| --- |
| language: |
| - en |
| pretty_name: Scientific Multitask Instructions |
| task_categories: |
| - text-generation |
| - question-answering |
| - summarization |
| tags: |
| - scientific |
| - instruction-tuning |
| - conversational |
| - supervised-fine-tuning |
| - grpo |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Scientific Multitask Instructions |
|
|
| A multi-task scientific instruction-following dataset created for |
| supervised fine-tuning and preference-optimization experiments. |
|
|
| ## Dataset summary |
|
|
| The dataset contains 1,576 conversational scientific examples across |
| eight task types. |
|
|
| | Split | Examples | |
| |---|---:| |
| | Train | 1,260 | |
| | Validation | 158 | |
| | Test | 158 | |
| | Total | 1,576 | |
|
|
| ## Task distribution |
|
|
| | Task | Examples | |
| |---|---:| |
| | Scientific question answering | 256 | |
| | Summarization | 220 | |
| | Concept explanation | 200 | |
| | Method comparison | 200 | |
| | Technical simplification | 200 | |
| | Bullet generation | 200 | |
| | Data analysis | 200 | |
| | Code generation | 100 | |
|
|
| ## Fields |
|
|
| - `id`: unique example identifier |
| - `category`: scientific subject category |
| - `task`: instruction-task category |
| - `difficulty`: example difficulty |
| - `messages`: system, user, and assistant chat messages |
|
|
| ## Usage |
|
|
| from datasets import load_dataset |
| |
| dataset = load_dataset( |
| "Miladsaeedi70/scientific-multitask-instructions", |
| token=True, |
| ) |
| |
| print(dataset) |
| print(dataset["train"][0]) |
| |
| The `token=True` argument is needed while the repository is private. |
|
|
| ## Intended use |
|
|
| This dataset is intended for: |
|
|
| - Scientific instruction tuning |
| - Multi-task supervised fine-tuning |
| - LoRA and PEFT experiments |
| - Preference optimization |
| - GRPO reward-design research |
| - Scientific language-model evaluation |
|
|
| ## Split methodology |
|
|
| Examples were divided into train, validation, and test sets using grouped |
| splitting to reduce prompt-template leakage between splits. |
|
|
| ## Known limitations |
|
|
| - The dataset is relatively small. |
| - Some answers may contain scientific inaccuracies. |
| - Task categories are not equally represented. |
| - Lexical-overlap metrics do not fully measure factual correctness. |
| - Responses should be independently verified for high-stakes use. |
|
|
| ## Publication status |
|
|
| The repository is initially private. Provenance, licensing, duplicate |
| checks, and sample quality should be reviewed before public release. |
|
|
