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# roberta-base-frenk-hate |
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Text classification model based on `roberta-base` and fine-tuned on the [FRANK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) 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). |
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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": 6, |
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"learning_rate": 3e-6, |
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"train_batch_size": 69} |
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``` |
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## Performance |
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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. |
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| model | average accuracy | average macro F1| |
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|---|---|---| |
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|roberta-base-frenk-hate|0.7915|0.7785| |
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|xlm-roberta-large |0.7904|0.77876| |
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|xlm-roberta-base |0.7577|0.7402| |
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|distilbert-base-uncased-finetuned-sst-2-english|0.7201|0.69862| |
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From recorded accuracies and macro F1 scores p-values were also calculated: |
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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 | 1.35e-08 | 1.05e-07| |
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Comparison with `distilbert-base-uncased-finetuned-sst-2-english`: |
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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 | 1.33e-12 | 3.03e-12| |
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Comparison with `xlm-roberta-large` yielded inconclusive results; whereas accuracy was outperformed by this model, the macro F1 score was not. Neither metric allowed for statistically significant conclusions about which model might be better. |
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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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"roberta", "5roop/roberta-base-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(["Build the wall", |
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"Build the wall of trust"] |
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) |
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predictions |
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### Output: |
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### array([1, 0]) |
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``` |