原文引用:"According to the pre-training objectives, PLMs for text generation can be categorized as masked LMs, causal LMs, prefix LMs, and encoder-decoder LMs"3.参数优化问题 问题:如何有效优化模型参数 解决方案: 微调:普通微调、中间微调、多任务微调等 提示学习:离散提示、连续提示 属性调优:相关性、...
Fine tuning bert for text generation. Contribute to voidful/BertGenerate development by creating an account on GitHub.
For an illustration, BERTScore recall can be computed as If you find this repo useful, please cite: @inproceedings{bert-score, title={BERTScore: Evaluating Text Generation with BERT}, author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi}, booktit...
BERT 是建立在NLP领域中涌现的许多聪明想法之上的,包括但不限于半监督学习(Semi-supervised Sequence Learning)、ELMo(Deep contextualized word representations)、ULMFiT(Universal Language Model Fine-tuning for Text Classification)、GPT(Improving Language Understanding by Generative Pre-Training) 和Transformer(Attentio...
bertscore: evaluating text generation with bert BERTScore: Evaluating Text Generation with BERT BERTScore是一个在自然语言处理领域广泛应用的评价指标,它能够对文本生成任务中的表现进行量化衡量。BERT(Bidirectional Encoder Representations from Transformers)作为一种先进的神经网络模型,在自然语言处理领域取得了显著的...
pycorrector: 中文文本纠错工具。支持中文音似、形似、语法错误纠正,python3开发。实现了Kenlm、ConvSeq2Seq、BERT、MacBERT、ELECTRA、ERNIE、Transformer等多种模型的文本纠错,并在SigHAN数据集评估各模型的效果。 1.中文文本纠错任务,常见错误类型: 当然,针对不同业务场景,这些问题并不一定全部存在,比如拼音输入法、语音...
The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. And, the second one remedies the first one by restructuring the BERT employment into a sequential manner for taking information from previous decoded results. Our models are ...
Here's how to run the data generation. The input is a plain text file, with one sentence per line. (It is important that these be actual sentences for the "next sentence prediction" task). Documents are delimited by empty lines. The output is a set oftf.train.Examples serialized intoTF...
尽管预训练的语言模型(如BERT)在许多任务中都有出色的表现,但是它极易受对抗文本的影响,并且中文的文字具有“多义、字形”特性。为此,「今天分享的这篇文章基于中文特性,提出了RoChBERT框架,该框架通过使用更全面的对抗性图,在微调过程中将汉语语音和字形特征融合到预训练的表示中,基于Bert模型构建了更鲁棒的模型」...
outputs = model(**text_tokens)defget_errors(corrected_text, origin_text): sub_details = []fori, ori_charinenumerate(origin_text):ifori_charin[' ','“','”','‘','’','\n','…','—','擤']:# add unk wordcorrected_text = corrected_text[:i] + ori_char + corrected_text[i:...