Wikipedia Embeddings BGE-M3 Multilingual
June 2024
Source, license and coverage
Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.
- License
- apache-2.0
- Source / creator
- huggingface: Upstash/wikipedia-2024-06-bge-m3
- Collection method
- auto_imported_huggingface_federated
- Coverage / snapshot
- June 2024
- Data last updated
- Not documented
- Update schedule
- Not documented
Sample structure score: 100 / 100
This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.
Assessed 10 sample records (JSON) on 2026-10-09. All records in the provided sample were checked.
| Check | Points | Evidence |
|---|---|---|
| Populated cells | 50 / 50 | 50 of 50 top-level cells contain a value. Null, absent and blank values count as missing; zero and false count as populated. |
| Consistent value types | 30 / 30 | 50 of 50 populated cells match their column's most common observed type. Types are inferred, not checked against real-world truth. |
| Consistent record shape | 20 / 20 | 10 of 10 records have the expected fields. CSV/TSV use the header width; JSON uses the union of observed keys. |
Field-level findings and improvements
Check missing cells and mixed types below. Document intentional missing values or mixed types in your field descriptions. Do not fill legitimate unknowns with invented values just to increase this score.
| Field | Missing cells | Most common type | Other populated types |
|---|---|---|---|
| id | 0 / 10 | string | 0 / 10 |
| url | 0 / 10 | string | 0 / 10 |
| title | 0 / 10 | string | 0 / 10 |
| text | 0 / 10 | string | 0 / 10 |
| embedding | 0 / 10 | object | 0 / 10 |
About this data
Paragraph embeddings from Wikipedia across 11 languages, generated with the multilingual BGE-M3 model. Suitable for multilingual semantic search, retrieval-augmented generation, and retrieval evaluation tasks.
Retrieve with your agent or Python
Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.
Download the Python examplepython3 retrieve-dataset.py 302b18f4-92d7-49e5-b7ea-0240cae81291 --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| id | VARCHAR | Unique identifier combining article ID and paragraph number (format: {article_id}_{paragraph_index}) |
| url | VARCHAR | Full Wikipedia URL for the source article |
| title | VARCHAR | Wikipedia article title in original language |
| text | VARCHAR | Paragraph text extracted from article body, prefixed with article title |
| embedding | FLOAT[] | 1024-dimensional dense vector from BGE-M3 model (multilingual) |
Sample Data
Preview a sample of the data before downloading.
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For AI Agents
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
"mcpServers": {
"databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
}
}
# 2. Your agent can then call:
search_datasets({ query: "Wikipedia Embeddings BGE-M3 Mu" })
// Found: 302b18f4-92d7-49e5-b7ea-0240cae81291
get_download_url({ dataset_id: "302b18f4-92d7-49e5-b7ea-0240cae81291" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/302b18f4-92d7-49e5-b7ea-0240cae81291/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"