SQuAD 1.1 Stanford Question Answering Dataset
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
- cc-by-sa-4.0
- Source / creator
- huggingface: rajpurkar/squad
- Collection method
- auto_imported_huggingface_federated
- Coverage start
- Not documented
- Coverage end
- Not documented
- 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 |
| title | 0 / 10 | string | 0 / 10 |
| context | 0 / 10 | string | 0 / 10 |
| question | 0 / 10 | string | 0 / 10 |
| answers | 0 / 10 | object | 0 / 10 |
About this data
Crowdsourced question-answer pairs on Wikipedia passages. Canonical extractive reading comprehension benchmark for evaluating retrieval-augmented generation and fine-tuning natural language understanding models.
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 23152e83-2af2-4c0a-9f85-e53f5f9a83ae --output dataset.bin
Full supplier documentation
Schema
| Name | Type | Description |
|---|---|---|
| id | VARCHAR | Unique alphanumeric identifier for the question-answer pair. |
| title | VARCHAR | Wikipedia article title from which the passage was sourced. |
| context | VARCHAR | Full text passage from Wikipedia containing the answer. |
| question | VARCHAR | Natural language question crowdsourced by annotators. |
| answers | STRUCT("text" VARCHAR[], answer_start INTEGER[]) | Array of acceptable answer strings and their character offsets within the context. |
Sample Data
Preview a sample of the data before downloading.
Public sample only. Sign in to retrieve the full dataset, including free datasets.
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: "SQuAD 1.1 Stanford Question An" })
// Found: 23152e83-2af2-4c0a-9f85-e53f5f9a83ae
get_download_url({ dataset_id: "23152e83-2af2-4c0a-9f85-e53f5f9a83ae" }) // free — sign in with MCP OAuth first# Free dataset — sign in or use your account API key: curl https://api.databazaar.io/datasets/23152e83-2af2-4c0a-9f85-e53f5f9a83ae/download-url -H "Authorization: Bearer $DATABAZAAR_API_KEY"