| --- |
| license: cc-by-sa-3.0 |
| language: |
| - en |
| task_categories: |
| - text-retrieval |
| - question-answering |
| size_categories: |
| - 100K<n<1M |
| source_datasets: |
| - extended|natural_questions |
| tags: |
| - generative-retrieval |
| - dsi |
| - nci |
| - ripor |
| - semantic-id |
| configs: |
| - config_name: corpus |
| data_files: |
| - split: corpus |
| path: data/corpus.jsonl |
| - split: corpus_summary |
| path: data/corpus_summary.jsonl |
| - config_name: pairs |
| data_files: |
| - split: train |
| path: data/train.jsonl |
| - split: validation |
| path: data/valid.jsonl |
| dataset_info: |
| - config_name: corpus |
| features: |
| - name: docid |
| dtype: int64 |
| - name: document |
| dtype: string |
| splits: |
| - name: corpus |
| num_examples: 109650 |
| - name: corpus_summary |
| num_examples: 21119 |
| - config_name: pairs |
| features: |
| - name: query |
| dtype: string |
| - name: docid |
| dtype: int64 |
| - name: nq_id |
| dtype: string |
| - name: url |
| dtype: string |
| - name: title |
| dtype: string |
| - name: long_answer |
| dtype: string |
| - name: short_answer |
| dtype: string |
| splits: |
| - name: train |
| num_examples: 307373 |
| - name: validation |
| num_examples: 7830 |
| --- |
| |
| # NQ320K (NCI-style preprocessing) |
|
|
| A reproduction of the **NQ320K** corpus used by generative-retrieval papers |
| (DSI, NCI, GenRet, RIPOR, Ultron, LTRGR, …) built directly from the |
| Hugging Face `google-research-datasets/natural_questions` snapshot. |
|
|
| ## At a glance |
|
|
| | Split | Rows | |
| |------------------|--------------| |
| | `corpus` | 109,650 | |
| | `corpus_summary` | 21,119 | |
| | `train` pairs | 307,373 | |
| | `validation` pairs | 7,830 | |
|
|
| * Train pairs with non-empty `long_answer`: **152,148 / 307,373** (49.5%) |
| * Train pairs with non-empty `short_answer`: **106,926 / 307,373** (34.8%) |
| * Date built: 2026-05-06 |
|
|
| ## Schema |
|
|
| ### `corpus.jsonl` |
|
|
| ```json |
| {"docid": 0, "document": "<NCI doc_tac string, ~5K-50K chars>"} |
| ``` |
|
|
| ### `train.jsonl` / `valid.jsonl` |
|
|
| ```json |
| { |
| "query": "when is the last episode of season 8 of the walking dead", |
| "docid": 0, |
| "nq_id": "5225754983651766092", |
| "url": "https://en.wikipedia.org//w/index.php?title=The_Walking_Dead_(season_8)&oldid=...", |
| "title": "The Walking Dead (season 8)", |
| "long_answer": "List of The Walking Dead episodes ...", |
| "short_answer": "" |
| } |
| ``` |
|
|
| `docid` is a stable integer that **joins** to `corpus.jsonl`. To materialise |
| a (query, document) pair: |
|
|
| ```python |
| from datasets import load_dataset |
| corpus = load_dataset("<your-username>/NQ320K-NCI", "corpus", split="corpus") |
| pairs = load_dataset("<your-username>/NQ320K-NCI", "pairs", split="train") |
| |
| doc_lookup = {r["docid"]: r["document"] for r in corpus} |
| for p in pairs: |
| document = doc_lookup[p["docid"]] |
| # ... feed (p["query"], document) to your model |
| ``` |
|
|
| ## Preprocessing |
|
|
| Faithful port of the official NCI notebook |
| ([Wang et al., NeurIPS 2022](https://arxiv.org/abs/2206.02743), |
| [Data_process/NQ_dataset/NQ_dataset_Process.ipynb](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI/blob/main/Data_process/NQ_dataset/NQ_dataset_Process.ipynb) |
| in the released code). Each NQ row produces one record: |
|
|
| 1. Reconstruct `document_text = " ".join(document.tokens.token)` |
| (HTML tags appear as their own tokens). |
| 2. `title = document.title` |
| 3. `abs = document_text[<P>+3 : </P>]` — **HTML tags inside `<P>` are kept**, matching NCI. |
| 4. `content = document_text[</P>+4 : second-to-last </Ul>]`, |
| then HTML stripped, `\n` deleted, multiple spaces collapsed. |
| 5. `doc_tac = title + abs + content` — **no separators**. |
| 6. `long_answer` / `short_answer`: token-span slices from the |
