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规范代码的Bert系列用于序列标注
包括CNN,RNN以及attention在内的对文本分类
通过Bert结合BiLSTM-CRF进行序列标注任务
fine-tuning完Bert之后进行预测
bert模型用于文本分类
自己对Bert训练源码的改编,主要是为了方便随时获得验证集的LOSS值
把bert固化成pb文件以及bert批量预测
利用双向的LSTM+CRF实现词性标注
pytorch版bilstm_crf进行NER
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自己写的计算宏观微观precision、recall、f1-score
用过CNN计算短文本的相似性
基于chatglm进行sft
对改进kmeans以及层次聚类、密度聚类的使用对比
不用look_embedding层实现CNN对新闻文本进行分类,注意输入向量的pkl文件较大没法上传每次自己生成便可
语义相似度损失值的计算
用CRF++抽取特殊领域的专有名词
对语料的预处理使其符合CRF++的数据格式以及对CRF++输出文件的处理
用于释义识别的交叉Attention
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
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ELMO的预训练以及结合具体任务进行分类
常用文本分类工具fasttext的使用
GlobalPointer_lstm进行NER
添加了注释的,可跑通的,gpt2模型的训练代码
一个回归预测类比赛,对发电量进行预测
Chinese-LLaMA 、Chinese-Falcon 基础模型;ChatFlow中文对话模型;中文OpenLLaMA模型;NLP预训练/指令微调数据集
利用单层的LSTM实现文本分类
Models and examples built with TensorFlow
双向LSTM+CRF用于序列标注的规范化代码
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Open source projects and samples from Microsoft.
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