readme: ---
  
  language: "en"
  
  license: "cc-by-sa-4.0"
  
  tags:
  - text-classification
  - hate-speech
  
  widget:
  - text: "Gay is okay."
  ---
  
  
  
  
  # roberta-base-frenk-hate
  
  Text classification model based on [`roberta-base`](https://huggingface.co/roberta-base) and fine-tuned on the [FRENK 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).
  
  ## 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": 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
  
  ```python
  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}
  }
  ```
  
  
  