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--- |
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language: "sl" |
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tags: |
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- text-classification |
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- hate-speech |
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widget: |
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- text: "Silva, ti si grda in neprijazna" |
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--- |
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Text classification model based on `EMBEDDIA/sloberta` and fine-tuned on the [FRANK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the slovenian subset of the data was used for fine-tuning and the dataset has been relabeled for binary classification (offensive or acceptable). |
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## Fine-tuning hyperparameters |
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Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are: |
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```python |
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model_args = { |
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"num_train_epochs": 14, |
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"learning_rate": 1e-5, |
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"train_batch_size": 21, |
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} |
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``` |
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## Performance |
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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. |
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| model | average accuracy | average macro F1| |
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|---|---|---| |
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|sloberta-frenk-hate|0.7785|0.7764| |
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|EMBEDDIA/crosloengual-bert |0.7616|0.7585| |
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|xlm-roberta-base |0.686|0.6827| |
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|fasttext|0.709 |0.701 | |
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From recorded accuracies and macro F1 scores p-values were also calculated: |
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Comparison with `crosloengual-bert`: |
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| test | accuracy p-value | macro F1 p-value| |
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| --- | --- | --- | |
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|Wilcoxon|0.00781|0.00781| |
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|Mann Whithney U test|0.00163|0.00108| |
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|Student t-test |0.000101|3.95e-05| |
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Comparison with `xlm-roberta-base`: |
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| test | accuracy p-value | macro F1 p-value| |
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| --- | --- | --- | |
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|Wilcoxon|0.00781|0.00781| |
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|Mann Whithney U test|0.00108|0.00108| |
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|Student t-test |9.46e-11|6.94e-11| |
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## Use examples |
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```python |
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from simpletransformers.classification import ClassificationModel |
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model_args = { |
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"num_train_epochs": 6, |
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"learning_rate": 3e-6, |
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"train_batch_size": 69} |
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model = ClassificationModel( |
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"camembert", "5roop/sloberta-frenk-hate", use_cuda=True, |
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args=model_args |
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) |
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predictions, logit_output = model.predict(["Silva, ti si grda in neprijazna", "Naša hiša ima dimnik"]) |
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predictions |
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### Output: |
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### array([1, 0]) |
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``` |
