Sloberta Frenk Hate

🚀 Sloberta Frenk Hate - Text Classification Model

This is a text classification model based on EMBEDDIA/sloberta and fine - tuned on the FRENK dataset, which consists 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).

🚀 Quick Start

This text classification model is built upon EMBEDDIA/sloberta and fine - tuned on the FRENK dataset. It focuses on classifying text related to LGBT and migrant hatespeech in Slovenian.

✨ Features

• Based on EMBEDDIA/sloberta architecture.
• Fine - tuned on a dataset with relabeled binary classification (offensive or acceptable).
• Compares well with other transformer models and fasttext in terms of accuracy and macro F1 score.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

from simpletransformers.classification import ClassificationModel
model_args = {
        "num_train_epochs": 6,
        "learning_rate": 3e-6,
        "train_batch_size": 69}

model = ClassificationModel(
    "camembert", "5roop/sloberta-frenk-hate", use_cuda=True,
    args=model_args
    
)

predictions, logit_output = model.predict(["Silva, ti si grda in neprijazna", "Naša hiša ima dimnik"])
predictions
### Output:
### array([1, 0])

📚 Documentation

Fine - tuning hyperparameters

Fine - tuning was performed with simpletransformers. Beforehand, a brief hyperparameter optimisation was performed, and the presumed optimal hyperparameters are:

model_args = {
        "num_train_epochs": 14,
        "learning_rate": 1e-5,
        "train_batch_size": 21,
        }

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.

From recorded accuracies and macro F1 scores, p - values were also calculated:

Comparison with crosloengual-bert

Comparison with xlm-roberta-base

🔧 Technical Details

The model is based on EMBEDDIA/sloberta and fine - tuned using simpletransformers. Hyperparameter optimisation was carried out to find the optimal settings for fine - tuning.

📄 License

This project is licensed under the CC BY - SA 4.0 license.

📚 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}
}