Keras-TextClassification提供了多种经典的模型架构供选择,如卷积神经网络(CNN)、循环神经网络(RNN)和长短时记忆网络(LSTM)等。这些模型广泛应用于文本分类领域,并在许多任务中取得了优异的表现。选择适合的模型结构后,我们可以使用Keras-TextClassification提供的接口进行模型的搭建和训练。同时,Keras-TextClassification还支...
history = model.fit(x_train, y_train, batch_size=128, epochs=30, validation_data=(x_val, y_val), callbacks=[early_stopping]) #评估模型在验证集上的表现 y_pred = np.argmax(model.predict(x_val), axis=-1) print(classification_report(y_val, y_pred)) # 使用模型对验证集进行预测 y_...
Text Classification 基于Keras的15种模型:TextCNN, TextRNN, TextDPCNN, TextRCNN, TextHAN, TextBert等及其变种 支持5类特征及其组合:word-level, char-level, 结构化特征(TFIDF, LSA), Context特征(word-left, word-right, char-left, char-right), sentence-level 支持4种分类任务:单标签二分类,单标签多...
Another text classification using CNN (CNN-based text classification with DBpedia data): https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/text_classification_cnn.py A TensorFlow Tutorial: Email Classification(Feb 1, 2016 byJosh Meyer) It contains sample code for feeding ...
https://github.com/JackHCC/Chinese-Text-Classification-PyTorch 作者:JackHCC 链接:https://www.jianshu.com/p/9438fd0fea8c https://www.jianshu.com/p/9438fd0fea8c 中文文本分类,基于pytorch,开箱即用。 神经网络模型:TextCNN,TextRNN,FastText,TextRCNN,BiLSTM_Attention, DPCNN, Transformer ...
Chinese-Text-Classification Github项目地址: https://github.com/JackHCC/Chinese-Text-Classification-PyTorch 作者:JackHCC 中文文本分类,基于pytorch,开箱即用。 神经网络模型:TextCNN,TextRNN,FastText,TextRCNN,BiLSTM_Attention, DPCNN, Transformer 预训练模型:Bert,ERNIE ...
Another text classification using CNN (CNN-based text classification with DBpedia data): https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/text_classification_cnn.py A TensorFlow Tutorial: Email Classification(Feb 1, 2016 byJosh Meyer) ...
以及字符级CNN的论文:Character-level Convolutional Networks for Text Classification 本文是基于TensorFlow在中文数据集上的简化实现,使用了字符级CNN和RNN对中文文本进行分类,达到了较好的效果。 文中所使用的Conv1D与论文中有些不同,详细参考官方文档:tf.nn.conv1d 环境 Python 2/3 (感谢howie.hu调试Python2环境)...
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- 1. 进入keras_textclassification/m01_FastText目录, - 2. 训练: 运行 train.py, 例如: python train.py - 3. 预测: 运行 predict.py, 例如: python predict.py - 说明: 默认不带pre train的random embedding,训练和验证语料只有100条,完整语料移步下面data查看下载 ...