🚀 PamelaBorelli/flan-t5-base-summarization-pt-br
A Portuguese-language summarization model based on the Flan-T5 architecture, fine-tuned for text summarization tasks.
🚀 Quick Start
Basic Usage
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("PamelaBorelli/flan-t5-base-summarization-pt-br")
model = T5ForConditionalGeneration.from_pretrained("PamelaBorelli/flan-t5-base-summarization-pt-br")
input_text = "O corpo está mais propenso a sentir dores com exercícios de alta intensidade | Foto: Getty Images O problema está em saber identificar qual é qual. \"Em algumas situações, é difícil diferenciar uma da outra\", reconhece Juan Francisco Marco, professor do Centro de Ciência do Esporte, Treinamento e Fitness Alto Rendimento, na Espanha. \"A dor boa é aquela que associamos ao exercício físico, que não limita (o movimento) e permite continuar (a se exercitar) até o momento em que o músculo fica realmente esgotado e não trabalha mais\", explica. É importante detectar qual é o tipo de dor que você está sentindo, para evitar ter problemas mais sérios | Foto: Getty Images Para Francisco Sánchez Diego, diretor do centro de treinamento Corpore 10, \"a dor boa se sente no grupo muscular que você trabalhou, tanto durante o treinamento como nos dias seguintes\"."
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
✨ Features
- Multilingual Base: Built on the multilingual flan-t5-base with 248M parameters and a T5-based encoder-decoder architecture.
- Fine-tuned for Portuguese: Specifically fine-tuned for text summarization in Brazilian Portuguese through two finetuning steps.
📦 Installation
No specific installation steps are provided in the original document.
📚 Documentation
General Information
Summary
The original model that served as the basis for the final model is flan-t5-base. It is a multilingual model with 248M parameters and an architecture based on T5 (Text-to-Text Transfer Transformer) with an encoder-decoder. The original Flan-T5 was fine-tuned on a mixture of tasks to improve its generalization ability.
The final model PamelaBorelli/flan-t5-base-summarization-pt-br was trained through instruction and fine-tuning. Two fine-tuning steps were performed: first for text translation using a dataset and then for the summarization task with datasets in Brazilian Portuguese.
Intended Use
The model is designed for text summarization in Brazilian Portuguese and has not been tested for other languages.
Languages
Brazilian Portuguese
Training Data
The model was trained for the summarization task using the following parameters:
evaluation_strategy="steps"
eval_steps=
learning_rate=
per_device_train_batch_size=
per_device_eval_batch_size=
gradient_accumulation_steps=
weight_decay=
num_train_epochs=
save_strategy="steps"
save_steps =
push_to_hub=False
load_best_model_at_end=True
For tokenization, the following parameters were used:
start_prompt= "Sumarize: \n"
end_prompt= "\n\nSumário: "
input_name="coluna_imput"
target_name="coluna_target"
max_input_length = 256
max_target_length = 256
columns_to_remove= ['coluna_to_remove']
📄 License
This model is licensed under the MIT license.