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End of preview. Expand
in Data Studio
Bloomberg Financial News Embeddings for Vector Database Benchmarking
Dataset Description
This dataset contains pre-computed embeddings of Bloomberg financial news articles, designed for evaluating vector database performance. The embeddings are generated using Google's EmbeddingGemma-300M model.
Purpose
Benchmark dataset for evaluating vector database performance on financial news domain, specifically designed for use with VectorDBBench.
Dataset Summary
- Total Training Samples: 368,816
- Test Queries: 1,000
- Ground Truth: Top-1000 nearest neighbors per query
- Embedding Dimension: 768
- Embedding Model: google/embeddinggemma-300m
- Source Data: danidanou/Bloomberg_Financial_News
Dataset Structure
Data Splits
| Split | Samples | Description |
|---|---|---|
train |
368,816 | Training embeddings (80% random sample from source) |
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
id(int64): Unique identifier for each articleemb(List[float64]): 768-dimensional L2-normalized embedding vector
neigbors.parquet
id(int64): Query identifier (matches test)neighbors_id(List[int64]): List of 1000 nearest neighbor IDs from train set
Dataset Creation
Source Data
The dataset is derived from approximately 447K Bloomberg financial news articles:
- Train: 80% random sample (368,816 articles)
- Test: 1,000 articles randomly sampled from remaining 20% (non-overlapping with train)
Preprocessing
- Text Preparation: Concatenated Headline + Article for each news item
- Chunking: For texts exceeding 2048 tokens:
- Split into chunks with ~100 token overlap
- Embedded each chunk separately
- Averaged chunk embeddings for final representation
- Normalization: All embeddings are L2-normalized
Embedding Generation
- Model: google/embeddinggemma-300m
- Dimension: 768
- Max Token Length: 2048
- Normalization: L2-normalized
Ground Truth Generation
Ground truth nearest neighbors were computed using:
- Method: Flat search (brute-force)
- Metric: Cosine similarity
- K: Top-1000 neighbors per query
Usage
Loading the Dataset
from datasets import load_dataset
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']
# Load ground truth
neigbors = pd.read_parquet(
"hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors.parquet"
)
Evaluation Example
import numpy as np
from datasets import load_dataset
import pandas as pd
# Load data
dataset = load_dataset("redcourage/Bloomberg-Financial-News-embedding-gemma-300m")
train_data = dataset['train']
test_data = dataset['test']
neigbors = pd.read_parquet(
"hf://datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m/neigbors.parquet"
)
# Convert to numpy arrays
train_embeddings = np.array(train_data['emb'])
test_embeddings = np.array(test_data['emb'])
# Example: Compute recall@10
def compute_recall_at_k(retrieved_ids, neigbors_ids, k=10):
"""
Compute Recall@K
Args:
retrieved_ids: List of retrieved neighbor IDs
neigbors_ids: List of ground truth neighbor IDs
k: Number of top results to consider
"""
retrieved_k = set(retrieved_ids[:k])
neigbors_k = set(neigbors_ids[:k])
if len(neigbors_k) == 0:
return 0.0
return len(retrieved_k & neigbors_k) / len(neigbors_k)
# Use with your vector database
# ... insert your vector DB search code here ...
Use Cases
- Vector database performance benchmarking on financial domain
- Approximate nearest neighbor (ANN) algorithm evaluation
- Retrieval system testing for financial news
Limitations
- Domain-Specific: Optimized for financial news; may not generalize to other domains
- Language: English only
- Temporal Coverage: Limited to articles available in the source dataset (2006-2021)
- Chunking Strategy: Long documents are averaged, which may lose fine-grained information
- Ground Truth: Based on cosine similarity with embeddings, not human relevance judgments
- Financial Bias: May reflect biases present in Bloomberg's reporting and article selection
License
Apache 2.0
Citation
If you use this dataset, please cite:
@dataset{bloomberg_embeddings_gemma,
author = {redcourage},
title = {Bloomberg Financial News Embeddings for Vector Database Benchmarking},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/redcourage/Bloomberg-Financial-News-embedding-gemma-300m}
}
Source Dataset Citation
@dataset{bloomberg_financial_news,
author = {danidanou},
title = {Bloomberg Financial News},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/danidanou/Bloomberg_Financial_News}
}
Embedding Model Citation
@misc{embeddinggemma,
title={Embedding Gemma},
author={Google},
year={2024},
url={https://huggingface.co/google/embeddinggemma-300m}
}
Acknowledgments
- Original dataset: danidanou/Bloomberg_Financial_News
- Embedding model: google/embeddinggemma-300m
- Benchmark framework: VectorDBBench
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