |
+| `test` | 1,000 | Test query embeddings (from remaining 20%, non-overlapping) |
+| `neigbors.parquet` | 1,000 | Top-1000 nearest neighbors for each test query |
 
 ### Data Fields
 
-#### train & test_1k
+#### train & test
 - `id` (int64): Unique identifier for each article
 - `emb` (List[float64]): 768-dimensional L2-normalized embedding vector
 
-#### neigbors_1k.parquet
-- `id` (int64): Query identifier (matches test_1k)
+#### neigbors.parquet
+- `id` (int64): Query identifier (matches test)
 - `neighbors_id` (List[int64]): List of 1000 nearest neighbor IDs from train set
 
 ## Dataset Creation
@@ -96,11 +96,11 @@ import pandas as pd
 # Load train and test splits
 dataset = load_dataset("redcourage/Bloomberg-Financial-News-embedding-gemma-300m")
 train = dataset['train']
-test = dataset['test_1k']
+test = dataset['test']
 
 # Load ground truth
 neigbors = pd.read_parquet(
-    "hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors_1k.parquet"
+    "hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors.parquet"
 )
 ```
 
@@ -113,9 +113,9 @@ import pandas as pd
 # Load data
 dataset = load_dataset("redcourage/Bloomberg-Financial-News-embedding-gemma-300m")
 train_data = dataset['train']
-test_data = dataset['test_1k']
+test_data = dataset['test']
 neigbors = pd.read_parquet(
-    "hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors_1k.parquet"
+    "hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors.parquet"
 )
 
 # Convert to numpy arrays