AMALIA-ExtraiConheCiencia-PT
Model Description
AMALIA-ExtraiConheCiencia-PT is a fine-tuned version of the AMALIA 9B European Portuguese large language model, adapted for Scientific Information Extraction (SciIE) in Portuguese.
The model was fine-tuned on SCIERC-PT, a European Portuguese translation and curation of the SCIERC dataset. It is designed to perform joint extraction of scientific entities and relations from articles abstracts, producing structured outputs suitable for Named Entity Recognition (NER), Relation Extraction (RE), and downstream Scientific Knowledge Graph construction.
Base Model
- Base model: AMALIA-9B-1225-SFT
- Language: European Portuguese (PT-PT)
- Architecture: Decoder-only generative LLM
- Fine-tuning method: QLoRA
- Fine-tuning Task: Joint Scientific Information Extraction
Intended Use
This model is intended for:
- Scientific Information Extraction in Portuguese:
- Named Entity Recognition from scientific abstracts
- Relation Extraction from scientific abstracts
- Structured extraction for Scientific Knowledge Graph construction
Input Format
The model expects a European Portuguese scientific abstract and task instructions specifying the entity and relation schema.
Example input:
1Extrai entidades e relações do texto científico usando o esquema fornecido.
2
3Tipos de entidade: Tarefa, Método, Métrica, Material, Outros Termos Científicos, Genérico.
4Tipos de relação: Usado-para, Característica-de, Hipónimo-de, Parte-de, Avaliado-para, Comparado-com, Conjunção.
5
6Texto:
7{text}
Output Format
The model was fine-tuned to generate structured outputs containing scientific entities, entity types, and relations.
The expected output corresponds to structured triples.
Example output:
1[
2 {
3 "sujeito": "<subject entity text>",
4 "tipo_sujeito": "<subject entity type>",
5 "relação": "<relation type>",
6 "objeto": "<object entity text>",
7 "tipo_objeto": "<object entity type>"
8 }
9 ...
10]
Training Data
The model was fine-tuned on
SCIERC-PT, a European Portuguese version of SCIERC.
SCIERC-PT contains computer science abstracts annotated with scientific entities and semantic relations.
Entity Types
| English | Portuguese |
|---|
| Task | Tarefa |
| Method | Método |
| Evaluation Metric | Métrica de Avaliação |
| Material | Material |
| Other Scientific Term | Outros Termos Científicos |
| Generic | Genérico |
Relation Types
| English | Portuguese |
|---|
| Used-for | Usado-para |
| Feature-of | Característica-de |
| Hyponym-of | Hipónimo-de |
| Part-of | Parte-de |
| Compare | Comparado-com |
| Conjunction | Conjunção |
| Evaluate-for | Avaliado-para |
Training Procedure
The model was adapted using QLoRA for parameter-efficient supervised fine-tuning. Fine-tuning was performed using the joint extraction formulation, where the model receives a Portuguese scientific abstract and generates structured triples containing entities, entity types, and relations.
Training Configuration
| Parameter | Value |
|---|
| Base model | AMALIA-9B-1225-SFT |
| Fine-tuning method | QLoRA |
| Quantization (training only) | 4-bit NF4 |
| Double quantization | Yes |
| Compute dtype | bf16 |
| Training precision | bf16 |
| Maximum sequence length | 2048 |
| Training batch size | 1 |
| Gradient accumulation steps | 8 |
| Effective batch size | 8 |
| Epochs | 5 |
| Learning rate | 2e-4 |
LoRA rank (r) | 8 |
| LoRA alpha | 16 |
| LoRA dropout | 0.05 |
| Target modules | all linear layers |
| Bias | none |
| Task type | Causal language modeling |
| Evaluation strategy | Per epoch |
| Save strategy | Per epoch |
| Best model selection | Lowest validation loss |
| Early stopping patience | 2 epochs |
The model was trained using the training split of SCIERC-PT and validated on the development split. The best checkpoint was selected according to validation loss.
Evaluation
The model was evaluated on the SCIERC-PT test split using F1-score under a strict exact-match evaluation protocol.
In the fine-tuned setting, AMALIA-ExtraiConheCiencia-PT achieved:
| Task | Macro-F1 |
|---|
| NER | 0.44 |
| RE | 0.16 |
More Information
For more information about the model, the fine-tuning dataset and process, and its use in Portuguese Scientific Information Extraction experiments, see our paper:
- Paper: Evaluating Generative Large Language Models for Portuguese Scientific Information Extraction (NSLP 2026)
- https://lrec.elra.info/lrec2026-ws-nslp-15
