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      "ref": "christofhenkel/text2image-top-1",
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      "ref": "danieleewww/avito-lightgbm-with-ridge-feature-v-2-0",
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      "ref": "climbercarmich/exploration-distributions-and-relationships",
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      "ref": "batale/wip-eda-notebook",
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      "ref": "liuhdsgoal/ideas-for-image-features-multiprocessing-support",
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      "kernel_id": "1009009"
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      "ref": "sukhyun9673/image-processing-600000-to-750000",
      "title": "Image_processing[600000 to 750000]",
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    {
      "ref": "sukhyun9673/scraping-regional-info-population-time-zone-etc",
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      "kernel_id": "1005238"
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      "ref": "stecasasso/russian-city-population-from-wikipedia",
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      "ref": "him4318/avito-lightgbm-with-ridge-feature-v-2-0",
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      "kernel_id": "1001211"
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      "ref": "tetyanayatsenko/xgb-text2vec-tfidf-0-2237-yatsenko",
      "title": "xgb+text2vec+tfidf [0.2237] _Yatsenko",
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      "kernel_id": "999959"
    },
    {
      "ref": "classtag/take-care-of-emoji-character-when-nlp",
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      "kernel_id": "998942"
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    {
      "ref": "sujoyde/avito-demand-prediction-exploration",
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      "kernel_id": "998547"
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    {
      "ref": "lux666/avito",
      "title": "??????????????? ?????? AVITO",
      "source": "live",
      "kernel_id": "996869"
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    {
      "ref": "frankherfert/region-and-city-details-with-lat-lon-and-clusters",
      "title": "Region and City Details with Lat, Lon and Clusters",
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      "kernel_id": "996232"
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    {
      "ref": "shibashis/avito-end-to-end",
      "title": "Avito - End to End ",
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      "ref": "sukhyun9673/scraping-regional-info-from-wikipedia",
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      "ref": "liuhdsgoal/about-image-top-1-is-a-classify-label",
      "title": "about image_top_1, is a  classify label?",
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      "kernel_id": "989076"
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    {
      "ref": "learnmower/ideas-for-image-features-and-multiproccessing",
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      "kernel_id": "988222"
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      "ref": "jingqliu/stacked-model-cnn-xgboost",
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    {
      "ref": "frankherfert/tips-tricks-for-working-with-large-datasets",
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      "kernel_id": "983732"
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    {
      "ref": "him4318/lightgbm-with-aggregated-features-v-2-0",
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      "kernel_id": "982880"
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      "ref": "johnfarrell/adp-mixed-nn-rush",
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    {
      "ref": "classtag/avito-word2vec-with-all-dataset-text",
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      "kernel_id": "982014"
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      "ref": "prashantkikani/avito-lightgbm-with-ridge-feature",
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      "kernel_id": "980895"
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    {
      "ref": "danieleewww/lightgbm-with-ridge-feature",
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      "source": "live",
      "kernel_id": "980177"
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    {
      "ref": "cristianionescu92/number-of-words-in-description-vs-deal-probability",
      "title": "Number of words in description vs deal_probability",
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      "kernel_id": "979813"
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    {
      "ref": "cristianionescu92/fork-of-early-beginning",
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      "kernel_id": "979782"
    },
    {
      "ref": "bminixhofer/aggregated-features-lightgbm",
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      "kernel_id": "978883"
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    {
      "ref": "nareyko/very-simple-lgbm-0-2307",
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      "kernel_id": "978542"
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    {
      "ref": "danieleewww/bow-meta-text-and-dense-features-lb-0-2242",
      "title": "BoW, Meta text , and Dense Features - [LB 0.2242]",
      "source": "live",
      "kernel_id": "976141"
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    {
      "ref": "christofhenkel/self-trained-embeddings-starter-only-description",
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      "ref": "amitkumarjaiswal/avito-preprocessing-text",
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      "ref": "christofhenkel/using-train-active-for-training-word-embeddings",
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      "ref": "danieleewww/xgb-text2vec-tfidf-0-2243",
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      "ref": "johnfarrell/adp-pytorch-textcnn-dev",
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      "ref": "tapioca/item-seq-number-vs-deal-probability",
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      "ref": "vannak/character-embedding-pre-training",
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      "kernel_id": "971407"
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    {
      "ref": "tezdhar/map-ad-durations",
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      "kernel_id": "971229"
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      "ref": "vykhand/idear-for-avito-demand-forecasting",
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      "kernel_id": "970169"
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      "ref": "cbrioso/extract-image-features-test-using-dask",
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      "ref": "kuniyoshit/simple-lightgbm-vgg16-image-tfidf-text",
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      "ref": "prashantkikani/avito-lgb-features-diff-seed",
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      "kernel_id": "969450"
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      "ref": "leadbest/xgb-classification",
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      "ref": "classtag/cat2vec-powerful-feature-for-categorical",
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      "ref": "amitkumarjaiswal/in-depth-eda-feature-engineering",
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      "kernel_id": "965717"
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    {
      "ref": "scirpus/beat-the-benchmark",
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      "kernel_id": "965086"
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      "ref": "demery/lightgbm-with-ridge-feature",
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      "kernel_id": "965064"
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    {
      "ref": "christofhenkel/fasttext-starter-description-only",
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      "ref": "legorreta/lightgbm-text-tfidf-bl",
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    {
      "ref": "skar26/avito-demand-prediction-model-random-forest",
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      "ref": "legorreta/ridge-model",
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      "kernel_id": "963072"
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    {
      "ref": "shivamb/ideas-for-image-features-and-image-quality",
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      "kernel_id": "961952"
