模型概述

这是 "ParaDetox: Detoxification with Parallel Data" 论文中提出的模型。

该模型本身是在平行排毒数据集 ParaDetox 上训练得到的 BART (base) 模型，为排毒任务实现了SOTA结果。有关更多详细信息、代码和数据，请参见 here 。

如何使用

from transformers import BartForConditionalGeneration, AutoTokenizer
base_model_name = 'facebook/bart-base'
model_name = 'SkolkovoInstitute/bart-base-detox'
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)

引用

@inproceedings{logacheva-etal-2022-paradetox,
    title = "{P}ara{D}etox: Detoxification with Parallel Data",
    author = "Logacheva, Varvara  and
      Dementieva, Daryna  and
      Ustyantsev, Sergey  and
      Moskovskiy, Daniil  and
      Dale, David  and
      Krotova, Irina  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.acl-long.469",
    pages = "6804--6818",
    abstract = "We present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this pipeline can be used to distill a large existing corpus of paraphrases to get toxic-neutral sentence pairs. We release two parallel corpora which can be used for the training of detoxification models. To the best of our knowledge, these are the first parallel datasets for this task.We describe our pipeline in detail to make it fast to set up for a new language or domain, thus contributing to faster and easier development of new parallel resources.We train several detoxification models on the collected data and compare them with several baselines and state-of-the-art unsupervised approaches. We conduct both automatic and manual evaluations. All models trained on parallel data outperform the state-of-the-art unsupervised models by a large margin. This suggests that our novel datasets can boost the performance of detoxification systems.",
}

许可信息

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License 。