├── 1 ├── numpy_base.ipynb ├── pic │ ├── ngram_2-analyzer_char-lr_1.0-batchsize_32-epoch_number_20-test_rate_0.2.jpg │ ├── ngram_2-analyzer_word-lr_1.0-batchsize_128-epoch_number_20-test_rate_0.2.jpg │ ├── ngram_2-analyzer_word-lr_1.0-batchsize_32-epoch_number_10-test_rate_0.2.jpg │ ├── ngram_2-analyzer_word-lr_1.0-batchsize_32-epoch_number_20-test_rate_0.2.jpg │ ├── ngram_2-analyzer_word-lr_1.0-batchsize_8-epoch_number_20-test_rate_0.2.jpg │ ├── ngram_2-analyzer_word-lr_10.0-batchsize_32-epoch_number_20-test_rate_0.2.jpg │ └── ngram_3-analyzer_char-lr_1.0-batchsize_32-epoch_number_20-test_rate_0.2.jpg ├── report │ ├── 利用逻辑回归进行文本分类.md │ ├── 文本分类基本概念（教程）.md │ └── 运用sklearn进行文本分类.md ├── sklearn_base.ipynb ├── submission.csv └── 文本分类基本概念（教程）.md ├── 2 ├── LSTM_classify.ipynb ├── paper │ └── TEXTCNN.pdf ├── pic │ ├── lstm-glove-acc-50-0.4-init.jpg │ ├── lstm-glove-acc-50-0.5.jpg │ ├── lstm-glove-acc-50-0.8.jpg │ ├── lstm-glove-loss-20.jpg │ ├── lstm-glove-loss-50-0.4-init.jpg │ ├── lstm-glove-loss-50-0.5.jpg │ ├── lstm-glove-loss-50-0.8.jpg │ ├── lstmmax-glove-acc-50.jpg │ ├── lstmmax-glove-loss-50.jpg │ ├── textCNN-random-acc-50.jpg │ └── textCNN-random-loss-50.jpg ├── refercode │ ├── lstm_based2.ipynb │ └── textCNN_eample.ipynb ├── report │ ├── Lstm从理论到实践.md │ ├── TextCNN从理论到实践.md │ ├── TorchText使用指南.md │ └── 基于深度学习的文本分类.md ├── submission.csv └── textCNN_classify.ipynb ├── 3 ├── code │ ├── Attention-LSTM.ipynb │ ├── D2L-AddAttention.ipynb │ └── ESIM.ipynb ├── note │ └── 理解注意力机制.md ├── paper │ ├── A Decomposable Attention Model for Natural Language Inferenc.pdf │ ├── Enhanced LSTM for Natural Language Inference.pdf │ └── Reasoning about Entailment with Neural Attention.pdf ├── pic │ ├── ESMI-acc-5.jpg │ └── ESMI-loss-5.jpg └── 基于注意力机制的文本匹配.md ├── 4 ├── CRF理解与实践.md ├── code │ ├── BiLSTM_CRF.ipynb │ ├── BiLSTM_CRF_BATCH.py │ ├── BiLSTM_CRF_MODEL.py │ ├── pytorch_copy.ipynb │ └── pytorch_tutorial.ipynb ├── paper │ ├── Compositional Character Models forOpen Vocabulary Word Representation.pdf │ ├── End-to-end Sequence Labeling.pdf │ └── Neural Architectures for Named Entity Recognitio.pdf ├── pic │ ├── 1.drawio │ ├── BiLSTM_CRF-F1-20.jpg │ ├── BiLSTM_CRF-F1-5.jpg │ ├── BiLSTM_CRF-loss-20.jpg │ ├── BiLSTM_CRF-loss-5.jpg │ ├── BiLSTM_CRF-precision-20.jpg │ ├── BiLSTM_CRF-precision-5.jpg │ ├── BiLSTM_CRF-recall-20.jpg │ └── BiLSTM_CRF-recall-5.jpg └── 基于LSTM&CRF的序列标注.md ├── 5 ├── code │ └── GenerateModel.ipynb ├── pic │ ├── GRU-PP-100.jpg │ ├── GRU-acc-100.jpg │ ├── GRU-loss-100.jpg │ ├── lr4e4 │ │ ├── GRU-PP-50.jpg │ │ ├── GRU-acc-50.jpg │ │ ├── GRU-loss-50.jpg │ │ ├── LSTM-PP-50.jpg │ │ ├── LSTM-acc-50.jpg │ │ └── LSTM-loss-50.jpg │ └── lr5e5 │ │ ├── GRU-PP-100.jpg │ │ ├── GRU-acc-100.jpg │ │ ├── GRU-loss-100.jpg │ │ ├── LSTM-PP-100.jpg │ │ ├── LSTM-acc-100.jpg │ │ └── LSTM-loss-100.jpg └── 基于神经网络的语言模型.md ├── .gitignore ├── README.md └── 任务要求简化版.md /.gitignore: -------------------------------------------------------------------------------- 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