roberta-base-frenk-hate
Text classification model based on classla/bcms-bertic
and fine-tuned on the FRANK dataset comprising of LGBT and migrant hatespeech. Only the English subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable).
Fine-tuning hyperparameters
Fine-tuning was performed with simpletransformers
. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
Performance
The same pipeline was run with two other models and with the same dataset. Accuracy and macro F1 score were recorded for each of the 6 fine-tuning sessions and post festum analyzed.
model | average accuracy | average macro F1 |
---|---|---|
roberta-base-frenk-hate | 0.7915 | 0.7785 |
xlm-roberta-large | 0.7904 | 0.77876 |
xlm-roberta-base | 0.7577 | 0.7402 |
distilbert-base-uncased-finetuned-sst-2-english | 0.7201 | 0.69862 |
From recorded accuracies and macro F1 scores p-values were also calculated:
Comparison with xlm-roberta-base
:
test | accuracy p-value | macro F1 p-value |
---|---|---|
Wilcoxon | 0.00781 | 0.00781 |
Mann Whithney U-test | 0.00108 | 0.00108 |
Student t-test | 1.35e-08 | 1.05e-07 |
Comparison with distilbert-base-uncased-finetuned-sst-2-english
:
test | accuracy p-value | macro F1 p-value |
---|---|---|
Wilcoxon | 0.00781 | 0.00781 |
Mann Whithney U-test | 0.00108 | 0.00108 |
Student t-test | 1.33e-12 | 3.03e-12 |
Comparison with xlm-roberta-large
yielded inconclusive results; whereas accuracy was outperformed by this model, the macro F1 score was not. Neither outperformance was statistically significant.