pythonnlpdata-sciencemachine-learningawesomecomputer-visiondeep-learningartificial-intelligencenlp-projectsmachine-learning-projectsartificial-intelligence-projectscomputer-vision-projectdeep-learning-project UpdatedJul 26, 2024 Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities ...
Jcseg is a light weight NLP framework developed with Java. Provide CJK and English segmentation based on MMSEG algorithm, With also keywords extraction, key sentence extraction, summary extraction implemented based on TEXTRANK algorithm. Jcseg had a build-in http server and search modules for lucene...
论文链接：https://arxiv.org/abs/2012.15688 GitHub链接：https://github.com/PaddlePaddle/ERNIE/tree/repro/ernie-doc Transformer 是预训练模型所依赖的主流网络结构，但由于其计算量和空间消耗随建模长度呈平方级增加，导致模型难以建模篇章、书籍等长文本内容。受到人类先粗读后精读的阅读方式启发，本文提出了回顾...
Zero-based index of the word; first word has index 0, second word has index 1 and so on StartPosition StartPosition integer Zero-based character offset at which the word begins in the input string EndPosition EndPosition integer Zero-based character offset at which the word ends in th...
为此,Bahdanau等人在2015年提出了Attenion机制,Attention翻译成为中文叫做注意力,把这种模型称为Attention based model。就像我们自己看到一副画,我们能够很快的说出画的主要内容,而忽略画中的背景,因为我们注意的,更关注的往往是其中的主要内容。 通过这种方式,在我们的RNN中,我们有通过LSTM或者是GRU得到的所有信息,那么...
espnet: End-to-End Speech Processing Toolkit espnet.github.io/espnet pythia: A software suite for Visual Question Answering UnsupervisedMT: Phrase-Based & Neural Unsupervised Machine Translation. jiant: The jiant sentence representation learning toolkit. ...
第二,对比两种模式的任务效果,第一种模式是用较大的领域专用数据进行Fine-tuning,第二种是few-shot prompting或instruct-based方法。如果第二种方法效果达到或超过第一种方法,则意味着这个领域没有继续独立存在的必要性。如果用这个标准来看,其实很多研究领域,目前fine-tuning效果还是占优的(因为这种模式领域训练数据量...
espnet: End-to-End Speech Processing Toolkit espnet.github.io/espnet pythia: A software suite for Visual Question Answering UnsupervisedMT: Phrase-Based & Neural Unsupervised Machine Translation. jiant: The jiant sentence representation learning toolkit. ...
GitHub链接: https://github.com/PaddlePaddle/Knover/tree/develop/projects/PLATO-2 近期一些端到端的对话生成模型,通过更大的模型规模、更多的训练语料,获得了更加优秀的对话生成能力。PLATO-2 承袭了 PLATO 隐变量进行回复多样化生成的特性,模型参数规模上升到了 16 亿。考虑到精细化的引入隐变量的网络训练,计算消...
4.Kaggle 数据集：Find Open Datasets and Machine Learning Projects | Kaggle爱竞赛的盆友们应该很熟悉...