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    {
      "ref": "sukhyun9673/lgb-nan-image-blurrness-regional-info-date",
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      "kernel_id": "961767"
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    {
      "ref": "sneddy/fast-low-memory-learning-part-1-vowpalwabbit",
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      "kernel_id": "960735"
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    {
      "ref": "cczaixian/test-on-lb-split",
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      "kernel_id": "959009"
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    {
      "ref": "dhznsdl/nn-model-adding-variables-step-by-step",
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      "kernel_id": "958967"
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    {
      "ref": "iamivo/character-fun",
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      "kernel_id": "957772"
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    {
      "ref": "peterhurford/image-feature-engineering",
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      "ref": "skar26/avito-demand-prediction-model-gbm",
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      "ref": "peterhurford/boosting-mlp-lb-0-2297",
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      "ref": "seroppi/avito-with-lightgbm-tfidf",
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      "ref": "yacropolisy/random-forest-test-for-learning",
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      "ref": "kabure/testing-avito-s-bubblechart",
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      "ref": "lks21c/simple-exploration-avito",
      "title": "[????] Simple Exploration  Avito",
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      "ref": "tushar786/trying-lightgbm-first-time-on-avito-demand-pred",
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    {
      "ref": "tunguz/bow-meta-text-and-dense-features-lb-0-2241",
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      "kernel_id": "945611"
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    {
      "ref": "nicapotato/bow-meta-text-and-dense-features-lgbm",
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      "kernel_id": "944209"
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    {
      "ref": "gidutz/text2score-keras-rnn-word-embedding",
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      "kernel_id": "943946"
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    {
      "ref": "alxmamaev/how-to-easy-preprocess-russian-text",
      "title": "How to easy preprocess Russian text ????",
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      "kernel_id": "943443"
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    {
      "ref": "paulorzp/tfidf-tensor-starter-lb-0-233",
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      "kernel_id": "942709"
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    {
      "ref": "ranliu/xgb-lgb-with-group-text-vgg-image-features",
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      "kernel_id": "939899"
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    {
      "ref": "slickwilly/xgb-text2vec-tfidf-clone-of-clone-of-clone",
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      "source": "live",
      "kernel_id": "939563"
    },
    {
      "ref": "jpmiller/exploring-geography-for-1-5m-deals",
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      "kernel_id": "938513"
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    {
      "ref": "deeiip/simplest-xgb-without-description-or-title-0-2377",
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      "kernel_id": "938008"
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    {
      "ref": "legorreta/python-version-xgb-text2vec-tfidf-lb-0-226",
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    {
      "ref": "hugoncosta/tweaked-simple-catboost-tfidf",
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      "kernel_id": "934837"
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    {
      "ref": "soumya2g/avito-data-translation-and-transformation",
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      "ref": "tunguz/xgb-text2vec-tfidf-clone-of-clone-lb-0-2256",
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      "ref": "pedroschoen/analysis-of-date-variables",
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      "ref": "maheshak04/avito-simple-read",
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      "ref": "leeasy/yanji-itmba7-avito",
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      "kernel_id": "930127"
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      "ref": "amhchiu/reading-image-files-in-r",
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      "ref": "andreikhropov/simple-feat-catboost-w-hparam-tun-lb-0-230-unlim",
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      "ref": "inenakhov/simple-catboost-tfidf",
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      "ref": "gunnvant/russian-word-embeddings-for-fun-and-for-profit",
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      "kernel_id": "928046"
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      "ref": "skar26/saso-avito-demand-prediction-analysis",
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    {
      "ref": "kaparna/translate-titles-and-descriptions",
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      "ref": "prashantkikani/avito-random-forest-oof-lb-0-248",
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      "kernel_id": "926946"
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      "ref": "danofer/simple-catboost-fork",
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      "kernel_id": "926904"
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      "ref": "konradb/xgb-text2vec-tfidf-clone-lb-0-226",
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      "kernel_id": "926204"
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    {
      "ref": "wolfgangb33r/advanced-avito-prediction-xgboost-word-char-counts",
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      "kernel_id": "925819"
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      "ref": "gunnvant/russian-to-english-translate-with-progress-bar",
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      "kernel_id": "925310"
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    {
      "ref": "dicksonchin93/wordbatch-fm-ftrl-0-238",
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      "ref": "twistedtensor/baseline-on-structured-part-with-catboost",
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      "kernel_id": "923468"
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      "ref": "dicksonchin93/capsule-networks-on-description",
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      "ref": "florianaspart/lightgbm-with-mean-encode-feat-no-data-leakage",
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    {
      "ref": "kailex/xgb-text2vec-tfidf-0-2237",
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      "ref": "bguberfain/naive-lgb-with-text-images",
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      "ref": "bguberfain/vgg16-test-features",
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      "ref": "pamin2222/lgb-baseline-v23-script-add-text-feature",
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      "ref": "dicksonchin93/keras-gru-cnn-model-with-fasttext-on-description",
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      "ref": "naikankita/create-numpy-array-for-processing-images",
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      "ref": "bguberfain/vgg16-train-features",
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      "ref": "dicksonchin93/feature-extraction-on-description-title",
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      "source": "live",
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      "ref": "vannak/nn-starter-no-period-image-title-desc",
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      "kernel_id": "917889"
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      "ref": "ganeshn88/detailed-eda-nlp-xgboost-model",
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      "ref": "jingqliu/fasttext-conv2d-title-keras",
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      "ref": "dicksonchin93/xgb-with-mean-encode-tfidf-feature-0-232",
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      "source": "live",
      "kernel_id": "917024"
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