| --- |
| pretty_name: EGSciQA-ptPT-V1 |
| language: |
| - pt |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - scientific-reasoning |
| - grounded-generation |
| - supervised-fine-tuning |
| - portuguese |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: train.jsonl |
| - split: validation |
| path: dev.jsonl |
| - split: test |
| path: test.jsonl |
| - split: eval |
| path: eval.jsonl |
| --- |
| |
| # EGSciQA-ptPT-V1 |
|
|
| EGSciQA-ptPT-V1 is the first European Portuguese (`pt-PT`) supervised fine-tuning |
| dataset for evidence-grounded scientific question answering and reasoning. Each |
| example contains a system prompt, an instruction with scientific evidence, and |
| a structured target response using `<raciocinio>` and `<resposta>` sections. |
|
|
| Samples were generated directly from real pt-PT scientific manuscripts available in the [amalia-llm/CorEGe-PT](https://huggingface.co/datasets/amalia-llm/CorEGe-PT) corpus. |
|
|
| This repository contains the final dataset artifacts, including instructions and system prompts. It is not the raw pipeline export. Each eligible source example contributes exactly |
| one row: the original row is not included alongside additional copies. |
|
|
| ## Splits |
|
|
| | Split | File | Rows | |
| | --- | --- | ---: | |
| | Train | `train.jsonl` | 12,571 | |
| | Development/validation | `dev.jsonl` | 1,776 | |
| | Test | `test.jsonl` | 3,574 | |
| | Lite evaluation | `eval.jsonl` | 300 | |
| | **Total** | | **17,921** | |
|
|
| `lite-eval` is a distribution-matched 300-example subset of `test`; it is not counted again in the 17,921 unique examples. |
|
|
| ## Fields |
|
|
| The main fields are: |
|
|
| - `question_id`, `doc_id`: source identifiers. |
| - `source_type`: `single_document` or `document_cluster`. |
| - `question_family`: question-generation family. |
| - `groundability`: `groundable`, `partially_groundable`, or `non_groundable`. |
| - `system`: system message used for SFT. |
| - `instruction`: question and evidence in one augmented presentation. |
| - `response`: supervised assistant target. |
| - `selected_answer`: selected final answer before chat formatting. |
| - `cot_sample_index`: selected candidate index in the source pipeline. |
| - `split`, `split_group`: leakage controlled split metadata. |
| - `cluster_id`: present for document-cluster examples. |
|
|
|
|
| ## Intended use |
|
|
| Use `train` for SFT or RL-Based alignment, `validation` for model selection and |
| training diagnostics, `lite-eval` for faster development evaluations, and |
| `test` only for final held-out evaluation. The lite split approximates the full |
| test distribution over evidence type, domain, groundability, and agreement |
| band. The model should be trained to answer exclusively from the supplied |
| evidence, cite the evidence identifiers it uses, and explicitly state when the |
| evidence is insufficient. |
|
|
| ## Limitations |
|
|
| The examples are synthetically generated from real scientific documental evidence and selected by |
| an automated pipeline. They may retain OCR artifacts, source document errors, or imperfect generated reasoning despite automated validation. |
| The dataset should not be treated as a substitute for expert review. |
