Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Sub-tasks:
extractive-qa
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 1,805 Bytes
05cf79d f1068dd 05cf79d f1068dd 05cf79d a5381d1 522046d a5381d1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | ---
language:
- en
pretty_name: QASPER Chat-Format
license: unknown
task_categories:
- question-answering
task_ids:
- extractive-qa
source_datasets:
- allenai/qasper
tags:
- text
- science
- chat-format
- instruction-tuning
- datasets
- allenai/qasper
- evidence-selection
- arxiv:2105.03011
---
# QASPER (Chat-Format Preparation)
This dataset is a chat-format preparation of QASPER for supervised fine-tuning (SFT).
## Format
This format is commonly referred to as:
- chat-format SFT data
- instruction-tuning conversations
- OpenAI-style `messages` format
## Included files
- `train.jsonl`
- `validation.jsonl`
- `stats.json`
- `prepare_qasper_unsloth.py`
## Source
- Base dataset: `allenai/qasper`
## Original Dataset Highlights
- Original dataset: `allenai/qasper`
- Focus: question answering on scientific NLP papers with evidence selection.
- Reported scale on source card: 5,049 questions over 1,585 papers.
- Key annotation properties: multiple answer types (free-form, extractive, yes/no, unanswerable) and evidence annotations.
- Paper: [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://arxiv.org/abs/2105.03011)
## Preparation summary
- One row per `(paper, question)` using the best available annotation.
- Answer normalization priority:
1. free-form
2. yes/no
3. extractive spans
4. unanswerable
- Context mode is mixed between:
- evidence-only
- full-text
- User prompt follows a question-first structure.
Assistant target is the normalized answer text.
## Schema
Each JSONL row contains:
- `messages`
- `user`: text instruction + question + title + abstract + context
- `assistant`: text answer
- `meta`: ids, answer type, context mode, evidence count
## Reproduction
```bash
python prepare_qasper_unsloth.py
```
|