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README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
task_categories:
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| 4 |
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- question-answering
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- text-retrieval
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language:
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- en
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tags:
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- trivia-qa
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- reading-comprehension
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- lance
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- sentence-transformers
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pretty_name: trivia-qa-lance
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size_categories:
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- 100K<n<1M
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---
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| 17 |
+
# TriviaQA (Lance Format)
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| 18 |
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Lance-formatted version of [TriviaQA](https://nlp.cs.washington.edu/triviaqa/) (`rc.nocontext` config) — a question-answering dataset of trivia questions paired with answer aliases — with **MiniLM sentence embeddings** stored inline.
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## Why `rc.nocontext`?
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The full TriviaQA dataset bundles entire Wikipedia / web pages per question (`entity_pages`, `search_results`), which makes it tens of GB. The `rc.nocontext` slice keeps the question + answer + answer aliases in a compact form — ideal for closed-book QA, retrieval research, and as a search target.
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| 24 |
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## Splits
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| 26 |
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| Split | Rows |
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| 28 |
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|-------|------|
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| 29 |
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| `train.lance` | 138,384 |
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| 30 |
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| `validation.lance` | 17,944 |
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| 31 |
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## Schema
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| 33 |
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| Column | Type | Notes |
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| 35 |
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|---|---|---|
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| 36 |
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| `question_id` | `string` | TriviaQA question id (e.g. `tc_1`) |
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| 37 |
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| `question` | `string` | The trivia question |
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| 38 |
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| `question_source` | `string` | URL / source where the question came from |
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| 39 |
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| `answer_value` | `string` | Canonical answer |
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| 40 |
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| `answer_aliases` | `list<string>` | Other accepted phrasings (e.g. `["Sinclair Lewis", "Harry Sinclair Lewis"]`) |
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| 41 |
+
| `normalized_answer` | `string` | Lowercased / normalized form for exact-match scoring |
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| 42 |
+
| `answer_type` | `string` | TriviaQA entity type (e.g. `WikipediaEntity`, `FreebaseEntity`) |
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| 43 |
+
| `question_emb` | `fixed_size_list<float32, 384>` | MiniLM embedding of `question` (cosine-normalized) |
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| 44 |
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| 45 |
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## Pre-built indices
|
| 46 |
+
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| 47 |
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- `IVF_PQ` on `question_emb` — `metric=cosine`
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| 48 |
+
- `INVERTED` on `question`
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| 49 |
+
- `BTREE` on `question_id` and `answer_value`
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| 50 |
+
- `BITMAP` on `answer_type`
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| 51 |
+
|
| 52 |
+
## Quick start
|
| 53 |
+
|
| 54 |
+
```python
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| 55 |
+
import lance
|
| 56 |
+
|
| 57 |
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ds = lance.dataset("hf://datasets/lance-format/trivia-qa-lance/data/train.lance")
|
| 58 |
+
print(ds.count_rows(), ds.schema.names, ds.list_indices())
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| 59 |
+
```
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| 60 |
+
|
| 61 |
+
## Semantic search over questions
|
| 62 |
+
|
| 63 |
+
```python
|
| 64 |
+
import lance
|
| 65 |
+
import pyarrow as pa
|
| 66 |
+
from sentence_transformers import SentenceTransformer
|
| 67 |
+
|
| 68 |
+
encoder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cuda")
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| 69 |
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q = encoder.encode(["who painted the sistine chapel ceiling"], normalize_embeddings=True)[0]
|
| 70 |
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| 71 |
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ds = lance.dataset("hf://datasets/lance-format/trivia-qa-lance/data/train.lance")
|
| 72 |
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emb_field = ds.schema.field("question_emb")
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| 73 |
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hits = ds.scanner(
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| 74 |
+
nearest={"column": "question_emb", "q": pa.array([q.tolist()], type=emb_field.type)[0], "k": 5},
|
| 75 |
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columns=["question", "answer_value", "answer_aliases"],
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| 76 |
+
).to_table().to_pylist()
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| 77 |
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```
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| 78 |
+
|
| 79 |
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## Filter by answer type
|
| 80 |
+
|
| 81 |
+
```python
|
| 82 |
+
ds = lance.dataset("hf://datasets/lance-format/trivia-qa-lance/data/train.lance")
|
| 83 |
+
wiki = ds.scanner(filter="answer_type = 'WikipediaEntity'", columns=["question"], limit=5).to_table()
|
| 84 |
+
```
|
| 85 |
+
|
| 86 |
+
## Why Lance?
|
| 87 |
+
|
| 88 |
+
- One dataset carries questions + answers + aliases + embeddings + indices — no sidecar files.
|
| 89 |
+
- On-disk vector and full-text indices live next to the data, so search works on local copies and on the Hub.
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| 90 |
+
- Schema evolution: add columns (alternate embeddings, generated answers, task labels) without rewriting the data.
|
| 91 |
+
|
| 92 |
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## Source & license
|
| 93 |
+
|
| 94 |
+
Converted from [`mandarjoshi/trivia_qa`](https://huggingface.co/datasets/mandarjoshi/trivia_qa) (`rc.nocontext`). TriviaQA is released under the Apache 2.0 license.
|
| 95 |
+
|
| 96 |
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## Citation
|
| 97 |
+
|
| 98 |
+
```
|
| 99 |
+
@article{joshi2017triviaqa,
|
| 100 |
+
title={TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},
|
| 101 |
+
author={Joshi, Mandar and Choi, Eunsol and Weld, Daniel S and Zettlemoyer, Luke},
|
| 102 |
+
journal={arXiv preprint arXiv:1705.03551},
|
| 103 |
+
year={2017}
|
| 104 |
+
}
|
| 105 |
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```
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