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--- |
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language: "hr" |
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license: "cc-by-sa-4.0" |
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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: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'." |
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--- |
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# bcms-bertic-frenk-hate |
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Text classification model based on [`classla/bcms-bertic`](https://huggingface.co/classla/bcms-bertic) and fine-tuned on the [FRENK dataset](https://www.clarin.si/repository/xmlui/handle/11356/1433) comprising of LGBT and migrant hatespeech. Only the Croatian 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": 12, |
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"learning_rate": 1e-5, |
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"train_batch_size": 74} |
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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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| bcms-bertic-frenk-hate | 0.8313 | 0.8219 | |
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| EMBEDDIA/crosloengual-bert | 0.8054 | 0.796 | |
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| xlm-roberta-base | 0.7175 | 0.7049 | |
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| fasttext | 0.771 | 0.754 | |
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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 | 0.00108 | 0.00108 | |
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| Student t-test | 2.43e-10 | 1.27e-10 | |
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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 | 0.00107 | 0.00108 | |
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| Student t-test | 4.83e-11 | 5.61e-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 = ClassificationModel( |
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"bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True, |
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) |
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predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala', |
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"Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "]) |
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predictions |
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### Output: |
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### array([0, 0]) |
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``` |
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## Citation |
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If you use the model, please cite the following paper on which the original model is based: |
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``` |
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@inproceedings{ljubesic-lauc-2021-bertic, |
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title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian", |
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author = "Ljube{\v{s}}i{\'c}, Nikola and Lauc, Davor", |
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booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing", |
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month = apr, |
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year = "2021", |
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address = "Kiyv, Ukraine", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5", |
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pages = "37--42", |
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} |
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``` |
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and the dataset used for fine-tuning: |
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``` |
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@misc{ljubešić2019frenk, |
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title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English}, |
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author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec}, |
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year={2019}, |
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eprint={1906.02045}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/1906.02045} |
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} |
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``` |
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