Bart Base Detox

🚀 排毒模型（bart-base-detox）

這是一個用於文本排毒任務的模型，基於BART基礎模型在並行排毒數據集ParaDetox上訓練，在排毒任務中取得了SOTA效果。

🚀 快速開始

本模型是在論文 "ParaDetox: Detoxification with Parallel Data" 中提出的。它基於 BART (base) 模型，在並行排毒數據集ParaDetox上進行訓練，在排毒任務中達到了當前最優結果。更多詳細信息、代碼和數據可在 此處 找到。

📦 安裝指南

文檔未提及具體安裝步驟，跳過此章節。

💻 使用示例

基礎用法

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

input_ids = tokenizer.encode('This is completely idiotic!', return_tensors='pt')
output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
# This is unwise!

📚 詳細文檔

模型信息

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

此模型採用OpenRAIL++許可證，該許可證支持開發服務於公共利益的各種技術，包括工業和學術領域。