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bratao/
PortugueseT5-Instruct

Name: PortugueseT5-Instruct
Availability: InStock

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PortugueseT5-Instruct

PortugueseT5-Instruct is an instruction-tuned intermediate checkpoint derived from the PortugueseT5 research line. The doctoral thesis describes supervised tuning on approximately three million Portuguese question-answer pairs derived from Wikipedia and provided by a project partner.

This is not an OpenIE backend and is not registered by portuguese-openie. It is the intermediate model described before task-specific fine-tuning of PortugueseT5OieAbstractive.

Model details

Field	Value
Public repository	`bratao/PortugueseT5-Instruct`
Predecessor described in thesis	`bratao/portugueseT5`
Architecture	T5 encoder-decoder
Task status	general Portuguese instruction-tuning intermediate
Parameters	783,150,080 (approximately 783M; thesis rounds to 770M)
Published weight precision	float32
Approximate repository size	3.13 GB
Audited revision	`b379913821c4e886ead8ae576db7abfa05d9da69` (2026-08-30)

The published 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. It may be a stale copied state, but it does not establish a completed training run. Treat this as an experimental intermediate artifact.

Direct Transformers use

python

import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "bratao/PortugueseT5-Instruct"
revision = "b379913821c4e886ead8ae576db7abfa05d9da69"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModelForSeq2SeqLM.from_pretrained(
    model_id, revision=revision, dtype="auto", device_map="auto"
)

# Illustrative instruction only: the public repository does not document a stable
# prompt template, so validate a template on your own task before relying on it.
prompt = "Pergunta: Em qual cidade fica a UFBA?\nResposta:"
inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to(model.device)
with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

An illustrative desired answer would be Salvador., but this is not a recorded model output or accuracy claim. The public artifact has no documented canonical prompt or validated response for this example.

Training-data provenance

The thesis says the instruction data consists of about three million Portuguese QA pairs derived from Portuguese Wikipedia and supplied by a partner. It does not identify a public dataset release, and the model repository declares no Hugging Face dataset ID. The data is not distributed here; this card deliberately omits datasets and does not reproduce private examples.

Evaluation and status

No trustworthy task metric is published for this exact checkpoint. The training state above appears partial, and scores from later OpenIE checkpoints must not be assigned to this intermediate. Validate it independently for any intended task.

Requirements and hardware

Recent Python, PyTorch, Transformers, and Accelerate.
The float32 weights occupy about 3.13 GB. Around 6–8 GB of available RAM/VRAM is a practical starting point; memory varies with input length and runtime.
GPU is recommended for scale, though CPU inference is possible.

Limitations

No canonical prompt template, completion statement, or task evaluation is public.
The underlying instruction data is not publicly inspectable from this repository, limiting reproducibility and bias analysis.
Output may be incorrect, hallucinated, biased, or unsafe.
Do not treat this checkpoint as an OpenIE model or as a factual authority.

License

No license is declared in the public repository as of 2026-08-30. Missing license metadata is not permission to redistribute or modify the weights. Seek clarification from the author and account for the terms of predecessor weights and data. This card does not infer a license.

Citation

bibtex

@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