Evaluation of Portuguese Language Models

This repository presents ongoing research on fine-tuning Transformer models for Portuguese natural language understanding tasks.

Demos

All of our fine-tuned models have been integrated into an appropriate Hugging Face Space.

Interact with our models in your browser by exploring our demos:

Summary of the Fine-Tuning procedure

Step 1. Hyperparameter optimization is performed using quasi-random search based on Google's Deep Learning Playbook instructions. The best learning rate, weight decay, and adam beta1 parameters for each Transformer model on each task are identified.
Step 2. The best hyperparameters from step 1 are used to fine-tune each model 40 times with different random seeds for up to one epoch ( Dodge et al. (2020) ). The 10 best models after the first epoch are selected for the next step.
Step 3. The top 10 models from step 2 are fine-tuned for 20 epochs, generating predictions for the test set ( Mosbach et al. (2021) ). We select the model that is closest to the average of predictions (for regression tasks) or the mode of predictions (for classification tasks). This final model is then submitted for evaluation and uploaded to the Hugging Face Hub.

Results

Our fine-tuning procedure has achieved results that are either slightly superior or at the same level as the previous state-of-the-art (if any). Below is a summary of the results achieved on each dataset.

ASSIN 2 - STS ( Semantic Textual Similarity )

Model	Pearson	MSE
ruanchaves/bert-large-portuguese-cased-assin2-similarity	0.86	0.48
Previous SOTA ( for Pearson ) - Souza et al. (2020)	0.852	0.50
SOTA ( for MSE ) - Stilingue	0.817	0.47
ruanchaves/mdeberta-v3-base-assin2-similarity	0.847	0.62
ruanchaves/bert-base-portuguese-cased-assin2-similarity	0.843	0.54

ASSIN 2 - RTE ( Recognizing Textual Entailment )

Model	Accuracy	F1
ruanchaves/bert-large-portuguese-cased-assin2-entailment	0.90	0.90
ruanchaves/mdeberta-v3-base-assin2-entailment	0.90	0.90
Previous SOTA	0.90	0.90
ruanchaves/bert-base-portuguese-cased-assin2-entailment	0.88	0.88

ASSIN - STS ( Semantic Textual Similarity )

Model	Pearson	MSE
ruanchaves/bert-large-portuguese-cased-assin-similarity	0.859	0.3
ruanchaves/mdeberta-v3-base-assin-similarity	0.855	0.39
ruanchaves/bert-base-portuguese-cased-assin-similarity	0.847	0.33

ASSIN - RTE ( Recognizing Textual Entailment )

Model	Accuracy	F1
ruanchaves/mdeberta-v3-base-assin-entailment	0.927	0.862
ruanchaves/bert-large-portuguese-cased-assin-entailment	0.92	0.828
ruanchaves/bert-base-portuguese-cased-assin-entailment	0.92	0.827

HateBR ( Offensive Language Detection )

Model	Accuracy	F1
ruanchaves/bert-large-portuguese-cased-hatebr	0.928	0.928
ruanchaves/mdeberta-v3-base-hatebr	0.916	0.916
ruanchaves/bert-base-portuguese-cased-hatebr	0.914	0.914

FaQUaD-NLI ( Question Answering )

Model	Accuracy	F1
ruanchaves/bert-large-portuguese-cased-faquad-nli	0.929	0.93
ruanchaves/mdeberta-v3-base-faquad-nli	0.926	0.926
ruanchaves/bert-base-portuguese-cased-faquad-nli	0.92	0.883

PorSimplesSent ( Text Simplification )

Model	Accuracy	F1
ruanchaves/mdeberta-v3-base-porsimplessent	0.96	0.956
ruanchaves/bert-base-portuguese-cased-porsimplessent	0.942	0.937
ruanchaves/bert-large-portuguese-cased-porsimplessent	0.921	0.913

Citation

Our research is ongoing, and we are currently working on describing our experiments in a paper, which will be published soon. In the meanwhile, if you would like to cite our work or models before the publication of the paper, please use the following BibTeX citation for this repository:

@software{Chaves_Rodrigues_eplm_2023,
author = {Chaves Rodrigues, Ruan and Tanti, Marc and Agerri, Rodrigo},
doi = {10.5281/zenodo.7781848},
month = {3},
title = {{Evaluation of Portuguese Language Models}},
url = {https://github.com/ruanchaves/eplm},
version = {1.0.0},
year = {2023}
}

RFSFreitas/eplm