readme: ---
  language: "hr"
  
  license: "cc-by-sa-4.0"
  
  tags:
  - text-classification
  - hate-speech
  
  widget:
  - text: "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni'."
  ---
  # bcms-bertic-frenk-hate
  
  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).
  
  
  
  
  ## Fine-tuning hyperparameters
  
  Fine-tuning was performed with `simpletransformers`. Beforehand a brief hyperparameter optimisation was performed and the presumed optimal hyperparameters are:
  
  ```python
  
  model_args = {
          "num_train_epochs": 12,
          "learning_rate": 1e-5,
          "train_batch_size": 74}
  ```
  
  ## 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 |
  |----------------------------|------------------|------------------|
  | bcms-bertic-frenk-hate     | 0.8313           | 0.8219           |
  | EMBEDDIA/crosloengual-bert | 0.8054           | 0.796            |
  | xlm-roberta-base           | 0.7175           | 0.7049           |
  | fasttext                   | 0.771            | 0.754            |
  
  
  
  From recorded accuracies and macro F1 scores p-values were also calculated:
  
  Comparison with `crosloengual-bert`:
  
  | test           | accuracy p-value | macro F1 p-value |
  |----------------|------------------|------------------|
  | Wilcoxon       | 0.00781          | 0.00781          |
  | Mann Whithney  | 0.00108          | 0.00108          |
  | Student t-test | 2.43e-10         | 1.27e-10         |
  
  Comparison with `xlm-roberta-base`:
  
  | test           | accuracy p-value | macro F1 p-value |
  |----------------|------------------|------------------|
  | Wilcoxon       | 0.00781          | 0.00781          |
  | Mann Whithney  | 0.00107          | 0.00108          |
  | Student t-test | 4.83e-11         | 5.61e-11         |
  
  
  
  ## Use examples
  
  ```python
  from simpletransformers.classification import ClassificationModel
  
  model = ClassificationModel(
      "bert", "5roop/bcms-bertic-frenk-hate", use_cuda=True,
  
      
  )
  
  predictions, logit_output = model.predict(['Ne odbacujem da će RH primiti još migranata iz Afganistana, no neće biti novog vala',
                                             "Potpredsjednik Vlade i ministar branitelja Tomo Medved komentirao je Vladine planove za zakonsku zabranu pozdrava 'za dom spremni' "])
  predictions
  ### Output:
  ### array([0, 0])
  ```
  
  ## Citation
  
  If you use the model, please cite the following paper on which the original model is based:
  ```
  @inproceedings{ljubesic-lauc-2021-bertic,
      title = "{BERT}i{\'c} - The Transformer Language Model for {B}osnian, {C}roatian, {M}ontenegrin and {S}erbian",
      author = "Ljube{\v{s}}i{\'c}, Nikola  and Lauc, Davor",
      booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
      month = apr,
      year = "2021",
      address = "Kiyv, Ukraine",
      publisher = "Association for Computational Linguistics",
      url = "https://www.aclweb.org/anthology/2021.bsnlp-1.5",
      pages = "37--42",
  }
  ```
  
  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}
  }
  ```
  