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      "ref": "akshayt19nayak/pytorch-bi-lstm",
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      "ref": "anebzt/quora-preprocessing-model",
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      "ref": "wangcong95/qicq-final-kernel",
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      "ref": "wangggong/quora-bilstm-attention-kfold-fork-jannen",
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      "ref": "ishitori/using-convolution-encoder-with-apache-mxnet",
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      "ref": "labdmitriy/quora-eda-unicode-characters-distribution",
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      "ref": "alexanderliao/vectorizing-english-sentences-by-infersent",
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      "ref": "strifonov/avg-simple-rnn-with-attention-and-preprocessing",
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      "ref": "mabrek/simple-fasttext-pretrained",
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      "ref": "frngo3/quora-insecure-questions-predictions",
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      "ref": "cchyun/qiqc-pytorch-2",
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      "ref": "jannen/reaching-0-7-fork-from-bilstm-attention-kfold",
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      "kernel_id": "2672318"
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      "ref": "datatoknowl/fasttext",
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      "ref": "hamishdickson/using-keras-oov-tokens",
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      "kernel_id": "2668643"
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      "ref": "tanreinama/insincere-sincere-insincere",
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      "source": "live",
      "kernel_id": "2665473"
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      "ref": "kamal2611/quora-insincere",
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      "ref": "ayusov/find-quora-glove-50-vectors",
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      "source": "live",
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      "ref": "timothylucas/optimising-your-model-for-qiqc-using-hyperopt",
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      "kernel_id": "2656624"
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      "ref": "amitabhac/simple-lstm-attention-with-glove",
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      "kernel_id": "2655672"
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      "ref": "dilapsky/decrease-lr-local-f1-0-7432",
      "title": "decrease lr Local F1:0.7432",
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      "kernel_id": "2655599"
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    {
      "ref": "rishabhjain2764/word-embeddings-with-glove",
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      "source": "live",
      "kernel_id": "2655390"
    },
    {
      "ref": "sirsikarakshay/quora-neural-nets-for-checking-insincerity",
      "title": "Quora : Neural nets for checking insincerity",
      "source": "live",
      "kernel_id": "2654701"
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      "ref": "ishitori/what-if-we-could-finetune-bert-from-apache-mxnet",
      "title": "What if we could finetune BERT from Apache MXNet?",
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      "kernel_id": "2653464"
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      "ref": "tboyle10/lstm-attention",
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      "source": "live",
      "kernel_id": "2651722"
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    {
      "ref": "paulorzp/compressing-a-binary-submission-within-a-string",
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      "kernel_id": "2649500"
    },
    {
      "ref": "abdul0807/ensemble-simple-ml-models-for-beginners",
      "title": "Ensemble Simple ML Models (for beginners)",
      "source": "live",
      "kernel_id": "2649438"
    },
    {
      "ref": "n2cholas/preprocessing-and-cnn-attention-in-tensorflow",
      "title": "Preprocessing and CNN+Attention  in TensorFlow",
      "source": "live",
      "kernel_id": "2646394"
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    {
      "ref": "akrsrivastava/topic-modeling-lda-on-insincere-questions",
      "title": "Topic Modeling (LDA) on Insincere questions",
      "source": "live",
      "kernel_id": "2641099"
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      "ref": "xsakix/torch-lstm-word2vec",
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      "ref": "ksayantani/utility-functions",
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      "source": "live",
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      "ref": "ksayantani/insincere-questions-eda-understanding",
      "title": "Insincere questions EDA  understanding",
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      "kernel_id": "2638692"
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    {
      "ref": "jialinzhang/text-cnn-quora-question",
      "title": "Text-CNN实现Quora question分类",
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      "kernel_id": "2638616"
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    {
      "ref": "garydf/fork-from-bilstm-attention-kfold-0115",
      "title": "Fork_from_BiLSTM-attention-Kfold-0115",
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      "kernel_id": "2637990"
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    {
      "ref": "jmourad100/fork-of-nlp-text-analytics-quora-insincere-quest",
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      "kernel_id": "2636940"
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      "ref": "kashnitsky/how-to-download-submission-file-without-committing",
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      "ref": "xsakix/torch-lstm-glove-para-2",
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      "ref": "skumar2007ctae/accuracy-of-different-models",
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    {
      "ref": "red8012/gru-2-epochs",
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    {
      "ref": "vectors/quoraversion-1",
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      "ref": "red8012/unoptimized-model",
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      "ref": "mysterious123/lstm-gru-addfeatures-keras",
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    {
      "ref": "benedictldm/quora-comp",
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    {
      "ref": "jetouxu/bilstm-attention-kfold-clr-extra-features-bn",
