├── .gitignore ├── LICENSE ├── README.md ├── abstractive-summarization ├── 1.lstm-seq2seq-greedy.ipynb ├── 10.bert-dilated-fair.ipynb ├── 11.self-attention-pointer-generator.ipynb ├── 12.dilated-fairseq-pointer-generator.ipynb ├── 2.lstm-seq2seq-greedy-luong.ipynb ├── 3.lstm-seq2seq-beam.ipynb ├── 4.lstm-birnn-seq2seq-beam-luong.ipynb ├── 5.xueyouluo-pointer-generator-bahdanau.ipynb ├── 6.copynet.ipynb ├── 7.xueyouluo-pointer-generator-luong.ipynb ├── 8.dilated-seq2seq.ipynb ├── 9.dilated-seq2seq-self-attention.ipynb ├── README.md ├── dataset.tar.gz ├── dataset │ ├── ctexts.json │ └── headlines.json └── pointer_generator_helper.py ├── attention ├── 1.bahdanau.ipynb ├── 2.luong.ipynb ├── 3.hierarchical.ipynb ├── 4.additive.ipynb ├── 5.soft.ipynb ├── 6.attention-over-attention.ipynb ├── 7.bahdanau-api.ipynb └── 8.luong-api.ipynb ├── chatbot ├── 1.basic-seq2seq-manual.ipynb ├── 10.basic-birnn-seq2seq-greedy.ipynb ├── 11.lstm-birnn-seq2seq-greedy.ipynb ├── 12.gru-birnn-seq2seq-greedy.ipynb ├── 13.basic-seq2seq-luong.ipynb ├── 14.lstm-seq2seq-luong.ipynb ├── 15.gru-seq2seq-luong.ipynb ├── 16.basic-seq2seq-bahdanau.ipynb ├── 17.lstm-seq2seq-bahdanau.ipynb ├── 18.gru-seq2seq-bahdanau.ipynb ├── 19.lstm-birnn-seq2seq-luong.ipynb ├── 2.lstm-seq2seq-manual.ipynb ├── 20.gru-birnn-seq2seq-luong.ipynb ├── 21.lstm-birnn-seq2seq-bahdanau.ipynb ├── 22.gru-birnn-seq2seq-bahdanau.ipynb ├── 23.lstm-birnn-seq2seq-bahdanau-luong.ipynb ├── 24.gru-birnn-seq2seq-bahdanau-luong.ipynb ├── 25.lstm-seq2seq-greedy-luong.ipynb ├── 26.gru-seq2seq-greedy-luong.ipynb ├── 27.lstm-seq2seq-greedy-bahdanau.ipynb ├── 28.gru-seq2seq-greedy-bahdanau.ipynb ├── 29.lstm-seq2seq-beam.ipynb ├── 3.gru-seq2seq-manual.ipynb ├── 30.gru-seq2seq-beam.ipynb ├── 31.lstm-birnn-seq2seq-beam-luong.ipynb ├── 32.gru-birnn-seq2seq-beam-luong.ipynb ├── 33.lstm-birnn-seq2seq-luong-bahdanau-stack-beam.ipynb ├── 34.gru-birnn-seq2seq-luong-bahdanau-stack-beam.ipynb ├── 35.byte-net.ipynb ├── 35.byte-net.py ├── 36.estimator.ipynb ├── 37.capsule-lstm-seq2seq-greedy.ipynb ├── 37.capsule-lstm-seq2seq-greedy.py ├── 38.capsule-lstm-seq2seq-luong-beam.ipynb ├── 38.capsule-lstm-seq2seq-luong-beam.py ├── 39.lstm-birnn-seq2seq-luong-bahdanau-stack-beam-dropout-l2.ipynb ├── 4.basic-seq2seq-api-greedy.ipynb ├── 40.dnc-seq2seq-bahdanau-greedy.ipynb ├── 41.lstm-birnn-seq2seq-beam-luongmonotic.ipynb ├── 42.lstm-birnn-seq2seq-beam-bahdanaumonotic.ipynb ├── 43.memory-network-basic.ipynb ├── 44.memory-network-lstm.ipynb ├── 45.attention-is-all-you-need.ipynb ├── 46.transformer-xl.ipynb ├── 47.attention-is-all-you-need-beam-search.ipynb ├── 48.transformer-xl-lstm.ipynb ├── 49.gpt-2-lstm.ipynb ├── 5.lstm-seq2seq-api-greedy.ipynb ├── 50.conv-encoder-conv-decoder.ipynb ├── 51.conv-encoder-lstm.ipynb ├── 52.tacotron-greedy.ipynb ├── 53.tacotron-beam.ipynb ├── 54.google-nmt.ipynb ├── 6.gru-seq2seq-greedy.ipynb ├── 7.basic-birnn-seq2seq-manual.ipynb ├── 8.lstm-birnn-seq2seq-manual.ipynb ├── 9.gru-birnn-seq2seq-manual.ipynb ├── README.md ├── access.py ├── addressing.py ├── dataset.tar.gz ├── dnc.py ├── gpt_2.py └── util.py ├── classification-comparison ├── Deep-learning │ ├── LNLSTM-vector.ipynb │ ├── bidirectional-rnn-vector.ipynb │ ├── cnn-rnn-vector.ipynb │ ├── cnn-vector.ipynb │ ├── feedforward-vector.ipynb │ ├── kmax-conv-vector.ipynb │ ├── multihead-attention.ipynb │ ├── ntm-vector.ipynb │ ├── only-attention-vector.ipynb │ ├── rnn-attention-vector.ipynb │ ├── rnn-timestamp.ipynb │ ├── rnn-vector-hinge.ipynb │ ├── rnn-vector-huber.ipynb │ ├── rnn-vector-stack.ipynb │ ├── rnn-vector.ipynb │ ├── self-optimized-feedforward-timestamp.ipynb │ ├── seq2seq-vector-stable.ipynb │ └── seq2seq-vector.ipynb ├── Ensemble │ ├── featuring-ensemble.ipynb │ └── oracle.ipynb ├── LGB │ ├── lgb-tfidf-svd50.ipynb │ ├── lgb-tfidf.ipynb │ ├── lgb-timestamp.ipynb │ └── nce-vector-lgb.ipynb ├── NB-SVM │ └── NB-SVM.ipynb ├── Naive-Bayes │ └── Bayes classifier.ipynb ├── README.md ├── SVM │ └── SVM.ipynb ├── XGB │ ├── xgb-bow.ipynb │ ├── xgb-tfidf-svd50.ipynb │ ├── xgb-tfidf.ipynb │ ├── xgb-timestamp-avg.ipynb │ └── xgb-timestamp50.ipynb └── preparation │ ├── dictionary_emotion.p │ ├── prepare-dataset.ipynb │ ├── prepare-vocab.ipynb │ └── word-vector.ipynb ├── dependency-parser ├── 1.lstm-birnn-crf-biaffine.ipynb ├── 2.lstm-birnn-bahdanau-crf-biaffine.ipynb ├── 3.lstm-birnn-luong-crf-biaffine.ipynb ├── 4.bert-crf-biaffine.ipynb ├── 5.biaffine-attention-cross-entropy.ipynb ├── 6.bert-biaffine-attention-cross-entropy.ipynb ├── 7.stackpointer.ipynb ├── 8.xlnet-biaffine-attention-cross-entropy.ipynb └── README.md ├── entity-tagging ├── 1.rnn-lstm-crf.ipynb ├── 2.rnn-lstm-crf-luong.ipynb ├── 3.rnn-lstm-crf-bahdanau.ipynb ├── 4.rnn-lstm-crf-bahdanau-ngrams.ipynb ├── 5.rnn-lstm-crf-luong-ngrams.ipynb ├── 6.cnn-residual-bahdanau-ngrams.ipynb ├── 7.attention-is-all-you-need.ipynb ├── 8.bert.ipynb ├── 9.xlnet-base.ipynb └── README.md ├── extractive-summarization ├── 1.rnn-lstm.ipynb ├── 2.dilated-cnn.ipynb ├── 3.multihead-attention.ipynb ├── 4.bert-base.ipynb ├── download-data.ipynb ├── modeling.py ├── preprocessing-data-bert.ipynb └── preprocessing-data.ipynb ├── generator ├── 