Datasets:
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
multi-hop-question-answering
hotpotqa
evidence-selection
question-decomposition
chain-of-thought
supervised-fine-tuning
License:
| language: | |
| - en | |
| license: cc-by-sa-4.0 | |
| pretty_name: Bactrainus HotpotQA Training Suite | |
| task_categories: | |
| - question-answering | |
| - text-generation | |
| tags: | |
| - multi-hop-question-answering | |
| - hotpotqa | |
| - evidence-selection | |
| - question-decomposition | |
| - chain-of-thought | |
| - supervised-fine-tuning | |
| - bactrainus | |
| size_categories: | |
| - 100K<n<1M | |
| arxiv: "2501.06286" | |
| configs: | |
| - config_name: structured | |
| data_files: | |
| - split: train | |
| path: data/structured/train-*.parquet | |
| - config_name: reader-sft | |
| data_files: | |
| - split: train | |
| path: data/reader-sft/train-*.parquet | |
| - config_name: cot-reader-sft | |
| data_files: | |
| - split: train | |
| path: data/cot-reader-sft/train-*.parquet | |
| - config_name: paragraph-selector-sft | |
| data_files: | |
| - split: train | |
| path: data/paragraph-selector-sft/train-*.parquet | |
| - config_name: question-decomposer-sft | |
| data_files: | |
| - split: train | |
| path: data/question-decomposer-sft/train-*.parquet | |
| - config_name: sentence-selector-sft | |
| data_files: | |
| - split: train | |
| path: data/sentence-selector-sft/train-*.parquet | |
| - config_name: decomposed-sentence-selector-sft | |
| data_files: | |
| - split: train | |
| path: data/decomposed-sentence-selector-sft/train-*.parquet | |
| - config_name: joint-selector-reader-sft | |
| data_files: | |
| - split: train | |
| path: data/joint-selector-reader-sft/train-*.parquet | |
| <div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; border: 1px solid #cbd5e1; border-radius: 16px; overflow: hidden; background: #ffffff; margin-bottom: 28px;"> | |
| <div style="background: linear-gradient(135deg, #1f4e79 0%, #12263a 100%); padding: 26px; color: white;"> | |
| <div style="display: flex; align-items: center; justify-content: space-between; flex-wrap: wrap; gap: 10px;"> | |
| <h1 style="margin: 0; font-size: 28px; font-weight: 800; color: white; border: none;">Bactrainus HotpotQA Training Suite</h1> | |
| <span style="background: #0f766e; color: white; font-size: 11px; font-weight: 700; padding: 5px 11px; border-radius: 20px; letter-spacing: 0.5px;">COMPLETE RELEASE</span> | |
| </div> | |
| <p style="margin: 9px 0 0 0; font-size: 15px; color: #dbeafe; font-weight: 500;">One canonical HotpotQA source, eight clean and ID-aligned training views.</p> | |
| </div> | |
| <div style="display: flex; gap: 8px; flex-wrap: wrap; padding: 13px 24px; background: #f8fafc; border-bottom: 1px solid #e2e8f0;"> | |
| <span style="background: #dbeafe; color: #1e40af; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #bfdbfe;">90,447 SOURCE IDs</span> | |
| <span style="background: #ccfbf1; color: #115e59; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #99f6e4;">8 CONFIGURATIONS</span> | |
| <span style="background: #fef3c7; color: #92400e; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #fde68a;">SFT READY</span> | |
| <span style="background: #ede9fe; color: #5b21b6; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #ddd6fe;">ID-ALIGNED</span> | |
| <span style="background: #dcfce7; color: #166534; font-size: 11px; font-weight: 700; padding: 4px 10px; border-radius: 20px; border: 1px solid #bbf7d0;">CC BY-SA 4.0</span> | |
| </div> | |
| <div style="padding: 22px 24px; color: #334155; line-height: 1.65; font-size: 14px;"> | |
| <p style="margin: 0;">The Bactrainus training suite turns the complete <strong>90,447-example</strong> HotpotQA <code>distractor/train</code> split into a canonical structured dataset and seven ready-to-train chat-formatted SFT configurations. Every row keeps the official HotpotQA identifier as <code>source_id</code>, so the same example can be followed across paragraph selection, decomposition, sentence selection, evidence-grounded reasoning, and answer generation without relying on row order.</p> | |
| <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(190px, 1fr)); gap: 12px; margin-top: 18px;"> | |
| <div style="border: 1px solid #bfdbfe; border-radius: 9px; background: #eff6ff; padding: 13px;"> | |
| <strong style="display: block; color: #1f4e79; margin-bottom: 4px;">Traceable provenance</strong> | |
| <span style="font-size: 13px;">Pinned upstream revision, machine-readable source manifest, and reviewed annotation patches.</span> | |
| </div> | |
| <div style="border: 1px solid #99f6e4; border-radius: 9px; background: #f0fdfa; padding: 13px;"> | |
| <strong style="display: block; color: #115e59; margin-bottom: 4px;">Complete modular coverage</strong> | |
| <span style="font-size: 13px;">Reader, CoT, paragraph selector, decomposer, sentence selector, and joint SFT data.</span> | |
| </div> | |
| <div style="border: 1px solid #fde68a; border-radius: 9px; background: #fffbeb; padding: 13px;"> | |
