Roberta Toxicity Classifier

🚀 毒性分類模型

本模型專為毒性分類任務而訓練。訓練所用的數據集是由 Jigsaw 提供的三個數據集的英文部分合並而成（Jigsaw 2018、Jigsaw 2019、Jigsaw 2020），包含約 200 萬個示例。我們將其分為兩部分，並在其上微調了一個 RoBERTa 模型（RoBERTa: A Robustly Optimized BERT Pretraining Approach）。該分類器在第一個 Jigsaw 競賽的測試集上表現出色，AUC-ROC 達到 0.98，F1 分數達到 0.76。

🚀 快速開始

模型信息

如何使用

import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification

tokenizer = RobertaTokenizer.from_pretrained('s-nlp/roberta_toxicity_classifier')
model = RobertaForSequenceClassification.from_pretrained('s-nlp/roberta_toxicity_classifier')

batch = tokenizer.encode("You are amazing!", return_tensors="pt")

output = model(batch)
# idx 0 for neutral, idx 1 for toxic

📚 詳細文檔

引用信息

若要引用我們的工作，請使用以下引用信息：

@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.",
}

許可證信息

本模型採用 OpenRAIL++ 許可證，該許可證支持開發各種服務於公眾利益的技術，包括工業和學術領域。