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EVALUATION.md

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Models

We are making a few models fine-tuned on the datasets in this benchmark available on the Hugging Face Hub.

Datasets mDeBERTa v3 BERT Large BERT Base
ASSIN 2 - STS Link Link Link
ASSIN 2 - RTE Link Link Link
ASSIN - STS Link Link Link
ASSIN - RTE Link Link Link
HateBR Link Link Link
FaQUaD-NLI Link Link Link
PorSimplesSent Link Link Link

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 uploaded to the Hugging Face Hub and displayed in the tables below.

Links and 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