| <strong style="display: block; color: #92400e; margin-bottom: 4px;">Strict integrity checks</strong> | |
| <span style="font-size: 13px;">Schema, evidence bounds, task labels, row counts, ID equality, and 80 shard checksums.</span> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
|  | |
| ## At a glance | |
| | Property | Value | | |
| |---|---| | |
| | Upstream dataset | [`hotpotqa/hotpot_qa`](https://huggingface.co/datasets/hotpotqa/hotpot_qa) | | |
| | Upstream revision | `1908d6afbbead072334abe2965f91bd2709910ab` | | |
| | Upstream configuration | `distractor` | | |
| | Published split | `train` | | |
| | Source examples | 90,447 | | |
| | Candidate paragraphs | 2-10 as supplied upstream; 89,609 examples contain 10 | | |
| | Dataset configurations | 8 | | |
| | Identity key | `source_id` | | |
| | Storage | Sharded Parquet with Zstandard compression | | |
| | License | CC BY-SA 4.0 | | |
| ## Load the data | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset( | |
| "bactrianus/bactrainus-hotpotqa", | |
| "cot-reader-sft", | |
| split="train", | |
| ) | |
| print(dataset.num_rows) # 90447 | |
| print(dataset[0]["source_id"]) | |
| print(dataset[0]["messages"]) | |
| ``` | |
| For a reproducible experiment, pin the dataset commit: | |
| ```python | |
| dataset = load_dataset( | |
| "bactrianus/bactrainus-hotpotqa", | |
| "structured", | |
| split="train", | |
| revision="7f3a1d4d21f22aad7262d8ffd6520f31186b284d", | |
| ) | |
| ``` | |
| ## Configurations | |
| Each configuration contains exactly 90,447 unique `source_id` values. | |
| | Configuration | What the model sees | Training target | | |
| |---|---|---| | |
| | `structured` | Canonical question and the complete candidate set | Answer, paragraph titles, and supporting facts as typed fields | | |
| | `reader-sft` | Question and gold supporting sentences | Final short answer | | |
| | `cot-reader-sft` | Question and gold supporting sentences | Ordered evidence trace followed by the answer | | |
| | `paragraph-selector-sft` | Question and the complete candidate set | Minimal supporting paragraph titles | | |
| | `question-decomposer-sft` | Question and selected supporting paragraphs | Ordered, paragraph-grounded sub-questions | | |
| | `sentence-selector-sft` | Question and selected supporting paragraphs | Supporting title/index pairs | | |
| | `decomposed-sentence-selector-sft` | Question, sub-questions, and selected paragraphs | Supporting title/index pairs | | |
| | `joint-selector-reader-sft` | Question and the complete candidate set | Supporting title/index pairs and final answer | | |
| The seven SFT views are deterministic transformations of `structured`. The CoT | |
| view is a traceable **evidence trace**: each step copies an annotated supporting | |
| sentence and records its exact title and zero-based sentence index. The | |
| decomposition view is generated from the | |
| paragraphs available after paragraph selection and is therefore complete, | |
| stable, and reproducible. | |
| ## Record formats | |
| ### Canonical `structured` record | |
| ```json | |
| { | |
| "source_id": "official-hotpotqa-id", | |
| "question": "...", | |
| "answer": "...", | |
| "question_type": "bridge", | |
| "difficulty": "hard", | |
| "candidate_paragraphs": [ | |
| {"title": "...", "sentences": ["...", "..."]} | |
| ], | |
| "supporting_facts": [ | |
| {"title": "...", "sentence_index": 1} | |
| ], | |
| "gold_paragraph_titles": ["..."] | |
| } | |
| ``` | |
| The candidate paragraphs preserve upstream count and order. Every supporting title | |
| must resolve to one of those paragraphs and every sentence index is checked | |
| against the corresponding sentence list. | |
| ### SFT record | |
| ```json | |
| { | |
| "source_id": "official-hotpotqa-id", | |
| "task": "cot_reader", | |
| "messages": [ | |
| {"role": "system", "content": "..."}, | |
| {"role": "user", "content": "..."}, | |
| {"role": "assistant", "content": "...training target..."} | |
| ] | |
| } | |
| ``` | |
| The data stores framework-neutral chat turns. Apply the native chat template of | |
| the chosen base model during tokenization; no tokenizer-specific control tokens | |
| are embedded in the Parquet files. | |
| ### CoT target shape | |
| ```text | |
| rationale: | |
| 1. [Paragraph title, sentence 0] Exact annotated evidence sentence. | |
| 2. [Second title, sentence 2] Exact annotated evidence sentence. | |
| answer: ***FINAL ANSWER*** | |
| ``` | |
| ### Decomposition target shape | |
| ```text | |
| sub-questions: | |
| 1. What information in "First paragraph" is needed to answer the original question? | |
| 2. How does the relevant information in "Second paragraph" combine with the previous evidence to determine the answer? | |
| ``` | |
| ## How the suite is built | |
| The release builder downloads the immutable upstream revision by default, | |
| normalizes the Hugging Face feature layout, constructs every view from the same | |
