语言: "斯洛文尼亚语"

许可证: "cc-by-sa-4.0"

标签:

• 文本分类
• 仇恨言论

示例:

• 文本: "Silva, ti si grda in neprijazna"（西尔瓦，你又丑又不友好）

基于EMBEDDIA/sloberta模型并在FRENK数据集上微调的文本分类模型，该数据集包含针对LGBT群体和移民的仇恨言论。微调仅使用了数据集的斯洛文尼亚语部分，并重新标记为二元分类（冒犯性或可接受性）。

微调超参数

使用simpletransformers进行微调。在此之前进行了简短的超参数优化，预设最优超参数为：

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

性能表现

为进行比较，相同的流程还运行了另外两个Transformer模型和fasttext。记录了6次微调会话的准确率和宏观F1分数，并进行了事后分析。

根据记录的准确率和宏观F1分数计算了p值：

与crosloengual-bert比较：

与xlm-roberta-base比较：

使用示例

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
### 输出:
### array([1, 0])

引用

如果使用该模型，请引用原始模型所基于的论文：

@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}
}

以及用于微调的数据集：

@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}
}