self.test_text = x_test self.test_label = y_test self.tokenizer = tokenizer self.batch_size = batch_size self.max_token_len = max_token_len def setup(self): self.train_dataset = QTagDataset(quest=self.tr_text, tags=self.tr_label,tokenizer=self.tokenizer,max_len= self.max_token_...
Multi-class Text Classification using BERT-based Active Learning.Sumanth PrabhuMoosa MohamedHemant Misra
BERT KDD-2020-Taming Pretrained Transformers for Extreme Multi-label Text Classification BERT NeurIPS-2021-Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification (Amazon) BERT arxiv-2022-Exploiting Local and Global Features in Transformer-based Extreme Multi-label Text C...
基于BERT的文本多标签多分类. Contribute to nibuhao/TextMultiLabelClassification development by creating an account on GitHub.
[1] BERT for Sequence-to-Sequence Multi-Label Text Classification [2] SGM模型讲解,参考博客:【多标签文本分类】SGM: Sequence Generation Model for Multi-Label Classification [3] Bert模型讲解,参考博客:【文本分类】BERT: Pre-training of Deep Bidirectional Transformer...
X-BERT: eXtreme Multi-label Text Classification with BERT Wei-Cheng Chang, Hsiang-Fu Yu, Kai Zhong, Yiming Yang, Inderjit Dhillon Preprint 2019 Installation Requirements conda python=3.6 cuda=9.0 Pytorch=0.4.1 pytorch-pretrained-BERT=0.6.2 ...
【BERT多标签文本分类】《Multi-label Text Classification using BERT – The Mighty Transformer》by Kaushal Trivedi http://t.cn/Ecxivbu pdf:http://t.cn/Ecxivb3
简介:【多标签文本分类】BERT for Sequence-to-Sequence Multi-Label Text Classification ·阅读摘要: 本文在已有的SGM和BERT模型上改进,提出了SGM+BERT模型、混合模型。实验证明SGM+BERT模型收敛比BERT快很多,混合模型的效果最好。 ·参考文献: [1] BERT for Sequence-to-Sequence Multi-Label Text Classification ...
https://towardsdatascience.com/building-a-multi-label-text-classifier-using-bert-and-tensorflow-f188e0ecdc5d 对于天气特征, 如果是多分类(multiclass), 天气可能是 晴天 阴天 雨雪 等之一, 天气预报只负责较粗略的特征。 对于详细的天气特征, 例如 有没有太阳、 有没有云、 有没有月亮, 则可以是其中之一...
Text Classification Multi-Label: 多标签文本分类 一、简介 1. 多元分类 多分类任务中一条数据只有一个标签,但这个标签可能有多种类别。比如判定某个人的性别,只能归类为"男性"、"女性"其中一个。再比如判断一个文本的情感只能归类为"正面"、"中面"或者"负面"其中一个。