| typed record, writes Parquet shards in a private staging directory, and installs | |
| them only after strict validation succeeds. | |
| ```bash | |
| git clone https://github.com/Iman998/bactrainus.git | |
| cd bactrainus | |
| python -m pip install -e ".[data]" | |
| # From the dataset-package directory: | |
| python scripts/build_release.py --root . | |
| python scripts/validate_release.py --root . | |
| ``` | |
| A complete official JSON or JSONL export can also be used: | |
| ```bash | |
| python scripts/build_release.py hotpot_train_v1.1.json --root . | |
| ``` | |
| `SOURCE_MANIFEST.json` records the upstream repository, immutable revision, | |
| configuration, split, row count, identity key, and published configurations. | |
| `CHECKSUMS.sha256` records every Parquet shard digest. | |
| The pinned upstream split contains 22 supporting-fact entries whose sentence | |
| indices are outside their annotated paragraphs. `SOURCE_PATCHES.json` records | |
| each affected `source_id`, original index, action, and replacement when needed. | |
| This reviewed repair covers 0.010% of supporting-fact entries, retains all | |
| 90,447 questions, and is applied before any SFT view is constructed. | |
| ## Integrity guarantees | |
| The validator enforces: | |
| - exactly 90,447 rows and 90,447 unique IDs in every configuration; | |
| - identical `source_id` sets across all eight configurations; | |
| - the exact upstream two-to-ten paragraph distribution, including 89,609 ten-paragraph records; | |
| - valid evidence titles and zero-based sentence bounds; | |
| - exact task identifiers and ordered `system` / `user` / `assistant` messages; | |
| - the documented 17,972 easy, 56,814 medium, and 15,661 hard examples; and | |
| - SHA-256 coverage for every published Parquet shard. | |
| No development examples, model predictions, score tables, notebooks, | |
| credentials, or checkpoints are bundled with the training suite. | |
| ## Recommended use | |
| Use `structured` when designing a new task representation or auditing evidence. | |
| Use an SFT configuration when training the corresponding modular component. | |
| The IDs make it straightforward to combine configurations without fuzzy joins: | |
| ```python | |
| reader = load_dataset( | |
| "bactrianus/bactrainus-hotpotqa", "reader-sft", split="train" | |
| ) | |
| decomposer = load_dataset( | |
| "bactrianus/bactrainus-hotpotqa", "question-decomposer-sft", split="train" | |
| ) | |
| assert set(reader["source_id"]) == set(decomposer["source_id"]) | |
| ``` | |
| This repository is training data, not an independent held-out benchmark. Follow | |
| the official HotpotQA protocol for benchmark reporting and disclose preprocessing, | |
| prompt, retrieval, and answer-normalization choices. | |
| ## Limitations | |
| - The suite inherits factual, coverage, annotation, and social biases from | |
| Wikipedia and HotpotQA. | |
| - The distractor setting supplies a bounded candidate set and does not measure | |
| open-corpus retrieval. | |
| - Supporting facts are dataset annotations and may not exhaust every relevant | |
| sentence. | |
| - Gold-grounded CoT and decomposition targets favor traceability and | |
| reproducibility over stylistic diversity. | |
| - Prompt wording is one clean task formulation and may need adaptation for a | |
| different model family. | |
| ## License and attribution | |
| This dataset is released under | |
| [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/), consistent with | |
| the upstream HotpotQA license. Redistributions and adaptations must preserve | |
| attribution and use a compatible ShareAlike license. See | |
| [`ATTRIBUTION.md`](ATTRIBUTION.md) for the complete attribution statement. | |
| ## Citation | |
| If you use this training suite, cite both Bactrainus and HotpotQA: | |
| ```bibtex | |
| @article{barati2025bactrainus, | |
| title = {Bactrainus: Optimizing Large Language Models for Multi-hop Complex Question Answering Tasks}, | |
| author = {Barati, Iman and Ghafouri, Arash and Minaei-Bidgoli, Behrouz}, | |
| journal = {arXiv preprint arXiv:2501.06286}, | |
| year = {2025}, | |
| url = {https://arxiv.org/abs/2501.06286} | |
| } | |
| @inproceedings{yang2018hotpotqa, | |
| title = {{HotpotQA}: A Dataset for Diverse, Explainable Multi-hop Question Answering}, | |
| author = {Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William W. and Salakhutdinov, Ruslan and Manning, Christopher D.}, | |
| booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}, | |
| year = {2018}, | |
| pages = {2369--2380}, | |
| doi = {10.18653/v1/D18-1259} | |
| } | |
| ``` | |
| ## Links | |
| - Models: <https://huggingface.co/bactrianus> | |
| - Clean code: <https://github.com/Iman998/bactrainus> | |
| - Article archive: <https://arxiv.org/abs/2501.06286> | |
| For the full method, experiments, and interpretation, read the | |
| [Bactrainus article on arXiv](https://arxiv.org/abs/2501.06286). | |