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      "kernel_id": "2621631"
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    {
      "ref": "jmourad100/nlp-text-analytics-quora-insincere-questions",
      "title": "NLP text analytics - Quora Insincere Questions",
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      "kernel_id": "2620933"
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    {
      "ref": "danghle/pad-length-experiments",
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      "ref": "alexfilippov/quora-lda-model",
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      "kernel_id": "2619347"
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      "ref": "tankist88/quora-insincere-questions-cnn-ensemble",
      "title": "Quora Insincere questions (CNN ensemble)",
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      "kernel_id": "2618043"
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    {
      "ref": "jialinzhang/dnn-quora-question",
      "title": "DNN实现Quora question分类",
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      "kernel_id": "2616817"
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      "ref": "xsakix/torch-lstm-2",
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      "ref": "zhaozhihao/pytorch-kfold-clr",
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    {
      "ref": "jialinzhang/lightgbm-quora-question",
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      "source": "live",
      "kernel_id": "2613588"
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      "ref": "lordskloore2/quora-compo",
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      "ref": "konohayui/cv-v-s-lb",
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      "ref": "hamishdickson/submission-distributions",
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      "ref": "zhaozhihao/i-like-run-script-kfold-clr-feat-thanks",
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      "ref": "xsakix/torch-lstm-train-pretrained",
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      "ref": "xsakix/torch-train-pretrianed",
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      "ref": "zubrabubra/pytorch-starter",
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      "ref": "red8012/robustness-validation",
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      "ref": "kleikev/quora-project",
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      "ref": "jialinzhang/xgboost-quora-question",
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      "ref": "swatisinghal/understanding-data-quora-insincere-questions",
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      "ref": "nrr1509/twitter-embeddings",
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      "ref": "yshubham/attention-on-bi-lstm-hidden-states-forked",
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      "ref": "genyuan/lstm-emsemble-deep-learning-pytorch",
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      "ref": "bminixhofer/a-validation-framework-impact-of-the-random-seed",
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      "ref": "sunnymarkliu/augment-insincere-questions-with-markov-chains",
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      "ref": "moshizhiyinof401/moshi-s",
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      "ref": "zsn6034/2gru-2attention-v1",
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      "ref": "jf2333/how-should-we-validate-our-models",
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      "ref": "xsakix/torch-with-clr",
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      "ref": "reatank/rnn-cnn",
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      "ref": "sunnymarkliu/latex-cannot-be-used-in-a-quora-question",
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      "ref": "reatank/convnns",
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      "ref": "praburocking/text-preprocessing",
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      "ref": "eligijus/k-fold-analysis-for-the-main-code",
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      "ref": "chenshengabc/fork-bilstm-attention-kfold-clr-extra-features",
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      "ref": "czk2014/textcnn",
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      "ref": "carrieisaacson/quora-step-1-clean-and-preprocess-documents",
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      "ref": "tangyuemeng/just-trying",
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      "ref": "strifonov/avg-on-embeddings-simple-rnn-with-attention",
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      "ref": "qibajiu/nlpproject",
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      "ref": "mlwhiz/third-place-model-for-toxic-comments-in-pytorch",
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      "kernel_id": "2565136"
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      "ref": "kobynim/quora-questions-revealed-zbn-kn-nf-1-2019-v7",
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      "ref": "bgeier/ltsm-capsule",
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      "ref": "ccatalfo/initial-eda",
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      "ref": "xsakix/bilstm-base-classifier-fold-unfreeze-meta-v2",
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      "ref": "pavelholubik/capsulenet",
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      "ref": "rajmehra03/a-detailed-explanation-of-keras-embedding-layer",
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      "ref": "datatoknowl/quora-insincere-questions-dl-approaches",
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      "ref": "splacorn/better-embeddings",
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      "ref": "mazdaryo/how-can-i-silence-the-tqdm-output",
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      "ref": "xsakix/cnn-base-classifier-fold-unfreeze-meta-v4",
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      "ref": "lyutry/textcnn-first",
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      "ref": "ziliwang/pytorch-text-cnn",
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      "ref": "xsakix/cnn-base-classifier-fold-meta-v3",
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      "ref": "red8012/gru-final",
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      "ref": "ashishpatel26/attension-layer-basic-for-nlp",
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      "ref": "lemonwaffle/quora-pytorch-torchtext",
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      "ref": "srujanperam/basic-text-mining",
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