multitask learning 案例 multitask learning案例 多任务学习（Multitask Learning，MTL）是机器学习的一种方法，旨在通过同时学习多个相关任务来提高模型的性能。以下是一个多任务学习的案例：假设你正在开发一个自然语言处理（NLP）模型，任务是对文本进行分类，以确定文本的情感极性（积极、消极或中性）以及主题分类（...
基于神经网络的多任务学习,尤其是基于深度神经网络的多任务学习(DL based Multitask Learning),适用于解决很多NLP领域的问题,比如把词性标注、句子句法成分划分、命名实体识别、语义角色标注等任务,都可以采用MTL任务来解决。 其他MTL的应用还有,网页图片和语音搜索[Zhou et. al. KDD’11],疾病预测[Zhang et. al. ...
an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model.Our method significantly
The disclosed techniques provide a so-called simultaneous multitask neural network model for solving increasingly complex natural language processing (NLP) tasks using layers that are increasingly deeper in a single end to end model.This model is trained sequentially by applying a so-called sequential...
与Multitask Learning相对的就是Single-task Learning, Single-task Learning就是之前的一个model解决某个任务的模型. 而从CV pre-train model的预训练模型得到启发, NLP当然也可以用预训练模型. 那么我们想要的其实就是一个unified multi-task model, 想到了之前看的一篇sentiment analysis论文[3], 把情感分析用一...
基于神经网络的多任务学习,尤其是基于深度神经网络的多任务学习(DL based Multitask Learning),适用于解决很多NLP领域的问题,比如把词性标注、句子句法成分划分、命名实体识别、语义角色标注等任务,都可以采用MTL任务来解决。 其他MTL的应用还有,网页图片和语音搜索[Zhou et. al. KDD’11],疾病预测[Zhang et. al. ...
Transformer-based classification framework: Recently, Transformer [7] of natural language processing (NLP) models has received a lot of attention in the field of computer vision. Show abstract Multi-task deep learning for medical image computing and analysis: A review 2023, Computers in Biology and...
zjunlp/MolGen Star142 Code Issues Pull requests [ICLR 2024] Domain-Agnostic Molecular Generation with Chemical Feedback moleculepytorchgenerationlanguage-modelselfiesmultitaskpre-trainingpre-trained-modelhuggingfacepre-trained-language-modelsmolecular-generationmolecular-optimizationmolgentargeted-molecular-generation...
基于神经网络的多任务学习,尤其是基于深度神经网络的多任务学习(DL based Multitask Learning),适用于解决很多NLP领域的问题,比如把词性标注、句子句法成分划分、命名实体识别、语义角色标注等任务,都可以采用MTL任务来解决。 其他MTL的应用还有,网页图片和语音搜索[Zhou et. al. KDD’11],疾病预测[Zhang et. al. ...
基于神经网络的多任务学习,尤其是基于深度神经网络的多任务学习(DL based Multitask Learning),适用于解决很多NLP领域的问题,比如把词性标注、句子句法成分划分、命名实体识别、语义角色标注等任务,都可以采用MTL任务来解决。 其他MTL的应用还有,网页图片和语音搜索[Zhou et. al. KDD’11],疾病预测[Zhang et. al. ...