roberta-base-frenk-hate
Text classification model based on roberta-base
and fine-tuned on the FRENK 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 transformer models and fasttext
for comparison. 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 |
fasttext | 0.725 | 0.707 |
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 xlm-roberta-large
yielded inconclusive results. roberta-base
has average accuracy 0.7915, while xlm-roberta-large
has average accuracy of 0.7904. If macro F1 scores were to be compared, roberta-base
actually has lower average than xlm-roberta-large
: 0.77852 vs 0.77876 respectively. The same statistical tests were performed with the premise that roberta-base
has greater metrics, and the results are given below.
test | accuracy p-value | macro F1 p-value |
---|---|---|
Wilcoxon | 0.188 | 0.406 |
Mann Whithey | 0.375 | 0.649 |
Student t-test | 0.681 | 0.934 |
With reversed premise (i.e., that xlm-roberta-large
has greater statistics) the Wilcoxon p-value for macro F1 scores for this case reaches 0.656, Mann-Whithey p-value is 0.399, and of course the Student p-value stays the same. It was therefore concluded that performance of the two models are not statistically significantly different from one another.
Use examples
from simpletransformers.classification import ClassificationModel
model_args = {
"num_train_epochs": 6,
"learning_rate": 3e-6,
"train_batch_size": 69}
model = ClassificationModel(
"roberta", "5roop/roberta-base-frenk-hate", use_cuda=True,
args=model_args
)
predictions, logit_output = model.predict(["Build the wall",
"Build the wall of trust"]
)
predictions
### Output:
### array([1, 0])
Citation
If you use the model, please cite the following paper on which the original model is based:
@article{DBLP:journals/corr/abs-1907-11692,
author = {Yinhan Liu and
Myle Ott and
Naman Goyal and
Jingfei Du and
Mandar Joshi and
Danqi Chen and
Omer Levy and
Mike Lewis and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
journal = {CoRR},
volume = {abs/1907.11692},
year = {2019},
url = {http://arxiv.org/abs/1907.11692},
archivePrefix = {arXiv},
eprint = {1907.11692},
timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
and the dataset used for fine-tuning:
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
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