PORTULAN/albertina-100m-portuguese-ptpt-encoder

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    This is the model card for Albertina PT-PT base. You may be interested in some of the other models in the Albertina (encoders) and Gervásio (decoders) families.

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Albertina 100M PTPT

Albertina 100M PTPT is a foundation, large language model for European Portuguese from Portugal.

It is an encoder of the BERT family, based on the neural architecture Transformer and developed over the DeBERTa model, with most competitive performance for this language. It is distributed free of charge and under a most permissible license.

Albertina's Family of Models
Albertina 1.5B PTPT
Albertina 1.5B PTBR
Albertina 1.5B PTPT 256
Albertina 1.5B PTBR 256
Albertina 900M PTPT
Albertina 900M PTBR
Albertina 100M PTPT
Albertina 100M PTBR

Albertina 100M PTPT is developed by a joint team from the University of Lisbon and the University of Porto, Portugal. For further details, check the respective publication:

@misc{albertina-pt-fostering,
      title={Fostering the Ecosystem of Open Neural Encoders
            for Portuguese with Albertina PT-* family}, 
      author={Rodrigo Santos and João Rodrigues and Luís Gomes
              and João Silva and António Branco
              and Henrique Lopes Cardoso and Tomás Freitas Osório
              and Bernardo Leite},
      year={2024},
      eprint={2403.01897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Please use the above cannonical reference when using or citing this model.

Model Description

This model card is for Albertina 100M PTPT base, with 100M parameters, 12 layers and a hidden size of 768.

Albertina-PT-PT base is distributed under an MIT license.

DeBERTa is distributed under an MIT license.

Training Data

Albertina 100M PTPT was trained over a 2.2 billion token data set that resulted from gathering some openly available corpora of European Portuguese from the following sources:

• OSCAR: 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 Portugal. We used the January 2023 version of OSCAR, which is based on the November/December 2022 version of Common Crawl.
• DCEP: the Digital Corpus of the European Parliament is a multilingual corpus including documents in all official EU languages published on the European Parliament's official website. We retained its European Portuguese portion.
• Europarl: the European Parliament Proceedings Parallel Corpus is extracted from the proceedings of the European Parliament from 1996 to 2011. We retained its European Portuguese portion.
• ParlamentoPT: the ParlamentoPT is a data set we obtained by gathering the publicly available documents with the transcription of the debates in the Portuguese Parliament.

Preprocessing

We filtered the PTPT 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.

Training

As codebase, we resorted to the DeBERTa V1 base, for English.

To train Albertina 100M PTPT, the data set was tokenized with the original DeBERTa tokenizer with a 128 token sequence truncation and dynamic padding. The model was trained using the maximum available memory capacity resulting in a batch size of 3072 samples (192 samples per GPU). We opted for a learning rate of 1e-5 with linear decay and 10k warm-up steps. A total of 200 training epochs were performed resulting in approximately 180k steps. The model was trained for one day on a2-megagpu-16gb Google Cloud A2 VMs with 16 GPUs, 96 vCPUs and 1.360 GB of RAM.

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 PTPT 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 900m PTPT	0.8339	0.4225	0.9171	0.8801
Albertina 100m PTPT	0.6787	0.4507	0.8829	0.8581

How to use

You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='PORTULAN/albertina-ptpt-base')
>>> unmasker("A culinária portuguesa é rica em sabores e [MASK], tornando-se um dos maiores tesouros do país.")

[{'score': 0.8332648277282715, 'token': 14690, 'token_str': ' costumes', 'sequence': 'A culinária portuguesa é rica em sabores e costumes, tornando-se um dos maiores tesouros do país.'},
{'score': 0.07860890030860901, 'token': 29829, 'token_str': ' cores', 'sequence': 'A culinária portuguesa é rica em sabores e cores, tornando-se um dos maiores tesouros do país.'},
{'score': 0.03278181701898575, 'token': 35277, 'token_str': ' arte', 'sequence': 'A culinária portuguesa é rica em sabores e arte, tornando-se um dos maiores tesouros do país.'},
{'score': 0.009515956044197083, 'token': 9240, 'token_str': ' cor', 'sequence': 'A culinária portuguesa é rica em sabores e cor, tornando-se um dos maiores tesouros do país.'},
{'score': 0.009381960146129131, 'token': 33455, 'token_str': ' nuances', 'sequence': 'A culinária portuguesa é rica em sabores e nuances, tornando-se um dos maiores tesouros do país.'}]

The model can be used by fine-tuning it for a specific task:

>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
>>> from datasets import load_dataset

>>> model = AutoModelForSequenceClassification.from_pretrained("PORTULAN/albertina-ptpt-base", num_labels=2)
>>> tokenizer = AutoTokenizer.from_pretrained("PORTULAN/albertina-ptpt-base")
>>> dataset = load_dataset("PORTULAN/glue-ptpt", "rte")

>>> def tokenize_function(examples):
...     return tokenizer(examples["sentence1"], examples["sentence2"], padding="max_length", truncation=True)

>>> tokenized_datasets = dataset.map(tokenize_function, batched=True)

>>> training_args = TrainingArguments(output_dir="albertina-ptpt-rte", evaluation_strategy="epoch")
>>> trainer = Trainer(
...     model=model,
...     args=training_args,
...     train_dataset=tokenized_datasets["train"],
...     eval_dataset=tokenized_datasets["validation"],
... )

>>> trainer.train()

Citation

When using or citing this model, kindly cite the following publication:

@misc{albertina-pt-fostering,
      title={Fostering the Ecosystem of Open Neural Encoders
            for Portuguese with Albertina PT-* family}, 
      author={Rodrigo Santos and João Rodrigues and Luís Gomes
              and João Silva and António Branco
              and Henrique Lopes Cardoso and Tomás Freitas Osório
              and Bernardo Leite},
      year={2024},
      eprint={2403.01897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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 ALBERTINA - Foundation Encoder Model for Portuguese and AI, funded by FCT—Fundação para a Ciência e Tecnologia under the grant CPCA-IAC/AV/478394/2022; 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; and LIACC - Laboratory for AI and Computer Science, funded by FCT—Fundação para a Ciência e Tecnologia under the grant FCT/UID/CEC/0027/2020.