---
license: cc-by-sa-4.0
task_categories:
- question-answering
- text-retrieval
language:
- en
tags:
- squad
- squad-v2
- reading-comprehension
- lance
- sentence-transformers
pretty_name: squad-v2-lance
size_categories:
- 100K<n<1M
---
# SQuAD v2 (Lance Format)

Lance-formatted version of [SQuAD v2](https://huggingface.co/datasets/rajpurkar/squad_v2) — Stanford Question Answering Dataset, version 2 — with **MiniLM sentence embeddings** stored inline alongside the questions, contexts, and answers.

## Why this version?

- **One self-contained Lance dataset** with 130k+ Wikipedia-grounded questions and reference answers.
- **Pre-computed text embeddings** (`sentence-transformers/all-MiniLM-L6-v2`, 384-dim, L2-normalized) on the question column with an `IVF_PQ` index — instant semantic question retrieval.
- **Full-text inverted indices** on both `question` and `context` for keyword search.
- **BITMAP** on `is_impossible` for fast filtering between answerable and unanswerable questions.

## Splits

| Split | Rows |
|-------|------|
| `train.lance`      | 130,319 |
| `validation.lance` | 11,873 |

## Schema

| Column | Type | Notes |
|---|---|---|
| `id` | `string` | SQuAD question id |
| `title` | `string` | Wikipedia article title |
| `context` | `string` | Paragraph the question was generated from |
| `question` | `string` | The question text |
| `answers` | `list<string>` | Accepted answer spans (empty for impossible questions) |
| `answer_starts` | `list<int32>` | Character offsets of each answer within `context` |
| `is_impossible` | `bool` | `true` for SQuAD 2.0 unanswerable questions |
| `question_emb` | `fixed_size_list<float32, 384>` | MiniLM embedding of `question` (cosine-normalized) |

## Pre-built indices

- `IVF_PQ` on `question_emb` — `metric=cosine`
- `INVERTED` on `question` and `context`
- `BTREE` on `id` and `title`
- `BITMAP` on `is_impossible`

## Quick start

```python
import lance

ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/validation.lance")
print(ds.count_rows(), ds.schema.names, ds.list_indices())
```

## Load with LanceDB

These tables can also be consumed by [LanceDB](https://lancedb.github.io/lancedb/), the multimodal lakehouse and embedded search library built on top of Lance, for simplified vector search and other queries.

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("validation")
print(f"LanceDB table opened with {len(tbl)} questions")
```

## Semantic question retrieval

```python
import lance
import pyarrow as pa
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q_vec = encoder.encode(["what year was the eiffel tower built?"], normalize_embeddings=True)[0]

ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/train.lance")
emb_field = ds.schema.field("question_emb")
query = pa.array([q_vec.tolist()], type=emb_field.type)

hits = ds.scanner(
    nearest={"column": "question_emb", "q": query[0], "k": 10, "nprobes": 16, "refine_factor": 30},
    columns=["id", "title", "question", "answers"],
).to_table().to_pylist()
```

### LanceDB semantic question retrieval

```python
import lancedb
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
q_vec = encoder.encode(["what year was the eiffel tower built?"], normalize_embeddings=True)[0]

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search(q_vec.tolist(), vector_column_name="question_emb")
    .metric("cosine")
    .select(["id", "title", "question", "answers"])
    .limit(10)
    .to_list()
)
```

## Full-text search on contexts

```python
ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/train.lance")
hits = ds.scanner(
    full_text_query="great pyramid of giza",
    columns=["title", "question", "context"],
    limit=5,
).to_table().to_pylist()
```

### LanceDB full-text search

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("train")

results = (
    tbl.search("great pyramid of giza")
    .select(["title", "question", "context"])
    .limit(5)
    .to_list()
)
```

## Filter answerable vs impossible questions

```python
ds = lance.dataset("hf://datasets/lance-format/squad-v2-lance/data/validation.lance")
impossible = ds.scanner(filter="is_impossible = true", columns=["question"], limit=5).to_table()
```

### Filter with LanceDB

```python
import lancedb

db = lancedb.connect("hf://datasets/lance-format/squad-v2-lance/data")
tbl = db.open_table("validation")
impossible = (
    tbl.search()
    .where("is_impossible = true")
    .select(["question"])
    .limit(5)
    .to_list()
)
```

## Why Lance?

- One dataset carries questions + contexts + answers + embeddings + indices — no sidecar files.
- On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
- Schema evolution: add columns (alternate embeddings, model predictions, task labels) without rewriting the data.

## Source & license

Converted from [`rajpurkar/squad_v2`](https://huggingface.co/datasets/rajpurkar/squad_v2). SQuAD v2 is released under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).

## Citation

```
@article{rajpurkar2018know,
  title={Know What You Don't Know: Unanswerable Questions for SQuAD},
  author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy},
  journal={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers)},
  year={2018},
}
```