1.char-generator-lstm.ipynb ├── 10.gru-seq2seq-beam-word.ipynb ├── 11.gru-seq2seq-bahdanau-greedy-char.ipynb ├── 12.gru-seq2seq-bahdanau-greedy-word.ipynb ├── 13.dilated-cnn-beam.ipynb ├── 14.transformer-beam.ipynb ├── 15.transformer-xl-beam.ipynb ├── 2.char-rnn-beam.ipynb ├── 3.char-generator-lstm-embedding.ipynb ├── 4.word-generator-lstm.ipynb ├── 5.word-generator-lstm-embedding.ipynb ├── 6.gru-seq2seq-greedy-char.ipynb ├── 7.gru-seq2seq-greedy-word.ipynb ├── 8.char-generator-lstm-bahdanau.ipynb ├── 9.char-generator-lstm-luong.ipynb ├── README.md └── shakespeare.txt ├── language-detection ├── 1.fast-text-ngrams.ipynb └── README.md ├── neural-machine-translation ├── 1.basic-seq2seq.ipynb ├── 10.basic-birnn-seq2seq-contrib-greedy.ipynb ├── 11.lstm-birnn-seq2seq-contrib-greedy.ipynb ├── 12.gru-birnn-seq2seq-contrib-greedy.ipynb ├── 13.basic-seq2seq-luong.ipynb ├── 14.lstm-seq2seq-luong.ipynb ├── 16.basic-seq2seq-bahdanau.ipynb ├── 17.lstm-seq2seq-bahdanau.ipynb ├── 18.gru-seq2seq-bahdanau.ipynb ├── 19.basic-birnn-seq2seq-bahdanau.ipynb ├── 2.lstm-seq2seq.ipynb ├── 20.lstm-birnn-seq2seq-bahdanau.ipynb ├── 21.gru-birnn-seq2seq-bahdanau.ipynb ├── 22.basic-birnn-seq2seq-luong.ipynb ├── 23.lstm-birnn-seq2seq-luong.ipynb ├── 24.gru-birnn-seq2seq-luong.ipynb ├── 25.lstm-seq2seq-contrib-greedy-luong.ipynb ├── 26.gru-seq2seq-contrib-greedy-luong.ipynb ├── 27.lstm-seq2seq-contrib-greedy-bahdanau.ipynb ├── 28.gru-seq2seq-contrib-greedy-bahdanau.ipynb ├── 29.lstm-seq2seq-contrib-beam-bahdanau.ipynb ├── 3.gru-seq2seq.ipynb ├── 30.gru-seq2seq-contrib-beam-bahdanau.ipynb ├── 31.lstm-birnn-seq2seq-contrib-beam-luong.ipynb ├── 32.gru-birnn-seq2seq-contrib-beam-luong.ipynb ├── 33.lstm-birnn-seq2seq-contrib-luong-bahdanau-beam.ipynb ├── 34.gru-birnn-seq2seq-contrib-luong-bahdanau-beam.ipynb ├── 35.bytenet-greedy.ipynb ├── 36.capsule-lstm-seq2seq-contrib-greedy.ipynb ├── 37.capsule-gru-seq2seq-contrib-greedy.ipynb ├── 38.dnc-seq2seq-bahdanau-greedy.ipynb ├── 39.dnc-seq2seq-luong-greedy.ipynb ├── 4.basic-seq2seq-contrib-greedy.ipynb ├── 40.lstm-birnn-seq2seq-beam-luongmonotic.ipynb ├── 41.lstm-birnn-seq2seq-beam-bahdanaumonotic.ipynb ├── 42.memory-network-lstm-seq2seq-contrib.ipynb ├── 43.attention-is-all-you-need-beam.ipynb ├── 44.conv-seq2seq.ipynb ├── 45.conv-encoder-lstm-decoder.ipynb ├── 46.dilated-conv-seq2seq.ipynb ├── 47.gru-birnn-esidual-seq2seq-greedy.ipynb ├── 47.gru-birnn-seq2seq-greedy-residual.ipynb ├── 48.google-nmt.ipynb ├── 49.bert-multilanguage-transformer-decoder-beam.ipynb ├── 5.lstm-seq2seq-contrib-greedy.ipynb ├── 50.xlnet-base-transformer-decoder-beam.ipynb ├── 51.evolved-transformer-tiny.ipynb ├── 6.gru-seq2seq-contrib-greedy.ipynb ├── 7.basic-birnn-seq2seq.ipynb ├── 8.lstm-birnn-seq2seq.ipynb ├── 9.gru-birnn-seq2seq.ipynb ├── README.md ├── access.py ├── addressing.py ├── bert-preprocessing.ipynb ├── dnc.py ├── download-preprocess-dataset-bpe.ipynb ├── download-preprocess-dataset.ipynb ├── gpt_2.py ├── util.py └── xlnet-preprocessing.ipynb ├── nlp-tf.png ├── not-deep-learning ├── decomposition-summarization │ ├── 1.lda.ipynb │ ├── 2.lsa.ipynb │ └── 3.nmf.ipynb └── markov-chatbot │ └── markov-chatbot.ipynb ├── ocr ├── 1.cnn-rnn-ctc.ipynb ├── 2.im2latex.ipynb └── README.md ├── pos-tagging ├── 1.rnn-lstm-crf.ipynb ├── 2.rnn-lstm-crf-luong.ipynb ├── 3.rnn-lstm-crf-bahdanau.ipynb ├── 4.rnn-lstm-crf-bahdanau-ngrams.ipynb ├── 5.rnn-lstm-crf-luong-ngrams.ipynb ├── 6.cnn-residual-bahdanau-ngrams.ipynb ├── 7.attention-is-all-you-need.ipynb ├── 8.bert.ipynb └── README.md ├── question-answer ├── 1.end-to-end-basic.ipynb ├── 2.end-to-end-gru.ipynb ├── 3.end-to-end-lstm.ipynb ├── 4.dynamic-memory-gru.ipynb ├── README.md ├── attention_gru.py ├── qa5_three-arg-relations_test.txt └── qa5_three-arg-relations_train.txt ├── requirements.txt ├── sentence-pair ├── Archive.zip ├── README.md └── bert.ipynb ├── speech-to-text ├── 1.tacotron.ipynb ├── 10.deep-speech2.ipynb ├── 11.wav2vec-transfer-learning-birnn-lstm-ctc.ipynb ├── 2.birnn-lstm-ctc-greedy.ipynb ├── 3.birnn-seq2seq-luong-cross-entropy.ipynb ├── 4.birnn-seq2seq-bahdanau-cross-entropy.ipynb ├── 5.birnn-seq2seq-bahdanau-ctc.ipynb ├── 6.birnn-seq2seq-luong-ctc.ipynb ├── 7.cnn-rnn-bahdanau.ipynb ├── 8.dilated-cnn-rnn.ipynb ├── 9.wavenet.ipynb ├── README.md ├── augmentation.py ├── caching.ipynb ├── download.ipynb ├── wav2vec-preprocessing.ipynb ├── wav2vec-pytorch.ipynb ├── wav2vec-tf.ipynb └── wav2vec.ipynb ├── spelling-correction ├── 1.bert-base.ipynb ├── 2.xlnet-base.ipynb ├── 3.bert-base-fast.ipynb └── 4.bert-accurate.ipynb ├── squad-qa └── 1.bert.ipynb ├── stemming ├── 1.lstm-seq2seq-beam.ipynb ├── 2.gru-seq2seq-beam.ipynb ├── 3.lstm-birnn-seq2seq-greedy.ipynb ├── 4.gru-birnn-seq2seq-greedy.ipynb ├── 5.dnc-seq2seq-bahdanau-greedy.ipynb ├── 6.birnn-bahdanau-copynet.ipynb ├── README.md ├── access.py ├── addressing.py ├── dnc-seq2seq-bahdanau-greedy.ipynb ├── dnc.py ├── lemmatization-en.txt └── util.py ├── text-augmentation ├── 1.glove.ipynb ├── 2.gru-vae-beam-tfprob.ipynb ├── 3.lstm-vae-beam-tfprob.ipynb ├── 4.gru-vae-beam-bahdanau-tfprob.ipynb ├── 5.vae-deterministic-bahdanau.ipynb ├── 6.vae-varitional-bahdanau │ ├── attention_wrapper.py │ ├── basic_decoder.py │ ├── decoder.py │ └── vae-variational-bahdanau.ipynb ├── 7.bert-base.ipynb ├── 8.xlnet-augmentation.ipynb └── README.md ├── text-classification ├── 1.basic-rnn.ipynb ├── 10.lstm-rnn-bidirectional.ipynb ├── 11.lstm-rnn-bidirectional-huber.ipynb ├── 12.lstm-rnn-dropout-l2.ipynb ├── 13.gru-rnn.ipynb ├── 14.gru-rnn-hinge.ipynb ├── 15.gru-rnn-huber.ipynb ├── 16.gru-rnn-bidirectional.ipynb ├── 17.gru-rnn-bidirectional-hinge.ipynb ├── 18.gru-rnn-bidirectional-huber.ipynb ├── 19.lstm-cnn-rnn.ipynb ├── 2.basic-rnn-hinge.ipynb ├── 20.kmax-cnn.ipynb ├── 21.lstm-cnn-rnn-highway.ipynb ├── 22.lstm-rnn-attention.ipynb ├── 23.dilated-rnn-lstm.ipynb ├── 24.lnlstm-rnn.ipynb ├── 25.only-attention.ipynb ├── 26.multihead-attention.ipynb ├── 27.neural-turing-machine.ipynb ├── 28.lstm-seq2seq.ipynb ├── 29.lstm-seq2seq-luong.ipynb ├── 3.basic-rnn-huber.ipynb ├── 30.lstm-seq2seq-bahdanau.ipynb ├── 31.lstm-seq2seq-beam.ipynb ├── 32.lstm-seq2seq-birnn.ipynb ├── 33.pointer-net.ipynb ├── 34.lstm-rnn-bahdanau.ipynb ├── 35.lstm-rnn-luong.ipynb ├── 36.lstm-rnn-bahdanau-luong.ipynb ├── 37.lstm-birnn-bahdanau-luong.ipynb ├── 38.bytenet.ipynb ├── 39.fast-slow-lstm.ipynb ├── 4.basic-rnn-bidirectional.ipynb ├── 40.siamese-network.ipynb ├── 41.estimator.ipynb ├── 42.capsule-rnn-lstm.ipynb ├── 43.capsule-seq2seq-lstm.ipynb ├── 44.capsule-birrn-seq2seq-lstm.ipynb ├── 45.nested-lstm.ipynb ├── 46.lstm-seq2seq-highway.ipynb ├── 47.triplet-loss-lstm.ipynb ├── 48.dnc.ipynb ├── 49.convlstm.ipynb ├── 5.basic-rnn-bidirectional-hinge.ipynb ├── 50.temporalconvd.ipynb ├── 51.batch-all-triplet-loss-lstm.ipynb ├── 52.fast-text.ipynb ├── 53.gated-convolution-network.ipynb ├── 54.simple-recurrent-units.ipynb ├── 55.lstm-han.ipynb ├── 56.bert.ipynb ├── 57.dynamic-memory-network.ipynb ├── 58.entity-network.ipynb ├── 59.memory-network.ipynb ├── 6.basic-rnn-bidirectional-huber.ipynb ├── 60.char-sparse.ipynb ├── 61.residual-network.ipynb ├── 62.residual-network-bahdanau.ipynb ├── 63.deep-pyramid-cnn.ipynb ├── 63.deep-pyramid-cnn.py ├── 64.transformer-xl.ipynb ├── 65.transfer-learning-gpt2.ipynb ├── 66.quasi-rnn.ipynb ├── 67.tacotron.ipynb ├── 68.slice-gru.ipynb ├── 69.slice-gru-bahdanau.ipynb ├── 7.lstm-rnn.ipynb ├── 70.wavenet.ipynb ├── 71.transfer-learning-bert-base.ipynb ├── 72.transfer-learning-xlnet-large.ipynb ├── 73.lstm-birnn-max-avg.ipynb ├── 74.transfer-learning-bert-base-6.ipynb ├── 75.transfer-learning-bert-large-12.ipynb ├── 76.transfer-learning-xlnet-base.ipynb ├── 77.transfer-learning-albert-base.ipynb ├── 78.electra-base.ipynb ├── 79.electra-large.ipynb ├── 