---
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.