🇵🇹 BERTimbau fine-tuned on ClaimPT (Claim Extraction)
This model is a fine-tuned version of
neuralmind/bert-base-portuguese-cased on the
ClaimPT dataset for
claim and non-claim detection in Portuguese news articles.
It classifies each token as part of a
Claim or
Non-Claim span, following the guidelines described below. For more information visit our
GitHub repository
🧠 Model Details
Model type: Transformer-based encoder (BERT)
Base model: neuralmind/bert-base-portuguese-cased
Fine-tuning objective: Token classification
Task: Claim Extraction
Language: Portuguese (pt)
Framework: 🤗 Transformers
License: CC BY-NC 4.0
(non-commercial use)
Authors: Ricardo Campos, Raquel Sequeira, Sara Nerea, Inês Cantante, Diogo Folques, Luís Filipe Cunha, João Canavilhas, António Branco, Alípio Jorge, Sérgio Nunes, Nuno Guimarães, Purificação Silvano
Institution(s): INESC TEC, University of Beira Interior, University of Porto, University of Lisbon
📘 Dataset
Dataset: ClaimPT
Authors: Ricardo Campos, Raquel Sequeira, Sara Nerea, Inês Cantante, Diogo Folques, Luís Filipe Cunha, João Canavilhas, António Branco, Alípio Jorge, Sérgio Nunes, Nuno Guimarães, Purificação Silvano
ClaimPT, a dataset of European Portuguese news articles annotated for factual claims, comprising 1,308 articles and 6,875 individual annotations.
⚙️ Training Details
- Task formulation: Token classification with labels
{B-Claim, I-Claim, B-Non-Claim, I-Non-Claim, O}
- Loss: Cross-entropy
- Optimizer: AdamW
- Learning rate: 2e-5
- Batch size: 16
- Max sequence length: 512
- Truncation strategy: Chunking with 128-token overlap (stride)
📊 Evaluation
| Model | Label | Precision (%) | Recall (%) | F1 (%) |
|---|
| BERT-Chunk | Claim | 40.38 | 22.58 | 28.97 |
| Non-Claim | 55.96 | 68.71 | 61.68 |
| Micro Avg | 55.24 | 64.31 | 59.43 |
🧩 Usage
1from transformers import AutoTokenizer, AutoModelForTokenClassification
2
3tokenizer = AutoTokenizer.from_pretrained("lfcc/bertimbau-claimpt-sent")
4model = AutoModelForTokenClassification.from_pretrained("lfcc/bertimbau-claimpt-sent")
5
6text = '"O governo vai reduzir o IVA dos alimentos", disse o ministro da economia.'
7inputs = tokenizer(text, return_tensors="pt")
8outputs = model(**inputs)
9logits = outputs.logits
Annotation Guidelines
Detailed annotation instructions, including procedures, quality-control measures, and schema definitions, are available in the document:
This manual describes:
- The annotation process and methodology
- The annotation scheme and entity structures
- The definition of a claim
- Metadata and label taxonomy
- Examples and boundary cases
Researchers interested in replicating the annotation or training models should refer to this guide.
Citation
If you use this dataset, please cite:
1@dataset{claimpt2025,
2 author = {Ricardo Campos and Raquel Sequeira and Sara Nerea and Inês Cantante and Diogo Folques and Luís Filipe Cunha and João Canavilhas and António Branco and Alípio Jorge and Sérgio Nunes and Nuno Guimarães and Purificação Silvano},
3 title = {ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles},
4 year = {2025},
5 doi = {https://rdm.inesctec.pt/dataset/cs-2025-008},
6 institution = {INESC TEC}
7}
Credits and Acknowledgements
Affiliated Institutions
Acknowledgements
This work was carried out as part of the project
Accelerat.AI (Ref. C644865762-00000008), financed by IAPMEI and the European Union — Next Generation EU Fund, within the scope of call for proposals no. 02/C05-i01/2022 — submission of final proposals for project development under the Mobilizing Agendas for Business Innovation of the Recovery and Resilience Plan.
Ricardo Campos, Alípio Jorge, and Nuno Guimarães also acknowledge support from the
StorySense project (Ref. 2022.09312.PTDC, DOI:
10.54499/2022.09312.PTDC).