8.lstm-rnn-hinge.ipynb ├── 9.lstm-rnn-huber.ipynb ├── README.md ├── bert_model.py ├── data.zip ├── data │ ├── negative │ │ └── negative │ └── positive │ │ └── positive ├── dynamic_memory_network.py ├── entity_network.py ├── gpt_2.py ├── modules.py ├── utils.py └── xl.py ├── text-similarity ├── 1.birnn-contrastive.ipynb ├── 2.dilated-cnn-contrastive.ipynb ├── 3.transformer-contrastive.ipynb ├── 4.dilated-cnn-crossentropy.ipynb ├── 5.transformer-crossentropy.ipynb ├── 6.bert.ipynb ├── 7.xlnet-ipynb └── README.md ├── text-to-speech ├── 1.tacotron │ ├── caching.py │ ├── tacotron.ipynb │ ├── tacotron.py │ ├── test-tacotron.wav │ └── utils.py ├── 2.fairseq-dilated-cnn.ipynb ├── 3.seq2seq-bahdanau.ipynb ├── 4.seq2seq-luong.ipynb ├── 5.dilated-cnn-monothonic-attention.ipynb ├── 6.dilated-cnn-self-attention.ipynb ├── 7.deep-cnn-monothonic-attention.ipynb ├── 8.deep-cnn-self-attention.ipynb ├── README.md ├── caching.py ├── download.ipynb ├── test-bahdanau.wav ├── test-dilated-cnn-monothonic-attention.wav ├── test-dilated-cnn-self-attention.wav ├── test-luong.wav └── utils.py ├── topic-generator ├── 1.tat.ipynb ├── 2.tav.ipynb ├── 3.mta.ipynb ├── 4.dilated-seq2seq.ipynb └── README.md ├── topic-model ├── 1.lda2vec.ipynb ├── 2.bert-topic.ipynb ├── 3.xlnet-topic.ipynb ├── modeling.py ├── prepro_utils.py ├── utils.py └── xlnet.py ├── unsupervised-extractive-summarization ├── 1.skip-thought.ipynb ├── 2.residual-network.ipynb ├── 3.residual-network-bahdanau.ipynb ├── README.md └── books │ ├── Blood_Born │ ├── Dark_Thirst │ └── Driftas_Quest ├── vectorizer ├── 1.cbow-softmax.ipynb ├── 10.fast-text.ipynb ├── 11.elmo.ipynb ├── 12.bert-batch-all-triplet-loss.ipynb ├── 2.cbow-nce.ipynb ├── 3.skipgram-softmax.ipynb ├── 4.skipgram-nce.ipynb ├── 5.lda2vec.ipynb ├── 6.supervised-embedded.ipynb ├── 7.triplet-loss.ipynb ├── 8.auto-encoder.ipynb ├── 9.batch-all-triplet-loss-lstm-embedded.ipynb ├── README.md ├── data │ ├── negative │ │ └── negative │ └── positive │ │ └── positive └── utils.py ├── visualization ├── 1.attention-visualization-bahdanau.ipynb ├── 2.attention-visualization-luong.ipynb ├── 3.bert-attention.ipynb ├── 4.xlnet-attention.ipynb └── 5.bert-topic.ipynb └── vocoder ├── 1.dilated-cnn.ipynb ├── README.md ├── caching-vocoder.ipynb └── download.ipynb /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/laxmimerit/NLP-Models-Tensorflow/HEAD/.gitignore -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/laxmimerit/NLP-Models-Tensorflow/HEAD/LICENSE -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 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