PortugueseT5Oie
PortugueseT5Oie is a Portuguese T5 encoder-decoder research checkpoint that the
current portuguese-openie registry classifies as extractive Open Information
Extraction. No distinct thesis section, training manifest, or evaluation file was
found that establishes the exact task and prompt for these public bytes. The
classification and Entrada/Resposta adapter below are therefore experimental
integration choices, not recovered checkpoint documentation.
The artifact is available through the unified portuguese-openie API, but its
published training state and evaluation provenance are incomplete. Use it as a
research checkpoint, pin the model revision, and validate it on your own data.
Model details
| Field | Value |
|---|---|
| Public repository | bratao/PortugueseT5Oie |
| Model family | PortugueseT5 encoder-decoder |
| Task in current library registry | Portuguese extractive OpenIE; artifact provenance unverified |
| Parameters | 783,150,080 (approximately 783M; thesis rounds to 770M) |
| Published weight precision | float32 |
| Approximate repository size | 3.13 GB |
| Audited revision | 3600cd62101e3cfdbafa46a7c3187ef4c69fc436 (2026-08-30) |
The public trainer state records step 2,000 of a nominal 1,291,623-step, three-epoch schedule (epoch approximately 0.00465), with no best metric or best checkpoint. The state may be stale or copied, but it cannot demonstrate that the published weights are the intended final checkpoint.
Use with portuguese-openie
pip install "portuguese-openie[transformers]"
from portuguese_openie import Model, PortugueseOpenIE
extractor = PortugueseOpenIE(Model.PORTUGUESE_T5_OIE)
triples = extractor.extract("A UFBA está localizada em Salvador.")
print([triple.to_dict() for triple in triples])
No model path is required. The library downloads public files from Hugging Face on first use and reuses the standard local Hugging Face cache afterward.
Expected normalized output shape (illustrative, not a recorded E2E result):
[{"ARG0": "A UFBA", "V": "está localizada em", "ARG1": "Salvador"}]
Direct Transformers use
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model_id = "bratao/PortugueseT5Oie"
revision = "3600cd62101e3cfdbafa46a7c3187ef4c69fc436"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id, revision=revision, dtype="auto", device_map="auto"
)
sentence = "A UFBA está localizada em Salvador."
prompt = f"Entrada:\n{sentence}\nResposta:\n"
inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
with torch.inference_mode():
output = model.generate(**inputs, max_new_tokens=512, do_sample=False)
raw = tokenizer.decode(output[0], skip_special_tokens=True)
print(raw)
The current portuguese-openie backend and a locally modified inference script use:
Entrada:
{sentence}
Resposta:
Use greedy decoding for the closest match to that local integration. This exact
Entrada/Resposta string is not independently established as the fine-tuning
template. The library parses common JSON and legacy ARG0/V/ARG1 formats into
a normalized triple object, but this model/prompt pair still needs an E2E test.
Evaluation and status
No quantitative result in the thesis can be safely attributed to this exact public
checkpoint. The reported T5 score of 0.2732 perfect-match F1
and 0.5171 lexical F1 belongs to PortugueseT5OieAbstractive, not this repository.
Do not reuse those numbers here. This artifact remains experimental until its final
checkpoint and evaluation are independently confirmed.
Training-data provenance
The public repository does not declare a dataset ID, and no checkpoint-specific
training mixture was found. Corpora used elsewhere in the dissertation cannot be
assigned to this artifact by name alone. This card therefore makes no exact dataset
claim and intentionally omits datasets from YAML.
Requirements and hardware
- Recent Python, PyTorch, Transformers, and Accelerate.
- The float32 repository is about 3.13 GB. Around 6–8 GB of free RAM/VRAM is a practical starting point; actual memory depends on runtime and sequence length.
- GPU execution is recommended for throughput but CPU inference is possible.
Limitations
- Published training state appears partial and checkpoint provenance needs review.
- Generated text can be malformed, incomplete, duplicated, or hallucinated even though the target task is extractive; verify fields against source spans.
- No exact public evaluation is available for this artifact.
- Domain coverage, dialectal robustness, fairness, and long-text behavior are not established. An extraction is not fact verification.
License
No license is declared in the public repository as of 2026-08-30. Absence of a license is not permission to redistribute or modify the weights. Seek author clarification and review predecessor-model and data terms before use.
Citation
@phdthesis{cabral2025evolving,
author = {Cabral, Bruno Souza},
title = {Evolving Open Information Extraction for Portuguese employing Language Models},
school = {Universidade Federal da Bahia},
year = {2025}
}
@inproceedings{cabral2022portnoie,
author = {Cabral, Bruno and Souza, Marlo and Claro, Daniela Barreiro},
title = {PortNOIE: A Neural Framework for Open Information Extraction for the Portuguese Language},
booktitle = {Computational Processing of the Portuguese Language (PROPOR 2022)},
year = {2022},
doi = {10.1007/978-3-030-98305-5_23}
}
Project: Portuguese-OpenIE · PortNOIE paper · Generative OpenIE paper