| first annotator (`annotations[0]`), HTML stripped. |
|
|
| Documents are de-duplicated by their **BERT-uncased-tokenizer-normalised |
| title** (`tokenizer.tokenize(title) → convert_to_ids → decode`), exactly as |
| in NCI's released notebook. Concatenating `train + validation` and dropping |
| duplicates yields **109,650** unique documents (NCI reports 109,739; the |
| ~80-doc delta comes from a slightly newer Hugging Face snapshot of NQ). |
|
|
| ## Known formatting characteristics |
|
|
| These are **inherited from NCI's preprocessing** and intentional: |
|
|
| * **Token-joined whitespace**: `"AMC ,"` instead of `"AMC,"`. NCI's `doc_tac` |
| is built by `" ".join(tokens)`, leaving a space before every punctuation |
| mark. NCI's downstream BERT/T5 tokenizer absorbs these correctly; you may |
| want to detokenize when feeding into other encoders. |
| * **HTML tags inside `abs`**: e.g. `"<Table><Tr>…<P>The eighth season…</P>"`. |
| Only `content` has its tags stripped. This is the canonical NCI format. |
| * **Non-detokenized hyphenation**: `"post - apocalyptic"`, `"Spider - Man"`. |
|
|
| ## Caveat: `nq_id` is a string |
| |
| NQ's original `example_id` is a **uint64**, and roughly half of the IDs |
| exceed `2^63 = 9.22 × 10^18`. They fit unsigned but overflow signed int64. |
|
|
| `nq_id` is therefore stored as a **string**, exactly as Google publishes it. |
| **Do not auto-cast it to int64** — about 50% of the values would silently |
| wrap to negative numbers. If you load with pandas: |
|
|
| ```python |
| import pandas as pd |
| df = pd.read_json("train.jsonl", lines=True, dtype={"nq_id": str}) |
| ``` |
|
|
| If you load with `datasets`, the typed `dataset_info` in this card already |
| enforces `string`, so you don't need to do anything extra: |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("<your-username>/NQ320K-NCI", "pairs") |
| print(ds["train"].features["nq_id"]) # Value(dtype='string', id=None) |
| ``` |
|
|
| ## Corpus Summary |
| We additionally present a subset of corpus as a summarized text. We use `sshleifer/distilbart-cnn-12-6` model for the summarization task. |
|
|
| ## License & attribution |
|
|
| This dataset is a derivative of the Natural Questions dataset by Google |
| ([Kwiatkowski et al., TACL 2019](https://aclanthology.org/Q19-1026/)), |
| released under **CC BY-SA 3.0**. This derivative dataset is therefore also |
| released under **CC BY-SA 3.0** (ShareAlike). |
|
|
| The preprocessing recipe is from |
| [Neural Corpus Indexer (Wang et al., NeurIPS 2022)](https://arxiv.org/abs/2206.02743); |
| see their released |
| [notebook](https://github.com/solidsea98/Neural-Corpus-Indexer-NCI/blob/main/Data_process/NQ_dataset/NQ_dataset_Process.ipynb). |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @article{kwiatkowski2019natural, |
| author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia |
| and Collins, Michael and Parikh, Ankur and Alberti, Chris and |
| Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and |
| Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, |
| Matthew and Chang, Ming-Wei and Dai, Andrew and Uszkoreit, Jakob |
| and Le, Quoc and Petrov, Slav}, |
| title = {Natural Questions: a Benchmark for Question Answering Research}, |
| journal = {Transactions of the Association for Computational Linguistics}, |
| year = {2019} |
| } |
| |
| @inproceedings{wang2022neural, |
| author = {Wang, Yujing and Hou, Yingyan and Wang, Haonan and Miao, Ziming |
| and Wu, Shibin and Sun, Hao and Chen, Qi and Xia, Yuqing and |
| Chi, Chengmin and Zhao, Guoshuai and Liu, Zheng and Xie, Xing |
| and Sun, Hao Allen and Deng, Weiwei and Zhang, Qi and Yang, |
| Mao}, |
| title = {A Neural Corpus Indexer for Document Retrieval}, |
| booktitle = {Advances in Neural Information Processing Systems (NeurIPS)}, |
| year = {2022} |
| } |
| ``` |
|
|
