license: mit
language:
- pt
tags:
- gervasio-pt*
- gervasio-ptpt
- gervasio-ptbr
- gervasio-ptpt-base
- gervasio-ptbr-base
- portulan
- albertina-pt*
- albertina-ptpt
- albertina-ptbr
- albertina-ptbr-nobrwac
- albertina-ptpt-base
- albertina-ptbr-base
- clm
- gpt
- portuguese
- decoder
- foundation model
- instruct
datasets:
- PORTULAN/glue-ptpt
This is the model card for Gervásio 7B PT-PT Instruct Decoder You may be interested in some of the other models in the Albertina (encoders) and Gervásio (decoders) families.
Gervásio 7B PT-PT Instruct
Gervásio PT-* is a ??, large language model for the Portuguese language.
+++++corrigir It is a decoder of the GPT family, based on the neural architecture Transformer and developed over the Pythia model, with competitive performance for this language. It has different versions that were trained for different variants of Portuguese (PT), namely the European variant from Portugal (PT-PT) and the American variant from Brazil (PT-BR), and it is distributed free of charge and under a most permissible license. ++++++++
Gervásio PT-PT 7B Instruct is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
For the record, its full name is Gervásio Produz Textos em Português, to which corresponds the natural acronym GPT PT, and which is know tough more shortly as Gervásio PT-*, or even more briefly just as Gervásio, among his acquaintances.
For further details, check the respective publication:
@misc{albertina-pt,
title={Advancing Generative AI for Portuguese with Open Decoder Gervásio~PT*},
author={Rodrigo Santos, João Silva, Luís Gomes, João Rodrigues, António Branco},
year={2024},
eprint={?},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Please use the above cannonical reference when using or citing this model.
Model Description
This model card is for Gervásio-7B-PTPT-Instruct-Decoder, with 7 billion parameters, ? layers and a hidden size of ?.
Gervásio-PT-BR base is distributed under an Apache 2.0 license (like Pythia).
Training Data
Gervásio-PT-BR base was trained over a 3.7 billion token curated selection of documents from the OSCAR data set. The OSCAR data set includes documents in more than one hundred languages, including Portuguese, and it is widely used in the literature. It is the result of a selection performed over the Common Crawl data set, crawled from the Web, that retains only pages whose metadata indicates permission to be crawled, that performs deduplication, and that removes some boilerplate, among other filters. Given that it does not discriminate between the Portuguese variants, we performed extra filtering by retaining only documents whose meta-data indicate the Internet country code top-level domain of Brazil. We used the January 2023 version of OSCAR, which is based on the November/December 2022 version of Common Crawl.
Preprocessing
We filtered the PT-BR corpora using the BLOOM pre-processing pipeline. We skipped the default filtering of stopwords since it would disrupt the syntactic structure, and also the filtering for language identification given the corpus was pre-selected as Portuguese.
Evaluation
The base model version was evaluated on downstream tasks, namely the translations into PT-PT of the English data sets used for a few of the tasks in the widely-used GLUE benchmark.
GLUE tasks translated
We resorted to GLUE-PT, a PT-PT version of the GLUE benchmark. We automatically translated the same four tasks from GLUE using DeepL Translate, which specifically provides translation from English to PT-PT as an option.
| Model | RTE (Accuracy) | WNLI (Accuracy) | MRPC (F1) | STS-B (Pearson) |
|---|---|---|---|---|
| Albertina-PT-PT | 0.8339 | 0.4225 | 0.9171 | 0.8801 |
| Albertina-PT-PT base | 0.6787 | 0.4507 | 0.8829 | 0.8581 |
How to use
You can use this model directly with a pipeline for causal language modeling (CLM):
>>> from transformers import pipeline
>>> generator = pipeline(model='PORTULAN/gervasio-ptbr-base')
>>> generator("A música brasileira é", max_new_tokens=10)
[{'generated_text': 'A música brasileira é uma das mais ricas do mundo. Ao'}]
Acknowledgments
The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language, funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the grant PINFRA/22117/2016; research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the grant CPCA-IAC/AV/478395/2022; innovation project ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação under the grant C625734525-00462629, of Plano de Recuperação e Resiliência, call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização.