{
  "source": "Meta Kaggle Kernels.csv",
  "source_snapshot": {
    "source_mtime": 1790828993000000000,
    "source_date": "2026-10-01T04:29:53Z"
  },
  "items": [
    {
      "ref": "kyouyap/titanickyouyap",
      "kernel_id": "5984937",
      "votes": 3
    },
    {
      "ref": "reighns/general-learning-linear-regression-from-scratch",
      "kernel_id": "7871139",
      "votes": 46
    },
    {
      "ref": "alincijov/nlp-starter-continuous-bag-of-words-cbow",
      "kernel_id": "9415350",
      "votes": 219
    },
    {
      "ref": "alincijov/nlp-starter-logsoftmax-nlloss-cross-entropy",
      "kernel_id": "9402446",
      "votes": 89
    },
    {
      "ref": "konradb/linear-baseline-with-cv",
      "kernel_id": "16705137",
      "votes": 89
    },
    {
      "ref": "konradb/diy-data-augmentation-with-gpt2",
      "kernel_id": "16802497",
      "votes": 26
    },
    {
      "ref": "konradb/chapter11-nlp-augmentations3",
      "kernel_id": "16883083",
      "votes": 15
    },
    {
      "ref": "abhishek/fork-of-fork-of-yum-yum-yum-93f968",
      "kernel_id": "16827391",
      "votes": 208
    },
    {
      "ref": "abhishek/step-1-create-folds",
      "kernel_id": "16752954",
      "votes": 229
    },
    {
      "ref": "abhishek/yum-yum-yum",
      "kernel_id": "16779234",
      "votes": 204
    },
    {
      "ref": "jeongyoonlee/tf-keras-bert-baseline-training-inference",
      "kernel_id": "16706858",
      "votes": 263
    },
    {
      "ref": "jeongyoonlee/all-zero-submission",
      "kernel_id": "16767372",
      "votes": 11
    },
    {
      "ref": "jeongyoonlee/tf-keras-bart-baseline-training-inference",
      "kernel_id": "16840794",
      "votes": 28
    },
    {
      "ref": "jeongyoonlee/pytorch-lightning-roberta-training-inference",
      "kernel_id": "16796730",
      "votes": 48
    },
    {
      "ref": "lawrencechernin/ridge-regression-starter-with-stop-words",
      "kernel_id": "16704738",
      "votes": 6
    },
    {
      "ref": "aninda/commonlit-fastai",
      "kernel_id": "16712371",
      "votes": 13
    },
    {
      "ref": "crained/deberta-pytorch-commonlit-readability-train",
      "kernel_id": "16854497",
      "votes": 37
    },
    {
      "ref": "crained/deberta-pytorch-inference",
      "kernel_id": "16859564",
      "votes": 21
    },
    {
      "ref": "crained/fun-with-commonlit-nltk-nda",
      "kernel_id": "16990949",
      "votes": 13
    },
    {
      "ref": "crained/fun-with-commonlit-using-scikit-learn-nda",
      "kernel_id": "16993667",
      "votes": 13
    },
    {
      "ref": "pavelvod/catboost-text-features",
      "kernel_id": "16704967",
      "votes": 10
    },
    {
      "ref": "pavelvod/simple-starter-baseline",
      "kernel_id": "16703161",
      "votes": 16
    },
    {
      "ref": "pavelvod/transformer-cnn-lstm-attention-heads",
      "kernel_id": "16987000",
      "votes": 34
    },
    {
      "ref": "tungmphung/commonlit-readability-eda",
      "kernel_id": "16704826",
      "votes": 14
    },
    {
      "ref": "pehahn/tidymodels-nb3train",
      "kernel_id": "16791041",
      "votes": 3
    },
    {
      "ref": "pehahn/basic-readabil",
      "kernel_id": "16955955",
      "votes": 3
    },
    {
      "ref": "pehahn/dnn-tokenizer",
      "kernel_id": "16976932",
      "votes": 6
    },
    {
      "ref": "pehahn/r-submission-1",
      "kernel_id": "16724893",
      "votes": 11
    },
    {
      "ref": "aristotelisch/basic-text-processing-lda-roberta-transformer",
      "kernel_id": "16785266",
      "votes": 19
    },
    {
      "ref": "praveengovi/commonlit-bert-roberta-model-explainablity",
      "kernel_id": "16849178",
      "votes": 4
    },
    {
      "ref": "praveengovi/commonlitreadabilityprize-all-in-one-eda",
      "kernel_id": "16703866",
      "votes": 21
    },
    {
      "ref": "evilmage93/common-lit-bilstm-based-regression",
      "kernel_id": "16977488",
      "votes": 6
    },
    {
      "ref": "indranilbhattacharya/eda-feature-engineering",
      "kernel_id": "16741288",
      "votes": 38
    },
    {
      "ref": "geoffcc/notebook935a1345d7",
      "kernel_id": "16870656",
      "votes": 1
    },
    {
      "ref": "zfatgxu/notebook214aa91702",
      "kernel_id": "16938690",
      "votes": 1
    },
    {
      "ref": "afufiza/commonlit-analysis",
      "kernel_id": "16848358",
      "votes": 0
    },
    {
      "ref": "mzntaka0/simple-vectorization-using-doc2vec",
      "kernel_id": "16808944",
      "votes": 8
    },
    {
      "ref": "ankur310794/crp-mix-6-models-inference",
      "kernel_id": "16834937",
      "votes": 62
    },
    {
      "ref": "simakov/lama-bert-inference",
      "kernel_id": "16723959",
      "votes": 25
    },
    {
      "ref": "simakov/lama-bert-starter",
      "kernel_id": "16717680",
      "votes": 25
    },
    {
      "ref": "safavieh/basic-word-based-model",
      "kernel_id": "16703260",
      "votes": 50
    },
    {
      "ref": "lautenschlager/readability-baseline-using-simple-stats",
      "kernel_id": "16713760",
      "votes": 0
    },
    {
      "ref": "solverworld/how-big-are-you-test-set",
      "kernel_id": "16920242",
      "votes": 9
    },
    {
      "ref": "abee82/commonlit-readability-using-fastai-v2",
      "kernel_id": "16767264",
      "votes": 7
    },
    {
      "ref": "barun2104/commonlit-readability-eda-svd-modeling",
      "kernel_id": "16774213",
      "votes": 20
    },
    {
      "ref": "abhilashreddyy/a-deeper-eda-on-pos-tags-topic-modelling-more",
      "kernel_id": "16932977",
      "votes": 19
    },
    {
      "ref": "jollibobert/commonlit-simple-se-and-excerpt-length-eda",
      "kernel_id": "16935362",
      "votes": 7
    },
    {
      "ref": "jollibobert/commonlit-readability-roberta-xgb-baseline",
      "kernel_id": "16946227",
      "votes": 42
    },
    {
      "ref": "srijita97/basic-feature",
      "kernel_id": "16801709",
      "votes": 3
    },
    {
      "ref": "srijita97/eda-work",
      "kernel_id": "16766821",
      "votes": 4
    },
    {
      "ref": "nabanitaroy/commonlit-challenge",
      "kernel_id": "16756297",
      "votes": 1
    },
    {
      "ref": "ajain3982/universal-sentence-encoder-nn-using-keras",
      "kernel_id": "16757592",
      "votes": 8
    },
    {
      "ref": "ajain3982/commonlit-readability-skipgrams-linearreg",
      "kernel_id": "16717435",
      "votes": 9
    },
    {
      "ref": "yeayates21/commonlit-tf-gpu-xlm-roberta-ssl",
      "kernel_id": "16790555",
      "votes": 12
    },
    {
      "ref": "yeayates21/train-commonlit-xgb-baseline-advanced-features",
      "kernel_id": "16792357",
      "votes": 4
    },
    {
      "ref": "yeayates21/commonlit-random-forest-na-ve-baseline",
      "kernel_id": "16704739",
      "votes": 14
    },
    {
      "ref": "yeayates21/commonlit-text-augmentation-eng-to-fre-to-eng",
      "kernel_id": "16733380",
      "votes": 12
    },
    {
      "ref": "shams1/explor",
      "kernel_id": "16752966",
      "votes": 3
    },
    {
      "ref": "shams1/prize",
      "kernel_id": "16716848",
      "votes": 9
    },
    {
      "ref": "ayushggarg/commonlit-readability-prize-eda-and-baseline",
      "kernel_id": "16703864",
      "votes": 16
    },
    {
      "ref": "hamasho/normalizing-and-wordcloud",
      "kernel_id": "16706011",
      "votes": 7
    },
    {
      "ref": "rybalkin/basic-readability-metrics",
      "kernel_id": "16763577",
      "votes": 13
    },
    {
      "ref": "infernape/textstat-linearregression",
      "kernel_id": "16776432",
      "votes": 8
    },
    {
      "ref": "narendra/basic-eda-tabular-baseline-pytorch-nn",
      "kernel_id": "16845676",
      "votes": 2
    },
    {
      "ref": "bootiu/commonlit-pytorch-lightning-bert-benchmark",
      "kernel_id": "16823951",
      "votes": 7
    },
    {
      "ref": "anubhav1302/readability-eda",
      "kernel_id": "16711879",
      "votes": 7
    },
    {
      "ref": "anubhav1302/simplemodel-tf",
      "kernel_id": "16772364",
      "votes": 5
    },
    {
      "ref": "msafi04/commonlit-readability-bert-baseline-tf",
      "kernel_id": "16713941",
      "votes": 12
    },
    {
      "ref": "msafi04/tensorflow-roberta-commonlit-readability",
      "kernel_id": "16869937",
      "votes": 12
    },
    {
      "ref": "abhibisht89/using-textstat-eda-and-readability-scores",
      "kernel_id": "16776114",
      "votes": 9
    },
    {
      "ref": "shahules/commonlit-roberta-xgb-ensemble",
      "kernel_id": "16830112",
      "votes": 24
    },
    {
      "ref": "cascadinglight/commonlit-dimensionality-reduction-and-eda",
      "kernel_id": "16850880",
      "votes": 15
    },
    {
      "ref": "snnclsr/commonlit-pytorch-distilbert-training",
      "kernel_id": "16704546",
      "votes": 20
    },
    {
      "ref": "snnclsr/commonlit-fastai-inference",
      "kernel_id": "16724110",
      "votes": 9
    },
    {
      "ref": "snnclsr/deadly-simple-baseline",
      "kernel_id": "16743675",
      "votes": 10
    },
    {
      "ref": "snnclsr/commonlit-pytorch-distilbert-inference",
      "kernel_id": "16708692",
      "votes": 17
    },
    {
      "ref": "snnclsr/commonlit-fastai-training-with-language-model",
      "kernel_id": "16723168",
      "votes": 16
    },
    {
      "ref": "yujikawa/commonlit-readability-prize-eda",
      "kernel_id": "16749087",
      "votes": 4
    },
    {
      "ref": "shaz13/commonlit-basic-feature-engineering-on-raw-text",
      "kernel_id": "16755156",
      "votes": 21
    },
    {
      "ref": "dimitreoliveira/commonlit-readability-eda-roberta-tf-baseline",
      "kernel_id": "16708236",
      "votes": 375
    },
    {
      "ref": "hengzheng/simpletransformers-regression-starter-less-code",
      "kernel_id": "16748633",
      "votes": 21
    },
    {
      "ref": "virksaab/data-augmentation-and-correlation",
      "kernel_id": "16929459",
      "votes": 7
    },
    {
      "ref": "samratsengupta/commonlit-readability-prize-roberta-ridge-ensemble",
      "kernel_id": "16823351",
      "votes": 0
    },
    {
      "ref": "tanmay17061/feature-eda",
      "kernel_id": "16704803",
      "votes": 4
    },
    {
      "ref": "ragnar123/commonlit-readability-roberta-tf",
      "kernel_id": "16860174",
      "votes": 147
    },
    {
      "ref": "ragnar123/commonlit-readability-roberta-tf-inference",
      "kernel_id": "16860318",
      "votes": 138
    },
    {
      "ref": "getitdone/simple-approach",
      "kernel_id": "16947600",
      "votes": 0
    },
    {
      "ref": "gustavomodelli/readability-lasso-regression",
      "kernel_id": "16763255",
      "votes": 6
    },
    {
      "ref": "gustavomodelli/readability-keras-model",
      "kernel_id": "16764357",
      "votes": 5
    },
    {
      "ref": "docxian/commonlit-first-glance",
      "kernel_id": "16704353",
      "votes": 9
    },
    {
      "ref": "ulrich07/ridge-regression-starter",
      "kernel_id": "16703875",
      "votes": 48
    },
    {
      "ref": "rohitpande/feature-engg-with-basic-linear-regression",
      "kernel_id": "16961822",
      "votes": 2
    },
    {
      "ref": "vdefont/dummy",
      "kernel_id": "16979488",
      "votes": 2
    },
    {
      "ref": "kneroma/clr-prize-roberta-model-inference",
      "kernel_id": "16708510",
      "votes": 49
    },
    {
      "ref": "kneroma/clr-prize-roberta-model-training",
      "kernel_id": "16773196",
      "votes": 47
    },
    {
      "ref": "deb009/commonlit-readability-prize-eda",
      "kernel_id": "16825636",
      "votes": 7
    },
    {
      "ref": "trantrikien239/preprocessing-and-svr-baseline",
      "kernel_id": "16967666",
      "votes": 3
    },
    {
      "ref": "gokhankesler/best-i-can-do-with-sklearn",
      "kernel_id": "16871666",
      "votes": 2
    },
    {
      "ref": "jiny333/creating-whl-files-to-install-external-libraries",
      "kernel_id": "16769828",
      "votes": 103
    },
    {
      "ref": "jiny333/simple-baseline-using-pycaret-training-inference",
      "kernel_id": "16716984",
      "votes": 6
    },
    {
      "ref": "nrottam/commonlit-readability-prize",
      "kernel_id": "16703974",
      "votes": 7
    },
    {
      "ref": "prvnkmr/tf-bert-baseline-lb-0-646-training",
      "kernel_id": "16995286",
      "votes": 7
    },
    {
      "ref": "prvnkmr/domain-knowledge-readability-score-methods",
      "kernel_id": "16992342",
      "votes": 16
    },
    {
      "ref": "leighplt/transformers-cv-train-inference-pytorch",
      "kernel_id": "16716661",
      "votes": 167
    },
    {
      "ref": "leighplt/simple-recurrent-model-pytorch",
      "kernel_id": "16703316",
      "votes": 53
    },
    {
      "ref": "aruthart/transformers-fastai-quick-starter",
      "kernel_id": "16727637",
      "votes": 9
    },
    {
      "ref": "aruthart/transformers-fastai-starter-submission",
      "kernel_id": "16784485",
      "votes": 24
    },
    {
      "ref": "hamditarek/commonlit-readability-prize-lsa-lda-nn-baseline",
      "kernel_id": "16704208",
      "votes": 25
    },
    {
      "ref": "harshitkhandelwal/commonlit-eda-literature-submission",
      "kernel_id": "16718154",
      "votes": 2
    },
    {
      "ref": "niduttnb/commonlit-readability",
      "kernel_id": "16984230",
      "votes": 1
    },
    {
      "ref": "atharvap329/torchflare-easy-model-training-bert-baseline",
      "kernel_id": "16939353",
      "votes": 14
    },
    {
      "ref": "sharrpie/commonlit-readability-eda-fe-topic-modelling",
      "kernel_id": "16806412",
      "votes": 12
    },
    {
      "ref": "teeyee314/readability-url-scrape",
      "kernel_id": "16961893",
      "votes": 55
    },
    {
      "ref": "teeyee314/readability-external-data-eda",
      "kernel_id": "16981902",
      "votes": 32
    },
    {
      "ref": "rahatreza/simple-randomforest-pipeline",
      "kernel_id": "16824530",
      "votes": 8
    },
    {
      "ref": "abhishekvermasg1/eda-what-do-we-teach-our-successors-v3",
      "kernel_id": "16712916",
      "votes": 3
    },
    {
      "ref": "ayuraj/how-to-use-tf-data-to-train-hf-transformer",
      "kernel_id": "16891217",
      "votes": 18
    },
    {
      "ref": "pawan2905/commonlit-readability-prize",
      "kernel_id": "16967990",
      "votes": 4
    },
    {
      "ref": "manabendrarout/eda-bow-classical-model-with-autokeras-ensemble",
      "kernel_id": "16800681",
      "votes": 13
    },
    {
      "ref": "drdevasiakurian/simple-commonlitreadability-for-beginners",
      "kernel_id": "16785822",
      "votes": 4
    },
    {
      "ref": "houssemayed/exploratory-data-analysis",
      "kernel_id": "16755574",
      "votes": 10
    },
    {
      "ref": "houssemayed/lstm-models-for-regression-on-text-data",
      "kernel_id": "16800669",
      "votes": 54
    },
    {
      "ref": "subinium/how-to-visualize-text-dataset",
      "kernel_id": "16906701",
      "votes": 102
    },
    {
      "ref": "gunesevitan/commonlit-readability-prize-eda",
      "kernel_id": "16747078",
      "votes": 104
    },
    {
      "ref": "abdokamr/starter-eda-baseline-ridge-and-xgboost",
      "kernel_id": "16741417",
      "votes": 30
    },
    {
      "ref": "hiroshihiroshi/low-freq-low-target-and-texteda-with-r",
      "kernel_id": "16809166",
      "votes": 11
    },
    {
      "ref": "vaibhavsxn/bert-fine-tuning-sentence-scoring",
      "kernel_id": "16755932",
      "votes": 8
    },
    {
      "ref": "maunish/clrp-pytorch-train-tpu",
      "kernel_id": "16811762",
      "votes": 34
    },
    {
      "ref": "maunish/clrp-roberta-svm",
      "kernel_id": "16882543",
      "votes": 501
    },
    {
      "ref": "hannes82/commonlit-readability-roberta-simple-baseline",
      "kernel_id": "16702131",
      "votes": 153
    },
    {
      "ref": "hannes82/commonlit-readability-roberta-inference",
      "kernel_id": "16704075",
      "votes": 201
    },
    {
      "ref": "sourabh219/roberta-baseline-model",
      "kernel_id": "16937720",
      "votes": 2
    },
    {
      "ref": "mpwolke/uncommon-literature",
      "kernel_id": "16707936",
      "votes": 33
    },
    {
      "ref": "wanko123/exploring-features-and-model-building",
      "kernel_id": "16824458",
      "votes": 3
    },
    {
      "ref": "udbhavpangotra/commonlit-readibility-prize-extensive-eda-model",
      "kernel_id": "16785196",
      "votes": 32
    },
    {
      "ref": "jitshil143/submission-score-0-62",
      "kernel_id": "16724768",
      "votes": 12
    },
    {
      "ref": "mdfahimreshm/bert-in-depth-understanding",
      "kernel_id": "16777228",
      "votes": 489
    },
    {
      "ref": "gogo827jz/roberta-model-parallel-fold-training-on-tpu",
      "kernel_id": "16782081",
      "votes": 39
    },
    {
      "ref": "takusid/popular-readability-formulas-and-metrics",
      "kernel_id": "16990254",
      "votes": 14
    },
    {
      "ref": "ruchi798/commonlit-readability-prize-eda-baseline",
      "kernel_id": "16701658",
      "votes": 768
    },
    {
      "ref": "muhammad4hmed/commonlit-hierarchical-attention-networks",
      "kernel_id": "16826927",
      "votes": 7
    },
    {
      "ref": "faressayah/transfer-learning-for-natural-language-processing",
      "kernel_id": "16597633",
      "votes": 37
    },
    {
      "ref": "heyytanay/commonlit-eda-understanding-the-competition",
      "kernel_id": "16701635",
      "votes": 65
    },
    {
      "ref": "heyytanay/auto-keras-text-regression-starter",
      "kernel_id": "16703282",
      "votes": 24
    },
    {
      "ref": "heyytanay/submission-pytorch-bert-kfolds-inference",
      "kernel_id": "16714442",
      "votes": 78
    },
    {
      "ref": "heyytanay/training-kfolds-pytorch-bert-large-w-o-oom",
      "kernel_id": "16711855",
      "votes": 234
    },
    {
      "ref": "harshsharma511/pytorch-lightining-roberta",
      "kernel_id": "16900453",
      "votes": 15
    },
    {
      "ref": "harshsharma511/one-stop-understanding-eda-bert-baseline",
      "kernel_id": "16765442",
      "votes": 18
    },
    {
      "ref": "konumaru/baseline-with-linear-regression",
      "kernel_id": "16916587",
      "votes": 4
    },
    {
      "ref": "shivanandmn/bert-pytorch-commonlit-readability-simple",
      "kernel_id": "16707504",
      "votes": 47
    },
    {
      "ref": "hamzaghanmi/crp-eda-preprocessing-lightgbm",
      "kernel_id": "16707020",
      "votes": 16
    },
    {
      "ref": "andreshg/commonlit-a-complete-analysis",
      "kernel_id": "16897787",
      "votes": 132
    },
    {
      "ref": "prajittr/commonlit-dnn-randomforest-linear-regression",
      "kernel_id": "16767185",
      "votes": 35
    },
    {
      "ref": "andradaolteanu/i-commonlit-explore-xgbrf-repeatedfold-model",
      "kernel_id": "16726323",
      "votes": 190
    },
    {
      "ref": "yuto16/grammar-spell-checker",
      "kernel_id": "16810135",
      "votes": 10
    },
    {
      "ref": "yuto16/target-standard-error",
      "kernel_id": "16758436",
      "votes": 7
    },
    {
      "ref": "louise2001/nltk-commonlit-starter-notebook",
      "kernel_id": "16703570",
      "votes": 23
    },
    {
      "ref": "louise2001/rapids-sentenceembeddings-for-regression",
      "kernel_id": "16704293",
      "votes": 23
    },
    {
      "ref": "dpaluszk/commonlit-cannot-submit-submission-csv-not-found",
      "kernel_id": "16729307",
      "votes": 10
    },
    {
      "ref": "gsabbih/news-categorisation",
      "kernel_id": "16253193",
      "votes": 0
    },
    {
      "ref": "tchaye59/clreadability-tf-bert-baseline",
      "kernel_id": "16758071",
      "votes": 9
    },
    {
      "ref": "tchaye59/download-tensorflow-text-tf-models-official",
      "kernel_id": "16746260",
      "votes": 6
    },
    {
      "ref": "tchaye59/tfhub-berts-download",
      "kernel_id": "16747216",
      "votes": 2
    },
    {
      "ref": "zefirchik/start-bert-models",
      "kernel_id": "16856664",
      "votes": 7
    },
    {
      "ref": "sumeetsawant/commonlit-readability-ridge-baseline-0-7",
      "kernel_id": "16894953",
      "votes": 1
    },
    {
      "ref": "ochaaaaaaan/commonlit-doc2vec-lightgbm",
      "kernel_id": "16952172",
      "votes": 8
    },
    {
      "ref": "abhibasavapattana/tensorflow-glove6b-lit-readability",
      "kernel_id": "16762359",
      "votes": 12
    },
    {
      "ref": "anandsm7/commonlit-roberta-training-inference",
      "kernel_id": "16724308",
      "votes": 5
    },
    {
      "ref": "rushinaik/base-model",
      "kernel_id": "16709830",
      "votes": 8
    },
    {
      "ref": "rushinaik/bigwords",
      "kernel_id": "16982512",
      "votes": 10
    },
    {
      "ref": "xinruizhan/pretrained-bert-nn",
      "kernel_id": "16927682",
      "votes": 0
    },
    {
      "ref": "chumajin/how-to-initialize-the-code-correctly-english",
      "kernel_id": "16935823",
      "votes": 69
    },
    {
      "ref": "chumajin/pytorch-bert-beginner-s-room-version",
      "kernel_id": "16868748",
      "votes": 329
    },
    {
      "ref": "chumajin/inference-for-pytorch-bert-beginner-s-room",
      "kernel_id": "16881616",
      "votes": 68
    },
    {
      "ref": "chumajin/pytorch-bert-beginner-s-room",
      "kernel_id": "16876642",
      "votes": 270
    },
    {
      "ref": "maksymshkliarevskyi/commlit-readability-start-with-r-eda-nlp-lstm",
      "kernel_id": "16813960",
      "votes": 46
    },
    {
      "ref": "vislupus/nlp-commonlit-readability",
      "kernel_id": "16748739",
      "votes": 3
    },
    {
      "ref": "bharadwajvedula/lb-0-683-tf-glove840b",
      "kernel_id": "16881143",
      "votes": 3
    },
    {
      "ref": "magdawjcicka/commonlit-readability-data-exploration-nn",
      "kernel_id": "16913180",
      "votes": 4
    },
    {
      "ref": "anaverageengineer/comlrp-baseline-for-complete-beginners",
      "kernel_id": "16705167",
      "votes": 29
    },
    {
      "ref": "yasserhessein/commonlit-eda-gru-glove-840b-300d",
      "kernel_id": "16804950",
      "votes": 17
    },
    {
      "ref": "sauravmaheshkar/commonlit-tensorflow-weights-biases",
      "kernel_id": "16867002",
      "votes": 16
    },
    {
      "ref": "artemglazunov1990/roberta-save",
      "kernel_id": "16780932",
      "votes": 1
    },
    {
      "ref": "pranav082001/commonlit-pytorch-bert-starter",
      "kernel_id": "16990975",
      "votes": 4
    },
    {
      "ref": "pranav082001/commonlit-simple-ensemble-models-ml",
      "kernel_id": "16834232",
      "votes": 6
    },
    {
      "ref": "nicolasalerno/smote-classifiers-dt-rf-xgb-stacking-ensemble",
      "kernel_id": "16720080",
      "votes": 15
    },
    {
      "ref": "datafan07/eda-simple-bayesian-ridge-with-sentence-embeddings",
      "kernel_id": "16764101",
      "votes": 218
    },
    {
      "ref": "thomaskonstantin/commonlit-feature-engineering-age-range-bert",
      "kernel_id": "16892082",
      "votes": 30
    },
    {
      "ref": "thomaskonstantin/commonlit-text-analysis-baseline-modeling",
      "kernel_id": "16798675",
      "votes": 42
    },
    {
      "ref": "josephassaker/commonlit-readability-eda-na-ve-submission",
      "kernel_id": "16705061",
      "votes": 14
    },
    {
      "ref": "kshitijmohan/commonlit-readability-bert-train-inference",
      "kernel_id": "16743478",
      "votes": 26
    },
    {
      "ref": "manishkc06/text-pre-processing-data-wrangling",
      "kernel_id": "16726083",
      "votes": 57
    },
    {
      "ref": "aishwarya2490/commonlit-readability-prize-eda-cleaning-data",
      "kernel_id": "16010623",
      "votes": 13
    },
    {
      "ref": "debarshichanda/pytorch-commonlit-readability-bert-baseline",
      "kernel_id": "16707379",
      "votes": 98
    },
    {
      "ref": "debarshichanda/explore-t5",
      "kernel_id": "16791397",
      "votes": 116
    },
    {
      "ref": "rxyfng/readabilitycontest",
      "kernel_id": "16841873",
      "votes": 1
    },
    {
      "ref": "sourabhy/common-lit-cnn-model",
      "kernel_id": "16797995",
      "votes": 9
    },
    {
      "ref": "sourabhy/commonlit-roberta-cnn",
      "kernel_id": "16956815",
      "votes": 59
    },
    {
      "ref": "ravillatejakumar/fast-ml",
      "kernel_id": "16858133",
      "votes": 13
    },
    {
      "ref": "shreyasajal/pytorch-openai-gpt2-commonlit-readability",
      "kernel_id": "16946814",
      "votes": 43
    },
    {
      "ref": "shreyasajal/pytorch-bert-baseline-lr-schedulers-guide",
      "kernel_id": "16896181",
      "votes": 82
    },
    {
      "ref": "hantianluo/huggingface-autonlp",
      "kernel_id": "16907462",
      "votes": 4
    },
    {
      "ref": "jinhangjiang/commonlit-readability-sbert-dl-jiang",
      "kernel_id": "16713591",
      "votes": 2
    },
    {
      "ref": "utcarshagrawal/commonlit-eda-most-nlp-techniques",
      "kernel_id": "16764463",
      "votes": 33
    },
    {
      "ref": "threesheds/simple-bow-baseline",
      "kernel_id": "16957489",
      "votes": 2
    },
    {
      "ref": "rushikeshdarge/commonlit-using-neural-network",
      "kernel_id": "16944930",
      "votes": 1
    },
    {
      "ref": "rushikeshdarge/easiest-approach-with-good-rmse",
      "kernel_id": "16944193",
      "votes": 8
    },
    {
      "ref": "darknesszx/roberta-simple-roberta-large-baseline",
      "kernel_id": "16854346",
      "votes": 13
    },
    {
      "ref": "vivekprajapati2048/a-complete-commonlit-readability-prize-nb",
      "kernel_id": "16762894",
      "votes": 5
    },
    {
      "ref": "raghavjha858/bert-nlp-eda",
      "kernel_id": "16832786",
      "votes": 1
    },
    {
      "ref": "kritanjalijain/commonlit-eda-and-lstm",
      "kernel_id": "16757125",
      "votes": 17
    },
    {
      "ref": "mghfarahani/commonlit-readability-prize-analysis-prediction",
      "kernel_id": "16933246",
      "votes": 6
    },
    {
      "ref": "alaasedeeq/commonlit-readability-eda",
      "kernel_id": "16708691",
      "votes": 36
    },
    {
      "ref": "riteshpatil8998/commonlit-readability-cross-validation",
      "kernel_id": "16755098",
      "votes": 21
    },
    {
      "ref": "dearsijie/commonlit-readability-tf-lstm-tune-hyperparameters",
      "kernel_id": "16724936",
      "votes": 11
    },
    {
      "ref": "ekaterinadranitsyna/text-readability-score",
      "kernel_id": "16986731",
      "votes": 6
    },
    {
      "ref": "davidjlochner/commonlit-elastic-net",
      "kernel_id": "16894778",
      "votes": 10
    },
    {
      "ref": "haiyunhu/commonlitreadabilityprize-train",
      "kernel_id": "16744793",
      "votes": 4
    },
    {
      "ref": "yoshitakayoshimura/yy-notebook",
      "kernel_id": "16973026",
      "votes": 0
    },
    {
      "ref": "hayahiko/ridge-10folds-step-by-step",
      "kernel_id": "16704458",
      "votes": 13
    },
    {
      "ref": "ash112/commonlit-readability-gbr",
      "kernel_id": "16951713",
      "votes": 2
    },
    {
      "ref": "ammarnassanalhajali/commonlit-readability-eda",
      "kernel_id": "16802718",
      "votes": 36
    },
    {
      "ref": "ttjccc/gaussian-multi-target-regularization",
      "kernel_id": "16994860",
      "votes": 3
    },
    {
      "ref": "gaetanlopez/pytorch-roberta-inference",
      "kernel_id": "16729367",
      "votes": 15
    },
    {
      "ref": "raoumair98/bogog",
      "kernel_id": "16836271",
      "votes": 2
    },
    {
      "ref": "ravishah1/readability-feature-engineering-non-nn-baseline",
      "kernel_id": "16729750",
      "votes": 188
    },
    {
      "ref": "amontgomerie/baseline-with-features-from-textstat-and-spacy",
      "kernel_id": "16875010",
      "votes": 15
    },
    {
      "ref": "leontrout/useful",
      "kernel_id": "16767641",
      "votes": 2
    },
    {
      "ref": "alvarofbudria/commonlit-readability",
      "kernel_id": "16721677",
      "votes": 1
    },
    {
      "ref": "kazu02/commonlitreadabilityprize-model1",
      "kernel_id": "16795797",
      "votes": 5
    },
    {
      "ref": "tobiasgabrielkroll/readability",
      "kernel_id": "16745340",
      "votes": 5
    },
    {
      "ref": "semyonkoshkarov/tf-idf-linearsvr-baseline",
      "kernel_id": "16721695",
      "votes": 15
    },
    {
      "ref": "shatakshiraman/commnlit-eda",
      "kernel_id": "16720243",
      "votes": 2
    },
    {
      "ref": "roguecomputer/commonlit-readability-eda-plotly-word-cloud",
      "kernel_id": "16720990",
      "votes": 2
    },
    {
      "ref": "hiroyanakamura/notebook085d9d0f67",
      "kernel_id": "16708682",
      "votes": 2
    },
    {
      "ref": "solorzano/comparison-of-huggingface-text-model-features",
      "kernel_id": "17252895",
      "votes": 10
    },
    {
      "ref": "solorzano/clrp-roberta-ridge",
      "kernel_id": "17477023",
      "votes": 43
    },
    {
      "ref": "konradb/linear-av",
      "kernel_id": "17455608",
      "votes": 1
    },
    {
      "ref": "sumantindurkhya/commonlit-baseline-tf-idf",
      "kernel_id": "17137710",
      "votes": 5
    },
    {
      "ref": "sumantindurkhya/bert-for-regression",
      "kernel_id": "17124727",
      "votes": 150
    },
    {
      "ref": "sumantindurkhya/commonlit-lstm-with-attention",
      "kernel_id": "17160374",
      "votes": 2
    },
    {
      "ref": "sumantindurkhya/commonlit-baseline-doc2vec",
      "kernel_id": "17161681",
      "votes": 3
    },
    {
      "ref": "akabaev/baseline-tf-distilbert",
      "kernel_id": "17984903",
      "votes": 1
    },
    {
      "ref": "johnhawkins/texturizer-lightgbm",
      "kernel_id": "17319877",
      "votes": 0
    },
    {
      "ref": "gilfernandes/commonlit-pytorch-distilbart-svm",
      "kernel_id": "17461766",
      "votes": 5
    },
    {
      "ref": "aidenkim/commonlit-fastai-baseline",
      "kernel_id": "17995716",
      "votes": 0
    },
    {
      "ref": "yassinealouini/how-to-huggingface",
      "kernel_id": "17101180",
      "votes": 1
    },
    {
      "ref": "crained/prep-data-with-keras-for-commonlit",
      "kernel_id": "17013032",
      "votes": 17
    },
    {
      "ref": "pavelvod/transformer-cnn-lstm-attention-head-inference",
      "kernel_id": "17019273",
      "votes": 16
    },
    {
      "ref": "grafael/beyond-the-excerpt",
      "kernel_id": "17387319",
      "votes": 0
    },
    {
      "ref": "shrutisaxena/commonlit-using-h2o-flow",
      "kernel_id": "17167596",
      "votes": 12
    },
    {
      "ref": "jamesmcguigan/commonlit-xgbooost",
      "kernel_id": "17638762",
      "votes": 5
    },
    {
      "ref": "tomwarrens/commonlit-readability-prize-eda",
      "kernel_id": "17037429",
      "votes": 57
    },
    {
      "ref": "pehahn/textfeatures-in-r",
      "kernel_id": "17121316",
      "votes": 11
    },
    {
      "ref": "pehahn/basic-bert-with-r",
      "kernel_id": "17379997",
      "votes": 23
    },
    {
      "ref": "evilmage93/commonlit-bert-tuned-svr",
      "kernel_id": "17312398",
      "votes": 9
    },
    {
      "ref": "indranilbhattacharya/tsne-of-bert-embeddings-3d-plots",
      "kernel_id": "17104432",
      "votes": 7
    },
    {
      "ref": "anjum48/commonlit-submission",
      "kernel_id": "17498222",
      "votes": 13
    },
    {
      "ref": "vigneshbaskaran/commonlit-derive-more-features-from-transformer",
      "kernel_id": "17247285",
      "votes": 15
    },
    {
      "ref": "vigneshbaskaran/commonlit-pt-vs-tf",
      "kernel_id": "17463054",
      "votes": 1
    },
    {
      "ref": "vigneshbaskaran/commonlit-a1-pure-scikit-learn",
      "kernel_id": "17006024",
      "votes": 8
    },
    {
      "ref": "vigneshbaskaran/commonlit-spacy-with-ridge-regression",
      "kernel_id": "17081182",
      "votes": 5
    },
    {
      "ref": "vigneshbaskaran/commonlit-pytorch-vs-sklearn-regression",
      "kernel_id": "17098073",
      "votes": 9
    },
    {
      "ref": "vigneshbaskaran/commonlit-sentencetransformer-sklearn-regression",
      "kernel_id": "17219552",
      "votes": 0
    },
    {
      "ref": "vigneshbaskaran/commonlit-text-comparator-rmse-vs-ranking-loss",
      "kernel_id": "17125613",
      "votes": 35
    },
    {
      "ref": "vigneshbaskaran/imdb-simple-bert-finetuner",
      "kernel_id": "17549429",
      "votes": 1
    },
    {
      "ref": "vigneshbaskaran/commonlit-easy-finetuner-inference",
      "kernel_id": "17638004",
      "votes": 7
    },
    {
      "ref": "vigneshbaskaran/commonlit-easy-transformer-finetuner",
      "kernel_id": "17638670",
      "votes": 11
    },
    {
      "ref": "vigneshbaskaran/commonlit-why-my-transformer-is-not-good-enough",
      "kernel_id": "17658624",
      "votes": 2
    },
    {
      "ref": "vigneshbaskaran/commonlit-halftime-recap-of-my-transformer-journey",
      "kernel_id": "17809316",
      "votes": 30
    },
    {
      "ref": "vigneshbaskaran/commonlit-inference-is-my-transformer-good",
      "kernel_id": "17776146",
      "votes": 8
    },
    {
      "ref": "vigneshbaskaran/commonlit-making-my-transformer-good-enough",
      "kernel_id": "17778889",
      "votes": 23
    },
    {
      "ref": "dsquareindia/sbert-lstm",
      "kernel_id": "17333698",
      "votes": 3
    },
    {
      "ref": "dsquareindia/roberta-svm-pytorch-lightning",
      "kernel_id": "17745549",
      "votes": 1
    },
    {
      "ref": "dsquareindia/commonlit-simple-pytorch-lightning",
      "kernel_id": "17507678",
      "votes": 0
    },
    {
      "ref": "kirichenko17roman/dl-lab-15-feature-engineering-in-nlp",
      "kernel_id": "17744576",
      "votes": 2
    },
    {
      "ref": "kirichenko17roman/dl-lab-14-sequence-classification-regression",
      "kernel_id": "17644612",
      "votes": 5
    },
    {
      "ref": "kirichenko17roman/dl-lab-17-lstm-for-nlp",
      "kernel_id": "17784753",
      "votes": 1
    },
    {
      "ref": "kirichenko17roman/hanet-regression",
      "kernel_id": "17797379",
      "votes": 1
    },
    {
      "ref": "takoihiraokazu/ex015-lgb",
      "kernel_id": "17244105",
      "votes": 6
    },
    {
      "ref": "takoihiraokazu/ex040-svr",
      "kernel_id": "17322000",
      "votes": 5
    },
    {
      "ref": "takoihiraokazu/fe001-step-1-create-folds",
      "kernel_id": "17218847",
      "votes": 4
    },
    {
      "ref": "takoihiraokazu/ex014-roberta",
      "kernel_id": "17240879",
      "votes": 6
    },
    {
      "ref": "rsesha/commonlit-keras-nnlm-tfhub",
      "kernel_id": "17475367",
      "votes": 5
    },
    {
      "ref": "tusonggao/clrp-pytorch-roberta-base-my-first-model-train",
      "kernel_id": "17932965",
      "votes": 7
    },
    {
      "ref": "tusonggao/clrp-pytorch-roberta-base-my-first-model-inference",
      "kernel_id": "17956445",
      "votes": 7
    },
    {
      "ref": "tusonggao/clrp-roberta-base-my-first-model-5fold-cv",
      "kernel_id": "17980580",
      "votes": 8
    },
    {
      "ref": "lautenschlager/glove-preprocessing-feel-free-to-use",
      "kernel_id": "17125725",
      "votes": 2
    },
    {
      "ref": "smsrikanthreddy/commonlit-non-transformers-models",
      "kernel_id": "17321207",
      "votes": 9
    },
    {
      "ref": "smsrikanthreddy/commonlit-eda-ml-baseline",
      "kernel_id": "17315489",
      "votes": 1
    },
    {
      "ref": "evansimpson/eda-for-commonlit-reading-prize",
      "kernel_id": "17697193",
      "votes": 20
    },
    {
      "ref": "abhilashreddyy/inference-transformer-model-using-hugging-face",
      "kernel_id": "17031461",
      "votes": 3
    },
    {
      "ref": "abhilashreddyy/0-6-rmse-finetuning-transformer-for-dummies",
      "kernel_id": "17013407",
      "votes": 6
    },
    {
      "ref": "annbogdanova/readability-feature-engineering",
      "kernel_id": "17654467",
      "votes": 8
    },
    {
      "ref": "lhagiimn/clrp-ensemble-public-notebooks",
      "kernel_id": "17581412",
      "votes": 346
    },
    {
      "ref": "kunalman/clrc-eda-v0-1",
      "kernel_id": "17016897",
      "votes": 0
    },
    {
      "ref": "socathie/basic-centroid-w-fasttext",
      "kernel_id": "17754235",
      "votes": 1
    },
    {
      "ref": "bryanb/first-approach-using-xgboost-with-hyperopt",
      "kernel_id": "17465130",
      "votes": 12
    },
    {
      "ref": "shivarama/inferencebert",
      "kernel_id": "17735114",
      "votes": 1
    },
    {
      "ref": "shivarama/samplebert",
      "kernel_id": "17705928",
      "votes": 1
    },
    {
      "ref": "ejmejm/commonlit-bert-submission-video-tutorial",
      "kernel_id": "17342501",
      "votes": 55
    },
    {
      "ref": "ejmejm/commonlit-eda-video-tutorial",
      "kernel_id": "17341345",
      "votes": 16
    },
    {
      "ref": "yuichihiguchi/commonlit-readability-prize-eda",
      "kernel_id": "17713152",
      "votes": 17
    },
    {
      "ref": "narendra/commonlit-gru-tabular-submision",
      "kernel_id": "17258691",
      "votes": 0
    },
    {
      "ref": "narendra/commonlit-masking-word-submission",
      "kernel_id": "17128832",
      "votes": 0
    },
    {
      "ref": "narendra/commonlit-glove-gru-submission",
      "kernel_id": "17182943",
      "votes": 3
    },
    {
      "ref": "narendra/commonlit-glove-gru",
      "kernel_id": "17175001",
      "votes": 17
    },
    {
      "ref": "narendra/commonlit-mask-rare-word-model-glove-submission",
      "kernel_id": "17164839",
      "votes": 0
    },
    {
      "ref": "narendra/commonlit-word-proportion-lda-baseline",
      "kernel_id": "17079006",
      "votes": 3
    },
    {
      "ref": "vikasvikkyd/commonlit",
      "kernel_id": "17041543",
      "votes": 0
    },
    {
      "ref": "shahules/guide-pytorch-data-samplers-sequence-bucketing",
      "kernel_id": "17976097",
      "votes": 127
    },
    {
      "ref": "qinhui1999/training-tf-roberta-large-on-mlm",
      "kernel_id": "17487946",
      "votes": 37
    },
    {
      "ref": "rerere/commonlit-readability-prize-lstm-approach-tf-idf",
      "kernel_id": "17271132",
      "votes": 1
    },
    {
      "ref": "rerere/commonlit-readability-prize-tf-idf-svm",
      "kernel_id": "17244868",
      "votes": 2
    },
    {
      "ref": "rerere/commonlit-readability-prize-roberta-approach",
      "kernel_id": "17587328",
      "votes": 2
    },
    {
      "ref": "willrice/commonlit-bert-stratified-k-fold-baseline-train",
      "kernel_id": "17408052",
      "votes": 4
    },
    {
      "ref": "nullpo20170707/word2vec-lightgbm",
      "kernel_id": "17872545",
      "votes": 1
    },
    {
      "ref": "shaz13/augment-at-word-level-and-help-your-nlp-model",
      "kernel_id": "17086662",
      "votes": 23
    },
    {
      "ref": "shaz13/using-commonlit-augmented-data-set",
      "kernel_id": "17096433",
      "votes": 11
    },
    {
      "ref": "shaz13/code-how-bradley-terry-model-works",
      "kernel_id": "17145656",
      "votes": 108
    },
    {
      "ref": "sapal6/common-lit-training",
      "kernel_id": "17273306",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-dataaug",
      "kernel_id": "17883080",
      "votes": 1
    },
    {
      "ref": "donkeys/pip-install-package-from-dataset-no-internet",
      "kernel_id": "17482722",
      "votes": 8
    },
    {
      "ref": "craving1030/caret-and-simple-text-fe-of-commonlit-readability",
      "kernel_id": "17157435",
      "votes": 7
    },
    {
      "ref": "getitdone/commonlit-word-to-vec-with-umap",
      "kernel_id": "17357120",
      "votes": 3
    },
    {
      "ref": "getitdone/beginners-friendly-notebook",
      "kernel_id": "17140168",
      "votes": 90
    },
    {
      "ref": "nageen94/commonlit-stacking-regressors",
      "kernel_id": "17976651",
      "votes": 0
    },
    {
      "ref": "rhtsingh/on-stability-of-few-sample-transformer-fine-tuning",
      "kernel_id": "17565100",
      "votes": 461
    },
    {
      "ref": "rhtsingh/utilizing-transformer-representations-efficiently",
      "kernel_id": "17873764",
      "votes": 1248
    },
    {
      "ref": "rhtsingh/speeding-up-transformer-w-optimization-strategies",
      "kernel_id": "17332743",
      "votes": 408
    },
    {
      "ref": "rhtsingh/commonlit-readability-prize-roberta-torch-infer-3",
      "kernel_id": "17289679",
      "votes": 626
    },
    {
      "ref": "rhtsingh/commonlit-readability-prize-roberta-torch-infer-2",
      "kernel_id": "17167790",
      "votes": 276
    },
    {
      "ref": "rhtsingh/two-roberta-s-are-better-than-one-0-469",
      "kernel_id": "17221122",
      "votes": 288
    },
    {
      "ref": "rhtsingh/commonlit-readability-prize-roberta-torch-fit",
      "kernel_id": "17133119",
      "votes": 624
    },
    {
      "ref": "rhtsingh/commonlit-readability-prize-roberta-torch-itpt",
      "kernel_id": "17133150",
      "votes": 725
    },
    {
      "ref": "rhtsingh/guide-to-huggingface-schedulers-differential-lrs",
      "kernel_id": "17143996",
      "votes": 222
    },
    {
      "ref": "rhtsingh/commonlit-readability-prize-roberta-torch-infer",
      "kernel_id": "17052686",
      "votes": 507
    },
    {
      "ref": "kumarsrikant/commonlit-readability-nlp-guide-for-beginners",
      "kernel_id": "17934254",
      "votes": 9
    },
    {
      "ref": "thedrcat/commonlit-what-are-we-reading-about",
      "kernel_id": "17084817",
      "votes": 67
    },
    {
      "ref": "thedrcat/commonlit-2-computer-vision-approach-really",
      "kernel_id": "17389521",
      "votes": 8
    },
    {
      "ref": "thedrcat/commonlit-train-average-baseline",
      "kernel_id": "17437455",
      "votes": 6
    },
    {
      "ref": "thedrcat/commonlit-3-computer-vision-inference",
      "kernel_id": "17437794",
      "votes": 10
    },
    {
      "ref": "thedrcat/commonlit-1-generate-pictures-really",
      "kernel_id": "17341549",
      "votes": 11
    },
    {
      "ref": "thedrcat/commonlit-hf-trainer-hyperparameter-tuning",
      "kernel_id": "17573128",
      "votes": 14
    },
    {
      "ref": "thedrcat/commonlit-25-distilbert-models",
      "kernel_id": "17617150",
      "votes": 8
    },
    {
      "ref": "thedrcat/commonlit-hf-minimalistic-example",
      "kernel_id": "17539006",
      "votes": 38
    },
    {
      "ref": "thedrcat/commonlit-readability-feature-importances",
      "kernel_id": "17656832",
      "votes": 13
    },
    {
      "ref": "duttadebadri/eda-basline-modeling-commonlit-passages",
      "kernel_id": "17129808",
      "votes": 58
    },
    {
      "ref": "shivamanhar/nltk-data-extraction-29062021",
      "kernel_id": "17930687",
      "votes": 0
    },
    {
      "ref": "wecdecqw/commonlith-bert",
      "kernel_id": "17630244",
      "votes": 1
    },
    {
      "ref": "shashwatwork/commonlit-the-topic-modelling-approach-with-py",
      "kernel_id": "17199319",
      "votes": 8
    },
    {
      "ref": "osblouf/external-data-for-model-pretraining",
      "kernel_id": "17876259",
      "votes": 8
    },
    {
      "ref": "jnegrini/commonlit-readability-nlp-eda-and-initial-model",
      "kernel_id": "17065552",
      "votes": 26
    },
    {
      "ref": "takamichitoda/commonlit-classical-methods-for-text-readability",
      "kernel_id": "17541488",
      "votes": 27
    },
    {
      "ref": "takamichitoda/commonlit-probing",
      "kernel_id": "17903498",
      "votes": 13
    },
    {
      "ref": "simoneazeglio/word2vec-umap-w2vaugmentation",
      "kernel_id": "17045357",
      "votes": 15
    },
    {
      "ref": "mikelovesrobots/substantial-book-club-readability-prize",
      "kernel_id": "17362014",
      "votes": 1
    },
    {
      "ref": "deb009/commonlit-readability-prize-using-bert",
      "kernel_id": "17117719",
      "votes": 12
    },
    {
      "ref": "shloksah/roberta-embedding-bayesian-ridge-using-tf",
      "kernel_id": "17424452",
      "votes": 3
    },
    {
      "ref": "prvnkmr/tf-bert-baseline-lb-0-646-inference",
      "kernel_id": "17016755",
      "votes": 2
    },
    {
      "ref": "sergioli212/clrp-pytorch-train-clean",
      "kernel_id": "17136624",
      "votes": 1
    },
    {
      "ref": "sergioli212/tutorial-text-processing-using-python-and-nltk",
      "kernel_id": "17888681",
      "votes": 1
    },
    {
      "ref": "sergioli212/clrp-try-allennlp-api-for-plm",
      "kernel_id": "17489751",
      "votes": 2
    },
    {
      "ref": "kanbehmw/pytorch-weebit-dataset-fine-tuning-train-kfold",
      "kernel_id": "17465360",
      "votes": 6
    },
    {
      "ref": "revathiprakash/commonlit-readability-eda-kit-wip",
      "kernel_id": "17129647",
      "votes": 1
    },
    {
      "ref": "lars123/neural-tangent-kernel-2",
      "kernel_id": "17033942",
      "votes": 111
    },
    {
      "ref": "lars123/neural-tangent-kernel",
      "kernel_id": "17017758",
      "votes": 30
    },
    {
      "ref": "alphadraco/commonlit-readability-prize-roberta-torch-itpt",
      "kernel_id": "17586923",
      "votes": 1
    },
    {
      "ref": "jcesquiveld/roberta-large-5-fold-single-model-meanpooling",
      "kernel_id": "17705703",
      "votes": 374
    },
    {
      "ref": "sharrpie/commonlit-readability-baseline",
      "kernel_id": "17383299",
      "votes": 2
    },
    {
      "ref": "fabriciotorquato/commonlit-readability-prize",
      "kernel_id": "17310633",
      "votes": 7
    },
    {
      "ref": "duboisian/first-draft-model",
      "kernel_id": "17042891",
      "votes": 5
    },
    {
      "ref": "tarandeep97/performance-comparison-of-regression-models",
      "kernel_id": "17661949",
      "votes": 55
    },
    {
      "ref": "vijendersingh412/classification-with-tensorflow-and-use-large",
      "kernel_id": "17266319",
      "votes": 1
    },
    {
      "ref": "vijendersingh412/hugging-face-model-roberta-large-with-svm",
      "kernel_id": "17656111",
      "votes": 4
    },
    {
      "ref": "tealgreen0503/the-simplest-stratifiedkfold-for-regression",
      "kernel_id": "17008406",
      "votes": 7
    },
    {
      "ref": "coldog/notebook4submit",
      "kernel_id": "17440910",
      "votes": 1
    },
    {
      "ref": "w1023672708/tfidf-xgboost",
      "kernel_id": "17861806",
      "votes": 11
    },
    {
      "ref": "w1023672708/fine-tuning-bert-using-transformers",
      "kernel_id": "17923059",
      "votes": 10
    },
    {
      "ref": "ashutoshiitg/clrp-roberta-train",
      "kernel_id": "17104603",
      "votes": 4
    },
    {
      "ref": "ankitp013/exploratory-data-analysis-lstm-gru",
      "kernel_id": "17354015",
      "votes": 16
    },
    {
      "ref": "mobassir/readability-tensorflow-roberta-training-baseline",
      "kernel_id": "17577954",
      "votes": 47
    },
    {
      "ref": "mobassir/commonlit-readability-tensorflow-torch-ensemble",
      "kernel_id": "17751472",
      "votes": 292
    },
    {
      "ref": "eneszvo/bert-roberta-distilbert-ensemble-5-fold-cv",
      "kernel_id": "17276715",
      "votes": 37
    },
    {
      "ref": "raymishra/commonlit-building-from-the-base",
      "kernel_id": "17506647",
      "votes": 0
    },
    {
      "ref": "ktgiahieu/bert-base-readability",
      "kernel_id": "17749650",
      "votes": 1
    },
    {
      "ref": "shinomoriaoshi/readability-data-augmentation",
      "kernel_id": "17066763",
      "votes": 5
    },
    {
      "ref": "shinomoriaoshi/readability-roberta-large-oof",
      "kernel_id": "17327844",
      "votes": 3
    },
    {
      "ref": "shinomoriaoshi/readability-roberta-large-inference",
      "kernel_id": "17371717",
      "votes": 4
    },
    {
      "ref": "krishnagk/notebookcommonlitlinearregression",
      "kernel_id": "17576883",
      "votes": 6
    },
    {
      "ref": "dagnelies/kiss-roberta",
      "kernel_id": "17216777",
      "votes": 32
    },
    {
      "ref": "javi23ruiz/sbert-feature-engineering",
      "kernel_id": "17372681",
      "votes": 5
    },
    {
      "ref": "kumapo/bert-baseline-training-validation",
      "kernel_id": "17006435",
      "votes": 0
    },
    {
      "ref": "stpeteishii/commonlit-readability-keras-conv1d",
      "kernel_id": "17362784",
      "votes": 3
    },
    {
      "ref": "stpeteishii/commonlit-readability-keras-bidirectional",
      "kernel_id": "17365465",
      "votes": 3
    },
    {
      "ref": "stpeteishii/commonlit-readability-bert-xgboost",
      "kernel_id": "17321278",
      "votes": 7
    },
    {
      "ref": "garuna1234/notebooka455286c9a",
      "kernel_id": "17136669",
      "votes": 11
    },
    {
      "ref": "matthewfranglen/kaggle-workshop-script",
      "kernel_id": "17191284",
      "votes": 0
    },
    {
      "ref": "salikhussaini49/text-difficulty-prediction",
      "kernel_id": "17616796",
      "votes": 0
    },
    {
      "ref": "muhammad2421/transformers-pytorch",
      "kernel_id": "17170318",
      "votes": 3
    },
    {
      "ref": "soumochatterjee/discover-difficulty-with-distill-bert",
      "kernel_id": "17283850",
      "votes": 0
    },
    {
      "ref": "soumochatterjee/discover-difficulty-with-bert2",
      "kernel_id": "17290243",
      "votes": 0
    },
    {
      "ref": "soumochatterjee/discover-difficulty-with-xlnet",
      "kernel_id": "17313820",
      "votes": 2
    },
    {
      "ref": "soumochatterjee/discover-difficulty-with-roberta",
      "kernel_id": "17493621",
      "votes": 2
    },
    {
      "ref": "hiroshihiroshi/word-length-and-tf-idf-has-negative-correlation",
      "kernel_id": "17128824",
      "votes": 3
    },
    {
      "ref": "hiroshihiroshi/sort-your-books-by-genre-topicmodel-r",
      "kernel_id": "17255867",
      "votes": 13
    },
    {
      "ref": "abhishekb0210/commonlit-eda-initial",
      "kernel_id": "17544744",
      "votes": 1
    },
    {
      "ref": "terrorblade1/bertmo",
      "kernel_id": "17435570",
      "votes": 2
    },
    {
      "ref": "terrorblade1/bert-lgb-whynotwork",
      "kernel_id": "17467619",
      "votes": 1
    },
    {
      "ref": "maunish/clrp-pytorch-roberta-finetune-tpu",
      "kernel_id": "17620938",
      "votes": 129
    },
    {
      "ref": "maunish/clrp-pytorch-roberta-inference",
      "kernel_id": "17506126",
      "votes": 161
    },
    {
      "ref": "maunish/clrp-pytorch-roberta-finetune",
      "kernel_id": "17398807",
      "votes": 282
    },
    {
      "ref": "maunish/clrp-pytorch-roberta-pretrain",
      "kernel_id": "17374041",
      "votes": 436
    },
    {
      "ref": "artemzapara/commonlit-readability-prize-eda",
      "kernel_id": "17372291",
      "votes": 1
    },
    {
      "ref": "shanelindh/basic-eda-of-commonlitreadbility-data",
      "kernel_id": "17317140",
      "votes": 4
    },
    {
      "ref": "rubiales/scraping-data-augmentation",
      "kernel_id": "17406693",
      "votes": 6
    },
    {
      "ref": "cwwong1606/clrp-inference",
      "kernel_id": "17074381",
      "votes": 0
    },
    {
      "ref": "ajax0564/training-tf-roberta-on-mlm",
      "kernel_id": "17247331",
      "votes": 21
    },
    {
      "ref": "wmanka/roll-your-own-rnn-pytorch",
      "kernel_id": "17273282",
      "votes": 22
    },
    {
      "ref": "mpwolke/dataprep-clean-literature",
      "kernel_id": "17447957",
      "votes": 73
    },
    {
      "ref": "wanko123/bert-with-targets-and-errors-using-trained-outside",
      "kernel_id": "17659006",
      "votes": 6
    },
    {
      "ref": "wanko123/simple-bert-with-targets-and-errors-2",
      "kernel_id": "17476117",
      "votes": 3
    },
    {
      "ref": "wanko123/simple-bert-cnn-with-targets-and-errors",
      "kernel_id": "17001153",
      "votes": 8
    },
    {
      "ref": "wanko123/roberta-cnn-with-target-and-error",
      "kernel_id": "17101856",
      "votes": 20
    },
    {
      "ref": "bacterio/2-bd-ia-directo-env-o-de-referencia",
      "kernel_id": "17967442",
      "votes": 2
    },
    {
      "ref": "bacterio/1-bd-ia-directo-eda",
      "kernel_id": "17965867",
      "votes": 0
    },
    {
      "ref": "donmarch14/commonlit-detailed-guide-to-learn-nlp",
      "kernel_id": "17188922",
      "votes": 154
    },
    {
      "ref": "gogo827jz/optimise-blending-weights-demo",
      "kernel_id": "17903263",
      "votes": 6
    },
    {
      "ref": "eiann1509/commonlit-fine-tuning-with-roberta-base",
      "kernel_id": "17725803",
      "votes": 10
    },
    {
      "ref": "th3niko/keras-bilsmt",
      "kernel_id": "17961582",
      "votes": 5
    },
    {
      "ref": "linainversez/bradley-terry",
      "kernel_id": "17381241",
      "votes": 0
    },
    {
      "ref": "pavelpds1/readability-fcnn-embedding",
      "kernel_id": "17077327",
      "votes": 2
    },
    {
      "ref": "accountstatus/readability-index-in-python",
      "kernel_id": "17261899",
      "votes": 36
    },
    {
      "ref": "kayvanshah/commonlit-readability-roberta-tf-inference",
      "kernel_id": "17486019",
      "votes": 3
    },
    {
      "ref": "kayvanshah/commonlit-readability-roberta-tf",
      "kernel_id": "17468511",
      "votes": 5
    },
    {
      "ref": "chamecall/train-val-split",
      "kernel_id": "17826221",
      "votes": 61
    },
    {
      "ref": "chamecall/bert-train",
      "kernel_id": "17515861",
      "votes": 4
    },
    {
      "ref": "chamecall/clrp-inference",
      "kernel_id": "17547657",
      "votes": 241
    },
    {
      "ref": "chamecall/boosting-train",
      "kernel_id": "17549477",
      "votes": 1
    },
    {
      "ref": "zhrz9616/fork-and-try-roberta-torch-fit",
      "kernel_id": "17320413",
      "votes": 0
    },
    {
      "ref": "engineerfordata/notebook0f7605beb7",
      "kernel_id": "17064890",
      "votes": 0
    },
    {
      "ref": "hinepo/clrp-textstat-feature-eng-xgb-importances",
      "kernel_id": "17618835",
      "votes": 8
    },
    {
      "ref": "mdhamani/clrp-roberta-train",
      "kernel_id": "17083100",
      "votes": 6
    },
    {
      "ref": "mdhamani/clrp-roberta-inference",
      "kernel_id": "17098553",
      "votes": 18
    },
    {
      "ref": "kushagrgoyal/common-readibility-trial",
      "kernel_id": "17930737",
      "votes": 2
    },
    {
      "ref": "wptouxx/clrp-xgboost",
      "kernel_id": "17024168",
      "votes": 5
    },
    {
      "ref": "syurenuko/clrp-word2vec-lightgbm-baseline",
      "kernel_id": "17767517",
      "votes": 40
    },
    {
      "ref": "andradaolteanu/ii-commonlit-bert-vs-roberta-w-b-testing",
      "kernel_id": "17247775",
      "votes": 108
    },
    {
      "ref": "gabrielcf8/commonlit-readability-competition",
      "kernel_id": "17409389",
      "votes": 1
    },
    {
      "ref": "hazigin/commonlit-readability-bert-lightgbm",
      "kernel_id": "17455111",
      "votes": 0
    },
    {
      "ref": "lekynam2000/bart-pretrained-no-fold",
      "kernel_id": "17128450",
      "votes": 5
    },
    {
      "ref": "lekynam2000/bart-custom-loss-use-stderror",
      "kernel_id": "17008868",
      "votes": 29
    },
    {
      "ref": "the0electronic0guy/commonlit-roberta-top-12",
      "kernel_id": "17443986",
      "votes": 98
    },
    {
      "ref": "kozkiuay/pytorch-bert-for-commonlit-readability",
      "kernel_id": "17096393",
      "votes": 2
    },
    {
      "ref": "krishna1997gopal/understand-roberta",
      "kernel_id": "17725833",
      "votes": 12
    },
    {
      "ref": "krishna1997gopal/understand-bert-in-depth-theory-implementation",
      "kernel_id": "17494937",
      "votes": 84
    },
    {
      "ref": "krishna1997gopal/important-in-readability-eda-tf-idf-w2v-xg-boost",
      "kernel_id": "17014204",
      "votes": 22
    },
    {
      "ref": "emrearuk/commonlitreadabilityprize",
      "kernel_id": "17865206",
      "votes": 5
    },
    {
      "ref": "adarshsng/complexity-word2vec-pca-conv1d",
      "kernel_id": "17484754",
      "votes": 16
    },
    {
      "ref": "adarshsng/complexity-cleaning-f-engg-bi-lstm",
      "kernel_id": "17463683",
      "votes": 46
    },
    {
      "ref": "lillymottershead/commonlit-readability-analysis",
      "kernel_id": "17187809",
      "votes": 1
    },
    {
      "ref": "chumajin/bert-v-s-roberta-english",
      "kernel_id": "17224385",
      "votes": 91
    },
    {
      "ref": "katsuyanomura/eda-crp",
      "kernel_id": "17574156",
      "votes": 5
    },
    {
      "ref": "katsuyanomura/word2vec-lgbm-baseline",
      "kernel_id": "17654513",
      "votes": 2
    },
    {
      "ref": "taherhaggui/eda-modeling-very-interesting",
      "kernel_id": "17355774",
      "votes": 4
    },
    {
      "ref": "taherhaggui/roberta-fine-tunning",
      "kernel_id": "17878190",
      "votes": 1
    },
    {
      "ref": "taherhaggui/roberta-training",
      "kernel_id": "17856249",
      "votes": 1
    },
    {
      "ref": "taherhaggui/inference-roberta-lgbm",
      "kernel_id": "17917621",
      "votes": 1
    },
    {
      "ref": "taherhaggui/training-bert-models",
      "kernel_id": "17982582",
      "votes": 0
    },
    {
      "ref": "taherhaggui/bert-fine-tunning",
      "kernel_id": "17991125",
      "votes": 0
    },
    {
      "ref": "felixneate/commonlit-signalvsnoise",
      "kernel_id": "17112773",
      "votes": 1
    },
    {
      "ref": "ruriarmandhani/clrp-roberta-lstm",
      "kernel_id": "17212162",
      "votes": 1
    },
    {
      "ref": "ruriarmandhani/commonlit-submission",
      "kernel_id": "17221362",
      "votes": 0
    },
    {
      "ref": "tensorchoko/commonlit-readability-roberta",
      "kernel_id": "17963449",
      "votes": 70
    },
    {
      "ref": "tensorchoko/commonlit-readability",
      "kernel_id": "17387039",
      "votes": 42
    },
    {
      "ref": "bharadwajvedula/tpu-high-speed-roberta-training",
      "kernel_id": "17316667",
      "votes": 43
    },
    {
      "ref": "bharadwajvedula/clr-lb-0-475-lazy-way-to-get-good-score",
      "kernel_id": "17883884",
      "votes": 62
    },
    {
      "ref": "reinaueda/submit",
      "kernel_id": "17867699",
      "votes": 3
    },
    {
      "ref": "nikolayromanenkov/lightautoml-roberta",
      "kernel_id": "17579544",
      "votes": 4
    },
    {
      "ref": "mananjhaveri/transformers-for-the-first-time",
      "kernel_id": "17861907",
      "votes": 14
    },
    {
      "ref": "iska47/commonlit-data-exploration-and-classic-algorithms",
      "kernel_id": "17347117",
      "votes": 1
    },
    {
      "ref": "justinchae/crp-regression-with-roberta-and-lightgbm",
      "kernel_id": "17692839",
      "votes": 5
    },
    {
      "ref": "drzhuzhe/roberta-base-with-radam-lookahead",
      "kernel_id": "17876185",
      "votes": 1
    },
    {
      "ref": "drzhuzhe/commonlit-stratified-kfold",
      "kernel_id": "17776434",
      "votes": 1
    },
    {
      "ref": "drzhuzhe/crp-roberta-base-tpt",
      "kernel_id": "17848511",
      "votes": 0
    },
    {
      "ref": "drzhuzhe/error-analysis",
      "kernel_id": "17858352",
      "votes": 3
    },
    {
      "ref": "oleksandrsirenko/commonlit-in-depth-eda-baseline",
      "kernel_id": "17057363",
      "votes": 11
    },
    {
      "ref": "markwijkhuizen/commonlit-training",
      "kernel_id": "17247939",
      "votes": 0
    },
    {
      "ref": "thecurvefitter/roberta-embedding-ml",
      "kernel_id": "17039508",
      "votes": 11
    },
    {
      "ref": "thecurvefitter/roberta-model",
      "kernel_id": "17033426",
      "votes": 1
    },
    {
      "ref": "thecurvefitter/baseline-model",
      "kernel_id": "17000402",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/commonlit-bert-stratified-k-fold-baseline-train",
      "kernel_id": "17220414",
      "votes": 62
    },
    {
      "ref": "atsushiiwasaki/commonlit-bert-stratified-k-fold-baseline-infer",
      "kernel_id": "17232860",
      "votes": 25
    },
    {
      "ref": "atsushiiwasaki/how-many-samples-do-i-want",
      "kernel_id": "17256011",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/sample-size-experiment-training-part",
      "kernel_id": "17255250",
      "votes": 0
    },
    {
      "ref": "shauryajain/lgbm-regressor",
      "kernel_id": "17296015",
      "votes": 7
    },
    {
      "ref": "mokicheese/fork-of-fork-of-transformers-cv-train-infe-b02165",
      "kernel_id": "17318641",
      "votes": 1
    },
    {
      "ref": "moeinshariatnia/does-augmentation-hurt-let-s-ask-the-data-w-tsne",
      "kernel_id": "17523826",
      "votes": 8
    },
    {
      "ref": "moeinshariatnia/commonlit-easy-data-augmentation",
      "kernel_id": "17523380",
      "votes": 1
    },
    {
      "ref": "biswajitroy7890/bert-embeddings-xgboost-regression-text-data",
      "kernel_id": "17789949",
      "votes": 4
    },
    {
      "ref": "tatamikenn/rough-argument-of-theoretical-lower-bound",
      "kernel_id": "17890508",
      "votes": 14
    },
    {
      "ref": "sourabhy/elmo-svm",
      "kernel_id": "17249479",
      "votes": 8
    },
    {
      "ref": "sourabhy/generating-elmo-embeddings",
      "kernel_id": "17248688",
      "votes": 6
    },
    {
      "ref": "sourabhy/commonlit-roberta-ensemble-multichannel-cnn",
      "kernel_id": "17145454",
      "votes": 79
    },
    {
      "ref": "harshmishraandheri/submission-csv",
      "kernel_id": "17847978",
      "votes": 4
    },
    {
      "ref": "adityagarg2809/using-basic-text-features-to-predict-readability",
      "kernel_id": "17934908",
      "votes": 2
    },
    {
      "ref": "tpothjuan/basic-eda-and-simple-lstm-model-prediction-tf",
      "kernel_id": "17022182",
      "votes": 3
    },
    {
      "ref": "yhirakawa/textstat-how-to-evaluate-readability",
      "kernel_id": "17891707",
      "votes": 68
    },
    {
      "ref": "superbb666/superbb666-ngram",
      "kernel_id": "17028537",
      "votes": 1
    },
    {
      "ref": "bumjunkoo/commonlit-run-glue-inference",
      "kernel_id": "17736195",
      "votes": 2
    },
    {
      "ref": "bumjunkoo/commonlit-run-glue",
      "kernel_id": "17644067",
      "votes": 2
    },
    {
      "ref": "shreyasajal/pytorch-lightning-bert-tpu-weights-biases-logs",
      "kernel_id": "17289194",
      "votes": 99
    },
    {
      "ref": "shreyasajal/prompt-tuning-bert-commonlit-readability",
      "kernel_id": "17124299",
      "votes": 93
    },
    {
      "ref": "hotsonhonet/inference-clrf",
      "kernel_id": "17317465",
      "votes": 13
    },
    {
      "ref": "hotsonhonet/helpme",
      "kernel_id": "17293293",
      "votes": 7
    },
    {
      "ref": "hotsonhonet/training-1",
      "kernel_id": "17040479",
      "votes": 5
    },
    {
      "ref": "jinhangjiang/commonlit-readability-sbert-xgb",
      "kernel_id": "17299533",
      "votes": 1
    },
    {
      "ref": "tracyporter/commonlit-readibility-svr-single-y",
      "kernel_id": "17989089",
      "votes": 2
    },
    {
      "ref": "tracyporter/commonlit-readibility-svr",
      "kernel_id": "17983280",
      "votes": 3
    },
    {
      "ref": "sujithkpanoor/required-data",
      "kernel_id": "17983034",
      "votes": 0
    },
    {
      "ref": "sujithkpanoor/roberta-svm-34",
      "kernel_id": "17983628",
      "votes": 1
    },
    {
      "ref": "amiteshgangrade/gradient-boosting-with-glove-embedding",
      "kernel_id": "17819348",
      "votes": 14
    },
    {
      "ref": "hedwig100/bert-simple-baseline-inference",
      "kernel_id": "17416505",
      "votes": 0
    },
    {
      "ref": "hedwig100/bert-simple-baseline",
      "kernel_id": "17391200",
      "votes": 2
    },
    {
      "ref": "aniketsharma00411/random-forest",
      "kernel_id": "17405272",
      "votes": 2
    },
    {
      "ref": "aniketsharma00411/commonlit-readability-decision-tree",
      "kernel_id": "17076153",
      "votes": 4
    },
    {
      "ref": "aniketsharma00411/commonlit-readability-data-observations",
      "kernel_id": "17711192",
      "votes": 8
    },
    {
      "ref": "aniketsharma00411/commonlit-readability-svr",
      "kernel_id": "17714490",
      "votes": 7
    },
    {
      "ref": "aniketsharma00411/commonlit-readability-random-forest",
      "kernel_id": "17699384",
      "votes": 2
    },
    {
      "ref": "aniketsharma00411/commonlit-readability-ridge",
      "kernel_id": "17741474",
      "votes": 4
    },
    {
      "ref": "pradipkumardas/4-commonlit-readability-simple-roberta-with-cv",
      "kernel_id": "17722190",
      "votes": 3
    },
    {
      "ref": "pradipkumardas/3-commonlit-readability-simple-model-with-bert",
      "kernel_id": "17624134",
      "votes": 1
    },
    {
      "ref": "pradipkumardas/1-commonlit-readability-eda",
      "kernel_id": "17537649",
      "votes": 1
    },
    {
      "ref": "pradipkumardas/2-commonlit-readability-baseline-perf-1dconvnet",
      "kernel_id": "17583957",
      "votes": 2
    },
    {
      "ref": "douglaskgaraujo/commonlit-readability-text-features",
      "kernel_id": "17951690",
      "votes": 7
    },
    {
      "ref": "douglaskgaraujo/sentence-complexity-comparison-dataset",
      "kernel_id": "17567339",
      "votes": 4
    },
    {
      "ref": "joshualancaster/readnet-regression",
      "kernel_id": "17137309",
      "votes": 2
    },
    {
      "ref": "joshualancaster/commonlit-readability-prize-roberta-torch-itpt",
      "kernel_id": "17222619",
      "votes": 4
    },
    {
      "ref": "bhaveshkumar2806/commonlit-readability-basic-eda-and-roberta-base",
      "kernel_id": "17050109",
      "votes": 12
    },
    {
      "ref": "srikantakumarsahoo/notebook2f62e91059",
      "kernel_id": "17694993",
      "votes": 0
    },
    {
      "ref": "ligala/readability",
      "kernel_id": "17988891",
      "votes": 75
    },
    {
      "ref": "samarthsharma1408/commonlit-readability-price-simple-eda",
      "kernel_id": "17876474",
      "votes": 5
    },
    {
      "ref": "maostack/clrp-how-to-get-text-embedding-from-roberta",
      "kernel_id": "17001590",
      "votes": 25
    },
    {
      "ref": "lucabottero/statistical-features-from-text",
      "kernel_id": "17109057",
      "votes": 4
    },
    {
      "ref": "nityasevak/heirarchial-attention-regression",
      "kernel_id": "17616502",
      "votes": 0
    },
    {
      "ref": "nityasevak/a-neural-factorization-machine-approach",
      "kernel_id": "17490812",
      "votes": 2
    },
    {
      "ref": "nityasevak/a-zero-feature-engineering-approach",
      "kernel_id": "17166516",
      "votes": 1
    },
    {
      "ref": "nityasevak/ncr-roberta-minilm-mpnet-xgboost",
      "kernel_id": "17282369",
      "votes": 0
    },
    {
      "ref": "lucamtb/readibily-nn",
      "kernel_id": "17147144",
      "votes": 1
    },
    {
      "ref": "lucamtb/readability-feature-engineering-non-nn-baseline",
      "kernel_id": "17167147",
      "votes": 26
    },
    {
      "ref": "urstrulysai/ensemble-just-do-it",
      "kernel_id": "17108582",
      "votes": 18
    },
    {
      "ref": "yus002/readability-will-i-overfit",
      "kernel_id": "17382819",
      "votes": 13
    },
    {
      "ref": "gabrielchapeu/gabriel-previs-o",
      "kernel_id": "17567623",
      "votes": 0
    },
    {
      "ref": "jeongbinpark/using-word2vec",
      "kernel_id": "17039107",
      "votes": 28
    },
    {
      "ref": "jeongbinpark/using-keras-word-embedding",
      "kernel_id": "17037572",
      "votes": 27
    },
    {
      "ref": "jeongbinpark/using-subwordtextencoder",
      "kernel_id": "17075475",
      "votes": 21
    },
    {
      "ref": "mohammedfahadvp/commonlit-readability-prize-beginners-eda",
      "kernel_id": "17421193",
      "votes": 21
    },
    {
      "ref": "dragospancescu/commonlit",
      "kernel_id": "17469946",
      "votes": 2
    },
    {
      "ref": "ttjccc/pytorch-1-9-0",
      "kernel_id": "17893411",
      "votes": 4
    },
    {
      "ref": "ttjccc/download-and-offline-install-the-latest-pytorch",
      "kernel_id": "17352069",
      "votes": 6
    },
    {
      "ref": "ttjccc/offline-install-pytorch-1-8-1",
      "kernel_id": "17364322",
      "votes": 14
    },
    {
      "ref": "tommasomarzi/sub-scoring-error",
      "kernel_id": "17083118",
      "votes": 3
    },
    {
      "ref": "kunihikofurugori/training-commonlit-readability-bertmodel",
      "kernel_id": "17003117",
      "votes": 2
    },
    {
      "ref": "kunihikofurugori/finetunning-commonlit-readability-transformer",
      "kernel_id": "17300819",
      "votes": 1
    },
    {
      "ref": "kunihikofurugori/make-nlpdataset",
      "kernel_id": "17445314",
      "votes": 0
    },
    {
      "ref": "kunihikofurugori/tpu8fold-transformer",
      "kernel_id": "17586186",
      "votes": 1
    },
    {
      "ref": "kunihikofurugori/tpu8fold-transformer-attention",
      "kernel_id": "17661229",
      "votes": 0
    },
    {
      "ref": "kunihikofurugori/tpu8fold-distilroberta",
      "kernel_id": "17973605",
      "votes": 1
    },
    {
      "ref": "mbonnet/baseline-pretrained-distilbert-and-regression",
      "kernel_id": "17783809",
      "votes": 49
    },
    {
      "ref": "mrruslionkz/notebookff02ed0974",
      "kernel_id": "17640364",
      "votes": 1
    },
    {
      "ref": "sounaklahiri01/readability-baseline-distilbert",
      "kernel_id": "17302109",
      "votes": 0
    },
    {
      "ref": "mehrankamal/transformer-cnn-lstm-attention-heads-roberta",
      "kernel_id": "17134324",
      "votes": 1
    },
    {
      "ref": "mehrankamal/transformer-cnn-lstm-attention-heads-distil",
      "kernel_id": "17134353",
      "votes": 1
    },
    {
      "ref": "mehrankamal/commonlit-roberta-cnn",
      "kernel_id": "17017897",
      "votes": 2
    },
    {
      "ref": "mehrankamal/fork-of-commonlit-roberta-cnn",
      "kernel_id": "17020396",
      "votes": 1
    },
    {
      "ref": "nafiularaf/commonlit-readability",
      "kernel_id": "17105177",
      "votes": 3
    },
    {
      "ref": "viteshkakadia/performance-comparison-of-regression-models",
      "kernel_id": "17858545",
      "votes": 1
    },
    {
      "ref": "annazhukovets/read-rate-svr",
      "kernel_id": "17657211",
      "votes": 4
    },
    {
      "ref": "kappawarrior/bertforsequence-with-kfold",
      "kernel_id": "17918114",
      "votes": 0
    },
    {
      "ref": "dlaststark/commonlit-roberta-variants-p1",
      "kernel_id": "17854649",
      "votes": 1
    },
    {
      "ref": "dlaststark/commonlit-bert-variants-p1",
      "kernel_id": "17849103",
      "votes": 3
    },
    {
      "ref": "dlaststark/commonlit-glove-models-ensemble",
      "kernel_id": "17490582",
      "votes": 7
    },
    {
      "ref": "dlaststark/commonlit-data-augmentation",
      "kernel_id": "17521984",
      "votes": 2
    },
    {
      "ref": "masatoootsuka/commonlit-eda",
      "kernel_id": "17378436",
      "votes": 1
    },
    {
      "ref": "gcmadhan/commonlit-readability-eda-modeling",
      "kernel_id": "17212883",
      "votes": 40
    },
    {
      "ref": "ashuto7h/commonlit-readability2-model",
      "kernel_id": "17857573",
      "votes": 0
    },
    {
      "ref": "heyanghuang/baseline-hhy-1-0",
      "kernel_id": "17972741",
      "votes": 4
    },
    {
      "ref": "elenakhachatryan/commonlit-readability-prize-eda-baseline",
      "kernel_id": "17658215",
      "votes": 3
    },
    {
      "ref": "elemento/litread-ml",
      "kernel_id": "17356853",
      "votes": 4
    },
    {
      "ref": "hwangdongjun/predict-readability",
      "kernel_id": "17515186",
      "votes": 5
    },
    {
      "ref": "meihanw/commonlit-autokeras-try-out",
      "kernel_id": "17337366",
      "votes": 8
    },
    {
      "ref": "luanvietnguyen/gpt2-surprisal",
      "kernel_id": "17885556",
      "votes": 1
    },
    {
      "ref": "rungogo/fork-of-commonlit-readability-prize-roberta-torc",
      "kernel_id": "17615442",
      "votes": 2
    },
    {
      "ref": "rungogo/fork-of-commonlit-readability-prize-rober-itpt",
      "kernel_id": "17621710",
      "votes": 2
    },
    {
      "ref": "vermichel/commonlit-readability-prize",
      "kernel_id": "17876203",
      "votes": 13
    },
    {
      "ref": "hjhgjghhg/commonlit-readability-roberta-pretrain",
      "kernel_id": "17637282",
      "votes": 3
    },
    {
      "ref": "pppass/commonlit-readability-robertalarge-tf-stratifie",
      "kernel_id": "17328889",
      "votes": 3
    },
    {
      "ref": "gavinjang/make-prediction",
      "kernel_id": "17694365",
      "votes": 0
    },
    {
      "ref": "walywaly/readingease",
      "kernel_id": "17978055",
      "votes": 2
    },
    {
      "ref": "dhlongle/basic-nlp-with-random-forest",
      "kernel_id": "17622204",
      "votes": 6
    },
    {
      "ref": "jrpark3797/house-prices-titanic-automl",
      "kernel_id": "17058230",
      "votes": 0
    },
    {
      "ref": "matinroom/fork-of-ensem",
      "kernel_id": "17443163",
      "votes": 4
    },
    {
      "ref": "samsonteo/common-readibility001",
      "kernel_id": "17282803",
      "votes": 12
    },
    {
      "ref": "bob2ross/fork-of-commonlit-itpt-weight-decay005-roberta-to",
      "kernel_id": "17682062",
      "votes": 1
    },
    {
      "ref": "rolando222/fork-of-ensem",
      "kernel_id": "17459320",
      "votes": 1
    },
    {
      "ref": "bongbu/notebook41b8773172",
      "kernel_id": "17491608",
      "votes": 1
    },
    {
      "ref": "andyanybody/fork-of-commonlit-readability-prize-roberta-torch",
      "kernel_id": "17285597",
      "votes": 3
    },
    {
      "ref": "brucebatmanwayne/clrp-distilbert-inference",
      "kernel_id": "17672718",
      "votes": 0
    },
    {
      "ref": "jennyteng/project",
      "kernel_id": "17874125",
      "votes": 3
    },
    {
      "ref": "anniedsouza/comlit-lstm",
      "kernel_id": "17923236",
      "votes": 1
    },
    {
      "ref": "dev1ceqqq/ensemblethreemethods",
      "kernel_id": "17907701",
      "votes": 57
    },
    {
      "ref": "dev1ceqqq/crl-newroberta-large-finetune",
      "kernel_id": "17873687",
      "votes": 3
    },
    {
      "ref": "dev1ceqqq/deberta-oof",
      "kernel_id": "17967154",
      "votes": 5
    },
    {
      "ref": "mathislucka/commonlitreadability-r3",
      "kernel_id": "17616127",
      "votes": 57
    },
    {
      "ref": "gabellinifrancesco/nlp-from-bag-of-words-to-transformer-commonlit",
      "kernel_id": "17891211",
      "votes": 36
    },
    {
      "ref": "ankarsingh/commonlit-text-cleaning-and-huggingface-model",
      "kernel_id": "17549930",
      "votes": 15
    },
    {
      "ref": "b0721129/commonlit-readability-analysis",
      "kernel_id": "17890792",
      "votes": 1
    },
    {
      "ref": "heyangk/kkkkk",
      "kernel_id": "17977366",
      "votes": 2
    },
    {
      "ref": "abhishek/debertapairwise466",
      "kernel_id": "18968786",
      "votes": 40
    },
    {
      "ref": "gilfernandes/commonlit-pytorch-ensemble-large",
      "kernel_id": "18523075",
      "votes": 4
    },
    {
      "ref": "gilfernandes/commonlit-pytorch-t5-large-svm",
      "kernel_id": "18638080",
      "votes": 28
    },
    {
      "ref": "ambarish/common-readability-embeddings-lstm",
      "kernel_id": "18621746",
      "votes": 23
    },
    {
      "ref": "neel783d/commonlit-regression-model-comparison",
      "kernel_id": "18817443",
      "votes": 16
    },
    {
      "ref": "datajmcn/nlp-read-eda",
      "kernel_id": "18451276",
      "votes": 14
    },
    {
      "ref": "deepaksadulla/xgb-roberta-customcv-sam-wgts-kmeans",
      "kernel_id": "18961894",
      "votes": 0
    },
    {
      "ref": "aman1391/lightgbm-dance-ridge",
      "kernel_id": "18302906",
      "votes": 0
    },
    {
      "ref": "kartikbhargav/clrp-roberta-model-and-inference",
      "kernel_id": "18339638",
      "votes": 6
    },
    {
      "ref": "vigneshbaskaran/commonlit-nn-kit",
      "kernel_id": "18462088",
      "votes": 23
    },
    {
      "ref": "kirichenko17roman/dl-lab-26-catalyst",
      "kernel_id": "18468467",
      "votes": 4
    },
    {
      "ref": "sagol79/sent-transformer-simple-normal-wiki-texts-svr",
      "kernel_id": "18986507",
      "votes": 2
    },
    {
      "ref": "yogeshkulkarni/commonlit-readability-prize-with-pytorch",
      "kernel_id": "18612751",
      "votes": 51
    },
    {
      "ref": "yogeshkulkarni/commonlit-readability-prize-with-autokeras",
      "kernel_id": "18620147",
      "votes": 0
    },
    {
      "ref": "sahilt93/feature-extraction-bow-word2vec-tfidf-base-model",
      "kernel_id": "18495847",
      "votes": 37
    },
    {
      "ref": "mzntaka0/inference-with-fasttext",
      "kernel_id": "18469309",
      "votes": 2
    },
    {
      "ref": "mzntaka0/document-represenataion-with-fasttext",
      "kernel_id": "18294708",
      "votes": 1
    },
    {
      "ref": "tbhavnani/baseline-bert-regression-using-pytorch",
      "kernel_id": "18287845",
      "votes": 53
    },
    {
      "ref": "tbhavnani/bertseq-with-reading-parameters",
      "kernel_id": "18545868",
      "votes": 1
    },
    {
      "ref": "takoihiraokazu/ex298-inference-save",
      "kernel_id": "18485883",
      "votes": 7
    },
    {
      "ref": "takoihiraokazu/clrp-pytorch-roberta-pretrain-fold0",
      "kernel_id": "18237814",
      "votes": 6
    },
    {
      "ref": "takoihiraokazu/ex237-roberta",
      "kernel_id": "18238368",
      "votes": 6
    },
    {
      "ref": "takoihiraokazu/deberta-v2-xlarge-194-save",
      "kernel_id": "18256189",
      "votes": 6
    },
    {
      "ref": "takoihiraokazu/funnel-large-base-save",
      "kernel_id": "18285976",
      "votes": 6
    },
    {
      "ref": "takoihiraokazu/funel-257-inference-save",
      "kernel_id": "18290586",
      "votes": 10
    },
    {
      "ref": "takoihiraokazu/albert-xxlarge-v2-save",
      "kernel_id": "18851254",
      "votes": 11
    },
    {
      "ref": "takoihiraokazu/ex450-448-albert-v2-xxlarge",
      "kernel_id": "18851355",
      "votes": 14
    },
    {
      "ref": "takoihiraokazu/ex423-funnel-medium",
      "kernel_id": "18815389",
      "votes": 12
    },
    {
      "ref": "takoihiraokazu/gpt-medium-save",
      "kernel_id": "18815353",
      "votes": 8
    },
    {
      "ref": "takoihiraokazu/ex216-inference-save-2",
      "kernel_id": "18815443",
      "votes": 9
    },
    {
      "ref": "takoihiraokazu/ex216-inference-save-1",
      "kernel_id": "18814724",
      "votes": 8
    },
    {
      "ref": "takoihiraokazu/funnel-medium-save",
      "kernel_id": "18815163",
      "votes": 8
    },
    {
      "ref": "takoihiraokazu/gpt-large-save",
      "kernel_id": "18824955",
      "votes": 13
    },
    {
      "ref": "takoihiraokazu/funnel-large-save",
      "kernel_id": "18756471",
      "votes": 10
    },
    {
      "ref": "takoihiraokazu/muppet-roberta-large",
      "kernel_id": "18713801",
      "votes": 8
    },
    {
      "ref": "takoihiraokazu/xlnet-base-save",
      "kernel_id": "18971369",
      "votes": 20
    },
    {
      "ref": "takoihiraokazu/bert-uncased-base-save",
      "kernel_id": "18971834",
      "votes": 14
    },
    {
      "ref": "takoihiraokazu/xlnet-large-save",
      "kernel_id": "18961495",
      "votes": 16
    },
    {
      "ref": "takoihiraokazu/t5-large-save",
      "kernel_id": "18981374",
      "votes": 12
    },
    {
      "ref": "tusonggao/clrp-pytorch-roberta-base-lightgbm",
      "kernel_id": "18036565",
      "votes": 5
    },
    {
      "ref": "tusonggao/clrp-pytorch-roberta-base-svm",
      "kernel_id": "18028986",
      "votes": 10
    },
    {
      "ref": "lhagiimn/commonlit-readability-tensorflow-torch-ensemble-v1",
      "kernel_id": "18711514",
      "votes": 4
    },
    {
      "ref": "himako/allennlp-jsonnet-weak-example-using-roberta",
      "kernel_id": "18837910",
      "votes": 4
    },
    {
      "ref": "bond005/twin-roberta-regression",
      "kernel_id": "18999565",
      "votes": 14
    },
    {
      "ref": "bond005/spacy-readability",
      "kernel_id": "18853482",
      "votes": 46
    },
    {
      "ref": "bond005/readability-bert",
      "kernel_id": "18719667",
      "votes": 35
    },
    {
      "ref": "shivarama/word2vec-xgb-starter-kit-101",
      "kernel_id": "18575092",
      "votes": 20
    },
    {
      "ref": "raghavendrakotala/basiceda",
      "kernel_id": "18577415",
      "votes": 1
    },
    {
      "ref": "raghavendrakotala/pytorch-basline-lstm",
      "kernel_id": "18662145",
      "votes": 7
    },
    {
      "ref": "authman/clit-fails",
      "kernel_id": "18998089",
      "votes": 11
    },
    {
      "ref": "narendra/clrp-roberta-task-finetune-2",
      "kernel_id": "18822308",
      "votes": 1
    },
    {
      "ref": "narendra/commonlit-gru-with-attention-aggregation",
      "kernel_id": "18221654",
      "votes": 4
    },
    {
      "ref": "narendra/commonlit-readability-prize-bert-finetuning",
      "kernel_id": "18105422",
      "votes": 1
    },
    {
      "ref": "narendra/clrp-hirearchial-attention",
      "kernel_id": "18403635",
      "votes": 0
    },
    {
      "ref": "atamazian/clrp-model-with-various-poolers",
      "kernel_id": "18741251",
      "votes": 6
    },
    {
      "ref": "vineethakkinapalli/clrp-roberta-base-representation-techniques",
      "kernel_id": "18403354",
      "votes": 30
    },
    {
      "ref": "vineethakkinapalli/commonlit-two-models",
      "kernel_id": "18423958",
      "votes": 5
    },
    {
      "ref": "urvishp80/roberta-base-last-2-hs-inference-seed-3000",
      "kernel_id": "18754431",
      "votes": 1
    },
    {
      "ref": "wamateusz/linear-regression",
      "kernel_id": "18793826",
      "votes": 10
    },
    {
      "ref": "kaielloasu/commonlit-readability-prize-roberta-torch-infer-3",
      "kernel_id": "18957287",
      "votes": 0
    },
    {
      "ref": "julian3833/1-learning-out-of-the-box-bert-lb-0-577",
      "kernel_id": "18023588",
      "votes": 14
    },
    {
      "ref": "julian3833/2-learning-out-of-the-box-roberta-lb-0-53",
      "kernel_id": "18025557",
      "votes": 27
    },
    {
      "ref": "julian3833/3-learning-out-of-the-box-electra-lb-0-58",
      "kernel_id": "18114566",
      "votes": 10
    },
    {
      "ref": "sapal6/common-lit-fwd-bkwd-training-on-pseudo-label",
      "kernel_id": "18953084",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-pseudo-labeler",
      "kernel_id": "18929693",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-inference-enseamble-fwd-bkwd-lm",
      "kernel_id": "18894513",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-inf-ensmble-fwd-bkwd-student",
      "kernel_id": "18959000",
      "votes": 1
    },
    {
      "ref": "sapal6/common-lit-fwd-training-on-pseudo-l",
      "kernel_id": "18982827",
      "votes": 0
    },
    {
      "ref": "sapal6/commonlit-inf-fwd-student",
      "kernel_id": "18987657",
      "votes": 0
    },
    {
      "ref": "sapal6/common-lit-forward-backward-training-on-ulmfit",
      "kernel_id": "18435731",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-infusing-data-with-same-domain-data",
      "kernel_id": "18484543",
      "votes": 1
    },
    {
      "ref": "sapal6/commonlit-train-lm-with-cbt-data-without-wndb",
      "kernel_id": "18575877",
      "votes": 0
    },
    {
      "ref": "davidgzx/stratifiedkfold-on-commonlit-dataset",
      "kernel_id": "18821632",
      "votes": 1
    },
    {
      "ref": "harshpatel1692/clrp-ensemble-inference",
      "kernel_id": "18569217",
      "votes": 0
    },
    {
      "ref": "victororlov/bert-simple-baseline-tensorflow",
      "kernel_id": "18407844",
      "votes": 1
    },
    {
      "ref": "wuyhbb/inference-large",
      "kernel_id": "18629674",
      "votes": 2
    },
    {
      "ref": "wuyhbb/inference-large-ensemble2",
      "kernel_id": "18983423",
      "votes": 0
    },
    {
      "ref": "rhtsingh/swa-apex-amp-interpreting-transformers-in-torch",
      "kernel_id": "18251751",
      "votes": 254
    },
    {
      "ref": "chenfeng1271/fork-of-fork-of-yum-yum-yum-93f968",
      "kernel_id": "18480956",
      "votes": 6
    },
    {
      "ref": "chenfeng1271/commonlit-readability-tensorflow-torch-ensemble",
      "kernel_id": "18001213",
      "votes": 41
    },
    {
      "ref": "shivamanhar/data-extraction-and-data-visualisation",
      "kernel_id": "18036972",
      "votes": 20
    },
    {
      "ref": "abhishek1aa/stacking-ensemble-roberta-xgboost-ridge",
      "kernel_id": "18143257",
      "votes": 19
    },
    {
      "ref": "ppjanka/2021-commonlitreadability-final",
      "kernel_id": "18926587",
      "votes": 7
    },
    {
      "ref": "ppjanka/2021-commonlitreadability",
      "kernel_id": "18502749",
      "votes": 8
    },
    {
      "ref": "jcesquiveld/best-transformer-representations",
      "kernel_id": "18418612",
      "votes": 139
    },
    {
      "ref": "jcesquiveld/generate-extra-data-with-web-scraping",
      "kernel_id": "18624203",
      "votes": 45
    },
    {
      "ref": "ananduk1993/cl-roberta-large-lightgbm-better-finetune",
      "kernel_id": "18924119",
      "votes": 3
    },
    {
      "ref": "ananduk1993/clrp-feature-engineering-baseline-prediction",
      "kernel_id": "18302140",
      "votes": 8
    },
    {
      "ref": "ananduk1993/clrp-data-clean-excerpt-stats",
      "kernel_id": "18470075",
      "votes": 2
    },
    {
      "ref": "juliojuse/commonlit-two-models-4dacde-aab544",
      "kernel_id": "18891187",
      "votes": 3
    },
    {
      "ref": "cdeotte/sentence-compare-approach-0-495",
      "kernel_id": "18954574",
      "votes": 40
    },
    {
      "ref": "takiholadi/02-commonlit-bert-cased-avg-folds",
      "kernel_id": "18305984",
      "votes": 2
    },
    {
      "ref": "takiholadi/01-commonlit-linreg-4-features",
      "kernel_id": "18266264",
      "votes": 1
    },
    {
      "ref": "sharrpie/transformers-clrp-pretrain-roberta-base",
      "kernel_id": "18993667",
      "votes": 0
    },
    {
      "ref": "sharrpie/clrp-finetune-roberta-base",
      "kernel_id": "18996223",
      "votes": 2
    },
    {
      "ref": "sharrpie/clrp-inference-roberta-base",
      "kernel_id": "18999026",
      "votes": 1
    },
    {
      "ref": "chtalhaanwar/simplest-solution",
      "kernel_id": "18371239",
      "votes": 3
    },
    {
      "ref": "nguyenvanlinh/fork-of-comonlit-continue-0-549-sparknlp3-13",
      "kernel_id": "18947808",
      "votes": 1
    },
    {
      "ref": "nguyenvanlinh/comonlit-continue-0-549-sparknlp3-13",
      "kernel_id": "18736642",
      "votes": 1
    },
    {
      "ref": "teeyee314/clr-url-legal-probe",
      "kernel_id": "18456123",
      "votes": 2
    },
    {
      "ref": "pdan93/clrp-features-only-model",
      "kernel_id": "18241489",
      "votes": 2
    },
    {
      "ref": "cgq081616/mean-pooling-4-seeds",
      "kernel_id": "18900200",
      "votes": 1
    },
    {
      "ref": "kaerunantoka/commonlit-110-inf",
      "kernel_id": "18459705",
      "votes": 27
    },
    {
      "ref": "kaerunantoka/commonlit-2nd-stacking-57",
      "kernel_id": "18995187",
      "votes": 26
    },
    {
      "ref": "ktgiahieu/commonlit-ensemble-distilrobertav3-v6",
      "kernel_id": "18522372",
      "votes": 3
    },
    {
      "ref": "ktgiahieu/commonlit-infer-roberta-base-regression",
      "kernel_id": "18143743",
      "votes": 0
    },
    {
      "ref": "shinomoriaoshi/readability-stacking-inference",
      "kernel_id": "18550705",
      "votes": 3
    },
    {
      "ref": "shinomoriaoshi/readability-meta-ensemble-inference",
      "kernel_id": "18740023",
      "votes": 10
    },
    {
      "ref": "kabhinay/roberta-base-inference",
      "kernel_id": "18495872",
      "votes": 1
    },
    {
      "ref": "kabhinay/commonlit-readability-prize-with-pytorch",
      "kernel_id": "18815050",
      "votes": 1
    },
    {
      "ref": "asvskartheek/bert-tpus-jax-huggingface",
      "kernel_id": "18079574",
      "votes": 141
    },
    {
      "ref": "harrytheorange/converttext2csv",
      "kernel_id": "18973508",
      "votes": 1
    },
    {
      "ref": "renjithrrkj/readnet-implemetation",
      "kernel_id": "18118908",
      "votes": 41
    },
    {
      "ref": "dijiswiki/clrp-ensemble-models-xgboost-roberta",
      "kernel_id": "18529671",
      "votes": 17
    },
    {
      "ref": "azertway/textstat-features-ridge",
      "kernel_id": "18242610",
      "votes": 1
    },
    {
      "ref": "w5833946/f50-train",
      "kernel_id": "18947057",
      "votes": 3
    },
    {
      "ref": "w5833946/f51-train",
      "kernel_id": "18947233",
      "votes": 2
    },
    {
      "ref": "w5833946/f-inference-1-1",
      "kernel_id": "18926325",
      "votes": 2
    },
    {
      "ref": "w5833946/f-inference-1",
      "kernel_id": "18722832",
      "votes": 2
    },
    {
      "ref": "w5833946/f41-train",
      "kernel_id": "18655331",
      "votes": 2
    },
    {
      "ref": "w5833946/f-inference-3-1-v4",
      "kernel_id": "18973745",
      "votes": 42
    },
    {
      "ref": "w5833946/f23-train",
      "kernel_id": "18498561",
      "votes": 2
    },
    {
      "ref": "w5833946/f17-train",
      "kernel_id": "18435413",
      "votes": 4
    },
    {
      "ref": "w5833946/f32-train",
      "kernel_id": "18634377",
      "votes": 3
    },
    {
      "ref": "w5833946/f1-itpt",
      "kernel_id": "18144226",
      "votes": 2
    },
    {
      "ref": "w5833946/f2-train",
      "kernel_id": "18225685",
      "votes": 4
    },
    {
      "ref": "dberezhnoy/implementing-bert-with-normalized-data",
      "kernel_id": "18327096",
      "votes": 1
    },
    {
      "ref": "dberezhnoy/edacode",
      "kernel_id": "18265250",
      "votes": 1
    },
    {
      "ref": "dberezhnoy/bert-model-0-577-public-score",
      "kernel_id": "18872811",
      "votes": 10
    },
    {
      "ref": "dhirenkakkar/custom-target-bootstrapping",
      "kernel_id": "18963428",
      "votes": 0
    },
    {
      "ref": "sambhavsg/clrp-training-and-inference-glove-embeddings",
      "kernel_id": "18115847",
      "votes": 10
    },
    {
      "ref": "sambhavsg/data-understanding-and-baseline-model-clrp",
      "kernel_id": "18106818",
      "votes": 4
    },
    {
      "ref": "maunish/clrp-pytorch-roberta-finetune-fixed-minor-issues",
      "kernel_id": "18589604",
      "votes": 44
    },
    {
      "ref": "akashdotcom/commonlit-using-glove-bidirectional-lstm",
      "kernel_id": "18545997",
      "votes": 5
    },
    {
      "ref": "darrentitor/0731-e-stratified-large-roberta",
      "kernel_id": "18969347",
      "votes": 0
    },
    {
      "ref": "darrentitor/0801-g-ensemble",
      "kernel_id": "18995367",
      "votes": 1
    },
    {
      "ref": "kuroyuli/commonlit-readnet-using-fastai",
      "kernel_id": "18911022",
      "votes": 1
    },
    {
      "ref": "arshisal/lateinthegame",
      "kernel_id": "18726257",
      "votes": 0
    },
    {
      "ref": "bacterio/6-nlp-con-transformers",
      "kernel_id": "18573686",
      "votes": 6
    },
    {
      "ref": "bacterio/5-submit-tfidf-ridge",
      "kernel_id": "18442341",
      "votes": 1
    },
    {
      "ref": "bacterio/4-regresion-tf-idf",
      "kernel_id": "18398553",
      "votes": 8
    },
    {
      "ref": "bacterio/3-entrenamiento-regresion-lineal",
      "kernel_id": "18045925",
      "votes": 19
    },
    {
      "ref": "bacterio/7-entrenamiento-transformer",
      "kernel_id": "18750569",
      "votes": 9
    },
    {
      "ref": "utkarsh175/lstm-commonlit",
      "kernel_id": "18438561",
      "votes": 0
    },
    {
      "ref": "sergeykubarev/commonlit-with-kerasregressor",
      "kernel_id": "18532639",
      "votes": 32
    },
    {
      "ref": "sergeykubarev/commonlit-readability-simple-models-to-start-with",
      "kernel_id": "18474615",
      "votes": 4
    },
    {
      "ref": "muhammadalaref/mha-commonlit-readability-prize",
      "kernel_id": "18273119",
      "votes": 0
    },
    {
      "ref": "iamshamikb/clrp-fe-w2v-and-ml",
      "kernel_id": "18987782",
      "votes": 17
    },
    {
      "ref": "iamshamikb/clrp-eda",
      "kernel_id": "18977410",
      "votes": 10
    },
    {
      "ref": "altruisticemphasis/a-different-approach",
      "kernel_id": "18252289",
      "votes": 4
    },
    {
      "ref": "rohithansdah/reading-submission",
      "kernel_id": "18695823",
      "votes": 0
    },
    {
      "ref": "anacgarcia/commonlit-readability-tensorflow-torch-ensemble",
      "kernel_id": "18408853",
      "votes": 36
    },
    {
      "ref": "o00585/rand95",
      "kernel_id": "18822396",
      "votes": 1
    },
    {
      "ref": "o00585/464-pre6",
      "kernel_id": "18733322",
      "votes": 3
    },
    {
      "ref": "mushaya/readability-index-basic",
      "kernel_id": "18662964",
      "votes": 6
    },
    {
      "ref": "hassanrady/roberta-torch-with-svr",
      "kernel_id": "18869391",
      "votes": 3
    },
    {
      "ref": "ktamta/eda-baseline",
      "kernel_id": "18457652",
      "votes": 2
    },
    {
      "ref": "kayvanshah/ensemble-commonlit-2-models",
      "kernel_id": "18660568",
      "votes": 3
    },
    {
      "ref": "muktan/roberta-large-double-attention-1-2-4-8-16-20",
      "kernel_id": "18871094",
      "votes": 7
    },
    {
      "ref": "chamecall/clrp-roberta-pretrain",
      "kernel_id": "18093152",
      "votes": 1
    },
    {
      "ref": "chamecall/clrp-roberta-finetune",
      "kernel_id": "18012087",
      "votes": 2
    },
    {
      "ref": "chamecall/clrp-pretrain",
      "kernel_id": "18142458",
      "votes": 71
    },
    {
      "ref": "chamecall/clrp-finetune-single-roberta-base",
      "kernel_id": "18143992",
      "votes": 200
    },
    {
      "ref": "chamecall/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "18352351",
      "votes": 34
    },
    {
      "ref": "chamecall/clrp-finetune-roberta-large",
      "kernel_id": "18553289",
      "votes": 85
    },
    {
      "ref": "chamecall/clrp-inference-with-handcrafted-features",
      "kernel_id": "18785428",
      "votes": 6
    },
    {
      "ref": "konumaru/submission-stacking-roberta-models",
      "kernel_id": "18757376",
      "votes": 3
    },
    {
      "ref": "dhruv1234/bert-regression",
      "kernel_id": "18453376",
      "votes": 8
    },
    {
      "ref": "hinepo/136th-5-architectures-35-models",
      "kernel_id": "18985862",
      "votes": 23
    },
    {
      "ref": "hinepo/clrp-electra-inference-lb-0-504",
      "kernel_id": "18000614",
      "votes": 14
    },
    {
      "ref": "mdhamani/clrp-roberta-pre-training-using-deepspeed",
      "kernel_id": "18348561",
      "votes": 62
    },
    {
      "ref": "prajittr/commonlit-readability-glove-xgb-nn-rf-ensemble",
      "kernel_id": "18110912",
      "votes": 22
    },
    {
      "ref": "ligtfeather/squeezebert-madgrad-grad-accumulation-w-b",
      "kernel_id": "18134761",
      "votes": 90
    },
    {
      "ref": "mcarujo/ease-of-reading-prediction-nlp",
      "kernel_id": "18315335",
      "votes": 21
    },
    {
      "ref": "petrand/submission-example",
      "kernel_id": "18705729",
      "votes": 2
    },
    {
      "ref": "mbmackenzie/feature-engineering-and-tfidf",
      "kernel_id": "18708552",
      "votes": 2
    },
    {
      "ref": "surajitcba2021/clrp-gru-withattention",
      "kernel_id": "18933335",
      "votes": 0
    },
    {
      "ref": "riadalmadani/svr-base-model",
      "kernel_id": "18349836",
      "votes": 1
    },
    {
      "ref": "wuharlem/bert-simple",
      "kernel_id": "18802033",
      "votes": 22
    },
    {
      "ref": "joeyaramouni/roberta-with-detailed-analysis-and-visualization",
      "kernel_id": "18795353",
      "votes": 0
    },
    {
      "ref": "vpalotai/commonlit-readability-prize-nlp-basics",
      "kernel_id": "18531535",
      "votes": 4
    },
    {
      "ref": "aleron751/lama-starter",
      "kernel_id": "18812912",
      "votes": 4
    },
    {
      "ref": "aleron751/lama-bert-inference",
      "kernel_id": "18812966",
      "votes": 4
    },
    {
      "ref": "aleron751/semantic-text-complexity-features",
      "kernel_id": "18792734",
      "votes": 7
    },
    {
      "ref": "aleron751/3-roberta-models-blending",
      "kernel_id": "18974291",
      "votes": 5
    },
    {
      "ref": "vivek61/simple-bert-without-finetuning-commonlit",
      "kernel_id": "18452605",
      "votes": 2
    },
    {
      "ref": "millak/bert-in-depth-understanding",
      "kernel_id": "18761197",
      "votes": 3
    },
    {
      "ref": "taherhaggui/bert-forecasting",
      "kernel_id": "18004707",
      "votes": 0
    },
    {
      "ref": "aditidutta/clrp-gpt2-implementation",
      "kernel_id": "18928128",
      "votes": 24
    },
    {
      "ref": "serkankaanbahsi/attention-bert-gru",
      "kernel_id": "18702512",
      "votes": 0
    },
    {
      "ref": "joezzb/synonyms",
      "kernel_id": "18633905",
      "votes": 1
    },
    {
      "ref": "mattjohn/commonlit",
      "kernel_id": "18808794",
      "votes": 0
    },
    {
      "ref": "panxnan/submittion-test",
      "kernel_id": "18417127",
      "votes": 0
    },
    {
      "ref": "panxnan/notebook-commonlit-readability-basedline",
      "kernel_id": "18242813",
      "votes": 20
    },
    {
      "ref": "lililycai/clrp-pytorch-roberta-pretrain",
      "kernel_id": "18796899",
      "votes": 3
    },
    {
      "ref": "justinchae/tune-roberta-pytorch-lightning-optuna",
      "kernel_id": "18249496",
      "votes": 4
    },
    {
      "ref": "justinchae/the-bernstein-bears-crp-submission-1",
      "kernel_id": "18638229",
      "votes": 0
    },
    {
      "ref": "abdelrhmanhosny/baseline-tfidf",
      "kernel_id": "18853258",
      "votes": 4
    },
    {
      "ref": "allohvk/comlit-tryst-with-han",
      "kernel_id": "18893067",
      "votes": 9
    },
    {
      "ref": "oleksandrsirenko/commonlit-text-cleaning",
      "kernel_id": "18835729",
      "votes": 9
    },
    {
      "ref": "juanscanlan/commonlit-inference",
      "kernel_id": "18583596",
      "votes": 0
    },
    {
      "ref": "romansafronenkov/commonlit-readability",
      "kernel_id": "18805104",
      "votes": 1
    },
    {
      "ref": "markwijkhuizen/simple-normal-wikipedia-abstract-dataset",
      "kernel_id": "18151742",
      "votes": 6
    },
    {
      "ref": "markwijkhuizen/simple-normal-wikipedia-abstracts-pretraining",
      "kernel_id": "18228395",
      "votes": 38
    },
    {
      "ref": "markwijkhuizen/simple-normal-wikipedia-sections-pretraining-v1",
      "kernel_id": "18714021",
      "votes": 17
    },
    {
      "ref": "bannourchaker/commonlit-readability-prize-part1-eda-confirmed",
      "kernel_id": "18744903",
      "votes": 3
    },
    {
      "ref": "bannourchaker/commonlit-readability-prize-part21-baseline-bert",
      "kernel_id": "18900437",
      "votes": 3
    },
    {
      "ref": "bannourchaker/commonlit-readability-prize-part2-baseline-bert",
      "kernel_id": "18881868",
      "votes": 5
    },
    {
      "ref": "bannourchaker/readability-tfidf-textstatis-count-feature-propipe",
      "kernel_id": "18932773",
      "votes": 3
    },
    {
      "ref": "atsushiiwasaki/clrp-stratify-on-predictability",
      "kernel_id": "18503345",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/clrp-stratify-on-target-s-e-bins-from-sturge-s",
      "kernel_id": "18454467",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/clrp-roberta-base-oof-task-fineturning-infer",
      "kernel_id": "18468419",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/split-based-on-target-standard-error",
      "kernel_id": "18423599",
      "votes": 0
    },
    {
      "ref": "atsushiiwasaki/clrp-roberta-large-att-mask-act-infer",
      "kernel_id": "18741834",
      "votes": 1
    },
    {
      "ref": "atsushiiwasaki/clrp-electra-large-att-conv1d-infer",
      "kernel_id": "18886424",
      "votes": 1
    },
    {
      "ref": "atsushiiwasaki/clrp-standard-error-feature-eda",
      "kernel_id": "18957224",
      "votes": 2
    },
    {
      "ref": "atsushiiwasaki/clrp-standard-error-as-a-feature",
      "kernel_id": "18947529",
      "votes": 0
    },
    {
      "ref": "stellaris98/commonlit-two-models",
      "kernel_id": "18426209",
      "votes": 3
    },
    {
      "ref": "artemglazunov1990/inference-with-roberta",
      "kernel_id": "18654238",
      "votes": 0
    },
    {
      "ref": "artemglazunov1990/transfer-learning-with-roberta-large",
      "kernel_id": "18654506",
      "votes": 2
    },
    {
      "ref": "artemglazunov1990/all-models-inference",
      "kernel_id": "18883851",
      "votes": 0
    },
    {
      "ref": "artemglazunov1990/roberta-base-svr-5-folds-training-skf",
      "kernel_id": "18830708",
      "votes": 0
    },
    {
      "ref": "artemglazunov1990/roberta-base-mp-xgb-5-folds-training-skf",
      "kernel_id": "18830755",
      "votes": 0
    },
    {
      "ref": "lukenguyen/basic-analysis",
      "kernel_id": "18352429",
      "votes": 5
    },
    {
      "ref": "kylecloud/commonlit-readability-distilbert-5folds",
      "kernel_id": "18450934",
      "votes": 13
    },
    {
      "ref": "takeshikobayashi/bronze-medal-solution-roberta-stacking-ensemble",
      "kernel_id": "18851026",
      "votes": 21
    },
    {
      "ref": "horsek/clrp-roberta-large-v31s-training-on-kaggle",
      "kernel_id": "18966273",
      "votes": 5
    },
    {
      "ref": "anushigulati/notebook5834e854bb",
      "kernel_id": "18275509",
      "votes": 0
    },
    {
      "ref": "masteryux/commonlit-nlp-data-augmentation",
      "kernel_id": "18944130",
      "votes": 11
    },
    {
      "ref": "yhirakawa/bertopic-visualization-of-topic-modeling",
      "kernel_id": "18530387",
      "votes": 43
    },
    {
      "ref": "bayartsogtya/structbert-train-script",
      "kernel_id": "18883626",
      "votes": 18
    },
    {
      "ref": "mayur7garg/common-lit-readability-similarity",
      "kernel_id": "18492320",
      "votes": 5
    },
    {
      "ref": "pranonraian/clrp-one-roberta-model",
      "kernel_id": "18455326",
      "votes": 4
    },
    {
      "ref": "bumjunkoo/comlit-inference-mp-attnhead",
      "kernel_id": "18862880",
      "votes": 1
    },
    {
      "ref": "sinatavakolibanizi/readability-feature-engineering-non-nn-baseline",
      "kernel_id": "18553602",
      "votes": 28
    },
    {
      "ref": "rohitgupta29/basic-eda",
      "kernel_id": "18239341",
      "votes": 0
    },
    {
      "ref": "rohitgupta29/basic-modelling-v1",
      "kernel_id": "18240866",
      "votes": 0
    },
    {
      "ref": "nikitaglazunov/simple-dnn-to-achieve-decent-score",
      "kernel_id": "18582054",
      "votes": 14
    },
    {
      "ref": "rajat95gupta/clrp-ensemble-3x-inference",
      "kernel_id": "18826385",
      "votes": 455
    },
    {
      "ref": "rajat95gupta/clrp-mean-pooling-inference",
      "kernel_id": "18665813",
      "votes": 159
    },
    {
      "ref": "rajat95gupta/mean-pooling-4-seeds",
      "kernel_id": "18654369",
      "votes": 102
    },
    {
      "ref": "rajat95gupta/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "18359483",
      "votes": 14
    },
    {
      "ref": "rajat95gupta/commonlit-ensemble-5x-inference",
      "kernel_id": "18350823",
      "votes": 8
    },
    {
      "ref": "rajat95gupta/commonlit-roberta-base-starter-implementation",
      "kernel_id": "18349589",
      "votes": 4
    },
    {
      "ref": "shubhamsindal0098/commonlit-readibility-prize",
      "kernel_id": "18824275",
      "votes": 4
    },
    {
      "ref": "tracyporter/commonlit-readibility-gaussianprocess",
      "kernel_id": "18426887",
      "votes": 4
    },
    {
      "ref": "sujithkpanoor/autonlp-finetune-save-weights",
      "kernel_id": "18221182",
      "votes": 0
    },
    {
      "ref": "sujithkpanoor/commonlit-readability-prize-roberta-torch-fit",
      "kernel_id": "18138216",
      "votes": 0
    },
    {
      "ref": "sujithkpanoor/roberta-finetune-046fb7",
      "kernel_id": "18578273",
      "votes": 1
    },
    {
      "ref": "vigneshmano/conv1d-test-1",
      "kernel_id": "18103643",
      "votes": 4
    },
    {
      "ref": "ivanajankov/lstm-nlp",
      "kernel_id": "18739749",
      "votes": 1
    },
    {
      "ref": "hosstell/notebookcf69c4ba48",
      "kernel_id": "18423443",
      "votes": 0
    },
    {
      "ref": "toutatsu/roberta-test",
      "kernel_id": "18568941",
      "votes": 0
    },
    {
      "ref": "afgawe/notebook6bff0b0cb5",
      "kernel_id": "18423972",
      "votes": 5
    },
    {
      "ref": "joechan619/svm-stacking-88th",
      "kernel_id": "18997821",
      "votes": 12
    },
    {
      "ref": "leolu1998/single-model-clrp-inference",
      "kernel_id": "18754362",
      "votes": 13
    },
    {
      "ref": "leolu1998/clrp-finetune-roberta-large",
      "kernel_id": "18738052",
      "votes": 4
    },
    {
      "ref": "leolu1998/commonlit-two-models-for-clrp",
      "kernel_id": "18831133",
      "votes": 13
    },
    {
      "ref": "leolu1998/clrp-pretrain-distilbert",
      "kernel_id": "18871865",
      "votes": 5
    },
    {
      "ref": "virajjayant/commonlit-readability-transformer-network",
      "kernel_id": "18575371",
      "votes": 5
    },
    {
      "ref": "virajjayant/commonlit-readability-transformers-part-2",
      "kernel_id": "18634654",
      "votes": 1
    },
    {
      "ref": "konstantinitsi/roberta-l-v3",
      "kernel_id": "18504876",
      "votes": 1
    },
    {
      "ref": "konstantinitsi/bert2-submit1",
      "kernel_id": "18453655",
      "votes": 2
    },
    {
      "ref": "alaasedeeq/object-oriented-programming-for-data-science",
      "kernel_id": "18878327",
      "votes": 250
    },
    {
      "ref": "sourabh3570/commonliteraturetarget",
      "kernel_id": "18407915",
      "votes": 0
    },
    {
      "ref": "ds572370/deep-evidential-regression",
      "kernel_id": "18115101",
      "votes": 26
    },
    {
      "ref": "devanshusingh/tensorflow-half-attention",
      "kernel_id": "18631630",
      "votes": 1
    },
    {
      "ref": "ankitsinghcoder/fork-of-commonreadability",
      "kernel_id": "18137798",
      "votes": 1
    },
    {
      "ref": "kaisen420/commonreadaility",
      "kernel_id": "18815495",
      "votes": 3
    },
    {
      "ref": "daviddirethucus/commonlit-readability-analysis",
      "kernel_id": "18228237",
      "votes": 4
    },
    {
      "ref": "kauvinlucas/commonlit-readability-prize-with-pretraining",
      "kernel_id": "18886486",
      "votes": 1
    },
    {
      "ref": "lucamtb/commonlit-transformer-train-and-inference",
      "kernel_id": "18135080",
      "votes": 4
    },
    {
      "ref": "thegreatjayant/deep-evidential-regression",
      "kernel_id": "18439917",
      "votes": 1
    },
    {
      "ref": "tanesan/use-masked-language-model-scoring",
      "kernel_id": "18958554",
      "votes": 6
    },
    {
      "ref": "calvchen/bert-regression-using-pytorch",
      "kernel_id": "18446773",
      "votes": 19
    },
    {
      "ref": "andretugan/commonlit-two-models",
      "kernel_id": "18254420",
      "votes": 884
    },
    {
      "ref": "andretugan/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "18235828",
      "votes": 578
    },
    {
      "ref": "andretugan/lightweight-roberta-solution-in-pytorch",
      "kernel_id": "18229702",
      "votes": 209
    },
    {
      "ref": "andretugan/commonlit-three-public-models",
      "kernel_id": "18878391",
      "votes": 6
    },
    {
      "ref": "kunihikofurugori/step2-1-tpu8foldtraining-rbase",
      "kernel_id": "18131357",
      "votes": 1
    },
    {
      "ref": "kunihikofurugori/step1-exclude-anomaly",
      "kernel_id": "18119096",
      "votes": 0
    },
    {
      "ref": "kunihikofurugori/readability-finalsub-model1",
      "kernel_id": "18981782",
      "votes": 3
    },
    {
      "ref": "kunihikofurugori/readability-finalsub-model2",
      "kernel_id": "18981802",
      "votes": 2
    },
    {
      "ref": "mbonnet/distilbert-fine-tuning-data-augmentation",
      "kernel_id": "18001656",
      "votes": 3
    },
    {
      "ref": "tutty1992/clrp-roberta-svm-roberta-large",
      "kernel_id": "18579055",
      "votes": 51
    },
    {
      "ref": "tutty1992/clrp-pytorch-roberta-inference-rbt-l-rbt-b-tpu",
      "kernel_id": "18502111",
      "votes": 1
    },
    {
      "ref": "tutty1992/clrp-pytorch-roberta-pretrain-roberta-large",
      "kernel_id": "18502131",
      "votes": 6
    },
    {
      "ref": "tutty1992/clrp-pytorch-roberta-finetune-roberta-large",
      "kernel_id": "18506765",
      "votes": 9
    },
    {
      "ref": "angyalfold/ensemble-for-commonlit",
      "kernel_id": "18972754",
      "votes": 1
    },
    {
      "ref": "angyalfold/roberta-large-with-custom-regressor-pytorch",
      "kernel_id": "18740581",
      "votes": 0
    },
    {
      "ref": "angyalfold/fine-tuning-roberta-large-on-clrp-data",
      "kernel_id": "18811142",
      "votes": 1
    },
    {
      "ref": "angyalfold/roberta-large-k-fold-models",
      "kernel_id": "18395187",
      "votes": 3
    },
    {
      "ref": "baokar7/clrp-distilrobertabase-training",
      "kernel_id": "18378944",
      "votes": 3
    },
    {
      "ref": "sunshineanyway/fork-of-commonlit-two-models-model1-w2-m2pred-df1",
      "kernel_id": "18862173",
      "votes": 3
    },
    {
      "ref": "pranjalchatterjee/bag-of-words-word-vectors-commonlit-readability",
      "kernel_id": "18256084",
      "votes": 10
    },
    {
      "ref": "dlaststark/commonlit-no-fancy-stuff",
      "kernel_id": "18048806",
      "votes": 6
    },
    {
      "ref": "maheller/universal-sentence-model-5-fold",
      "kernel_id": "18978201",
      "votes": 1
    },
    {
      "ref": "shankarviswanath/notebook92e8c50940",
      "kernel_id": "18496563",
      "votes": 0
    },
    {
      "ref": "leontrout/fork-of-useful",
      "kernel_id": "18995392",
      "votes": 14
    },
    {
      "ref": "maximkazantsev/commonlit-readability-roberta-lgbm",
      "kernel_id": "18643769",
      "votes": 10
    },
    {
      "ref": "maximkazantsev/commonlit-readability-univ-sent-encoder-xgb",
      "kernel_id": "18570800",
      "votes": 12
    },
    {
      "ref": "hserdaraltan/commonlitreadability-bert-train",
      "kernel_id": "18331485",
      "votes": 11
    },
    {
      "ref": "trnkhnhlinh/kali-baseline",
      "kernel_id": "18801816",
      "votes": 2
    },
    {
      "ref": "binhhuunguyen/attention-mean-pooling-roberta",
      "kernel_id": "18758048",
      "votes": 20
    },
    {
      "ref": "binhhuunguyen/pytorch-roberta-meanpooling-attention",
      "kernel_id": "18753802",
      "votes": 4
    },
    {
      "ref": "bigsmall/commonlit-two-models",
      "kernel_id": "18841543",
      "votes": 2
    },
    {
      "ref": "vungocbinh/commonlitreadabilityprize-bert",
      "kernel_id": "18335209",
      "votes": 1
    },
    {
      "ref": "vungocbinh/commonlitreadabilityprize-bert-autokeras",
      "kernel_id": "18917553",
      "votes": 1
    },
    {
      "ref": "charliezimmerman/clrp-inference-robertabase-robertalarge-ensemble",
      "kernel_id": "18983810",
      "votes": 5
    },
    {
      "ref": "k0mp0t/boosting-regression",
      "kernel_id": "18198993",
      "votes": 3
    },
    {
      "ref": "pichenguang/commonlit-two-models-with-new-model",
      "kernel_id": "18823810",
      "votes": 385
    },
    {
      "ref": "masatomurakawamm/clrp-roberta-simple-finetune-baseline-1",
      "kernel_id": "18365619",
      "votes": 14
    },
    {
      "ref": "masatomurakawamm/clrp-customized-robertamodel-fine-tuning",
      "kernel_id": "18436283",
      "votes": 6
    },
    {
      "ref": "yeahmove/clrp-ensemble-3x-inference",
      "kernel_id": "18874120",
      "votes": 7
    },
    {
      "ref": "alankadiev/why-am-i-getting-notebook-threw-exception-error",
      "kernel_id": "18810034",
      "votes": 4
    },
    {
      "ref": "rowenwitt/rough-spacy-vectors-simple-bidirectional-lstm",
      "kernel_id": "18234197",
      "votes": 18
    },
    {
      "ref": "idwivedi/huggingface-transformers-for-regression-colab",
      "kernel_id": "18008903",
      "votes": 15
    },
    {
      "ref": "markmario/fork-of-clrp-ensemble-3x-inference",
      "kernel_id": "18879514",
      "votes": 1
    },
    {
      "ref": "denniscarl/fork-of-fork-of-commonlit-two-models-model1-55",
      "kernel_id": "18836293",
      "votes": 1
    },
    {
      "ref": "junjitakeshima/beginner-s-gradual-improvement",
      "kernel_id": "18441609",
      "votes": 30
    },
    {
      "ref": "nitekot/commonlit-readability-lab2",
      "kernel_id": "18852803",
      "votes": 0
    },
    {
      "ref": "satyendrapandey16/notebook2bf07f2904",
      "kernel_id": "18039605",
      "votes": 1
    },
    {
      "ref": "yswysc/notebook1qa2ws3ed4rf0ok",
      "kernel_id": "18945385",
      "votes": 1
    },
    {
      "ref": "sylviacat1222/notebook7fe0fca25f",
      "kernel_id": "18104110",
      "votes": 2
    },
    {
      "ref": "aoduozhang/clrp-pytorch-roberta-inference",
      "kernel_id": "18503670",
      "votes": 3
    },
    {
      "ref": "aoduozhang/clrp-pytorch-roberta-finetune",
      "kernel_id": "18552226",
      "votes": 1
    },
    {
      "ref": "arkqaq/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "18842536",
      "votes": 0
    },
    {
      "ref": "arkqaq/clrp-ensemble-3x-inference",
      "kernel_id": "18837555",
      "votes": 1
    },
    {
      "ref": "mathislucka/commonlit-two-models",
      "kernel_id": "18350628",
      "votes": 12
    },
    {
      "ref": "chenhuang666/a-baseline-model-implemented-by-bert",
      "kernel_id": "18498873",
      "votes": 5
    },
    {
      "ref": "zmax505/notebook20b449257a",
      "kernel_id": "18859352",
      "votes": 0
    },
    {
      "ref": "shrey19/notebook89fa882372",
      "kernel_id": "18715903",
      "votes": 1
    },
    {
      "ref": "artificialmee/clrp-finetune-032fa4",
      "kernel_id": "18529396",
      "votes": 2
    },
    {
      "ref": "nicolesy/clrp-inference-b54382",
      "kernel_id": "18554160",
      "votes": 3
    },
    {
      "ref": "nicolesy/clrp-inference",
      "kernel_id": "18448548",
      "votes": 0
    },
    {
      "ref": "artificialmeee/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "18268388",
      "votes": 9
    },
    {
      "ref": "artificialmeee/notebook705621c585",
      "kernel_id": "18269917",
      "votes": 44
    },
    {
      "ref": "artificialmeee/readnet-inference",
      "kernel_id": "18759434",
      "votes": 7
    },
    {
      "ref": "artificialmeee/readnet",
      "kernel_id": "18756417",
      "votes": 7
    },
    {
      "ref": "legend123345/notebookf0cf8e8b72",
      "kernel_id": "18715891",
      "votes": 1
    },
    {
      "ref": "legend123345/bidirectional-lstm-spacy-embeddings",
      "kernel_id": "18911543",
      "votes": 3
    },
    {
      "ref": "legend123345/commonlit-transfer-learning-use",
      "kernel_id": "18861812",
      "votes": 2
    },
    {
      "ref": "yunfenghu/version0",
      "kernel_id": "18975599",
      "votes": 2
    },
    {
      "ref": "giangdnghong/mse-k8-group3-final-project",
      "kernel_id": "18987827",
      "votes": 1
    },
    {
      "ref": "minhhieuhoang/ease-read-prediction-nlp",
      "kernel_id": "18825028",
      "votes": 1
    },
    {
      "ref": "mabdelhamidmamin/tree-regression-with-dale-table",
      "kernel_id": "18811170",
      "votes": 9
    },
    {
      "ref": "lqtuantk97/group-4",
      "kernel_id": "18807639",
      "votes": 1
    },
    {
      "ref": "skynguyen3101/notebook20e7c65f48",
      "kernel_id": "18814890",
      "votes": 0
    },
    {
      "ref": "nicejared/fork-of-commonlit-two-models-model1-w2-m2-123w",
      "kernel_id": "18865388",
      "votes": 1
    },
    {
      "ref": "nicejared/fork-of-commonlit-two-models-model1-w2-m2pred-df1",
      "kernel_id": "18864092",
      "votes": 0
    },
    {
      "ref": "easyjaime/clrp-ensemble-3x-inference",
      "kernel_id": "18959717",
      "votes": 2
    },
    {
      "ref": "ruhong/on-stability-of-few-sample-transformer-fine-tuning",
      "kernel_id": "19947756",
      "votes": 1
    },
    {
      "ref": "kentaronakanishi/clrp-095-ensemble13-cv-kaggle93",
      "kernel_id": "19028246",
      "votes": 18
    },
    {
      "ref": "steubk/clrp-benchmark-your-model-with-onestopenglish",
      "kernel_id": "19143392",
      "votes": 8
    },
    {
      "ref": "nmkt6e6d6b74/training-roberta-large-using-cross-entropy",
      "kernel_id": "19216150",
      "votes": 5
    },
    {
      "ref": "davidmezzetti/commonlit-mean-pooled-regression",
      "kernel_id": "19096355",
      "votes": 3
    },
    {
      "ref": "takoihiraokazu/lb-ensemble-add-electra-base-bert-base2-t5-diba2",
      "kernel_id": "19029898",
      "votes": 55
    },
    {
      "ref": "takoihiraokazu/distilbart-save",
      "kernel_id": "19014783",
      "votes": 13
    },
    {
      "ref": "takoihiraokazu/final-sub1",
      "kernel_id": "19061883",
      "votes": 79
    },
    {
      "ref": "bond005/ensemble-after-readability-bert",
      "kernel_id": "19032558",
      "votes": 3
    },
    {
      "ref": "qinhui1999/readability-roberta-large-infer-pseudo-label",
      "kernel_id": "19055159",
      "votes": 17
    },
    {
      "ref": "kurupical/191-192-202-228-251-253-268-288-278-final",
      "kernel_id": "19020749",
      "votes": 15
    },
    {
      "ref": "jacob34/commonlit-create-target-samples",
      "kernel_id": "19132130",
      "votes": 1
    },
    {
      "ref": "iru538/clrp-10model-inference",
      "kernel_id": "19638826",
      "votes": 0
    },
    {
      "ref": "nyleve/3-roberta-electra-deberta-4-layer",
      "kernel_id": "19058990",
      "votes": 17
    },
    {
      "ref": "ananduk1993/ensemble-top-models",
      "kernel_id": "19033696",
      "votes": 8
    },
    {
      "ref": "gomesluiz/commonlit-readability-predict-distilbert-ft",
      "kernel_id": "19014505",
      "votes": 0
    },
    {
      "ref": "potatoc5/final-sub",
      "kernel_id": "19709860",
      "votes": 41
    },
    {
      "ref": "eneszvo/commonlit-summary-transformers-meta-labeling",
      "kernel_id": "19409898",
      "votes": 1
    },
    {
      "ref": "shinomoriaoshi/readability-training-notebook",
      "kernel_id": "19022918",
      "votes": 8
    },
    {
      "ref": "shinomoriaoshi/readability-model-architecture",
      "kernel_id": "19072530",
      "votes": 0
    },
    {
      "ref": "yogesh1190/rating-passage-reading-complexity",
      "kernel_id": "19000222",
      "votes": 0
    },
    {
      "ref": "o00585/1007rand",
      "kernel_id": "19020759",
      "votes": 0
    },
    {
      "ref": "yshrhr/clrp-roberta-stacking",
      "kernel_id": "19007900",
      "votes": 1
    },
    {
      "ref": "yshrhr/clrp-roberta-xgboost-optuna",
      "kernel_id": "19000452",
      "votes": 0
    },
    {
      "ref": "vlomme/commonlit-deberta-large",
      "kernel_id": "19071882",
      "votes": 7
    },
    {
      "ref": "riadalmadani/finetune-roberta-model-with-attention-head",
      "kernel_id": "19885026",
      "votes": 1
    },
    {
      "ref": "riadalmadani/finetune-roberta-evaluate-model-at-each-10-steps",
      "kernel_id": "19461523",
      "votes": 1
    },
    {
      "ref": "riadalmadani/infrerence-roberta-model",
      "kernel_id": "19513030",
      "votes": 2
    },
    {
      "ref": "riadalmadani/debertav3-tf-4-fold-model",
      "kernel_id": "19661371",
      "votes": 6
    },
    {
      "ref": "riadalmadani/increase-the-external-dataset-for-pseudo-labeling",
      "kernel_id": "19270920",
      "votes": 5
    },
    {
      "ref": "riadalmadani/infrerence-bert-model",
      "kernel_id": "19332097",
      "votes": 1
    },
    {
      "ref": "joeyaramouni/roberta-kfold-inference",
      "kernel_id": "19250227",
      "votes": 2
    },
    {
      "ref": "madhurrawat/complexity-mr",
      "kernel_id": "19008810",
      "votes": 1
    },
    {
      "ref": "aithammadiabdellatif/clrp-pytorch-roberta-finetune",
      "kernel_id": "19801245",
      "votes": 1
    },
    {
      "ref": "mananjhaveri/rank-88-clrp-lb-0-457-solution",
      "kernel_id": "19063980",
      "votes": 7
    },
    {
      "ref": "bannourchaker/transferlearning-bert-glove-xgb-lstm-attention",
      "kernel_id": "19095480",
      "votes": 16
    },
    {
      "ref": "ptrikp/distillbert-bert-and-lgbm",
      "kernel_id": "19136672",
      "votes": 0
    },
    {
      "ref": "joshuatalamayan/dasc-project-commonlit-readability-eda",
      "kernel_id": "19547877",
      "votes": 1
    },
    {
      "ref": "jizhouliu/fork-of-fork-of-roberta-base",
      "kernel_id": "19469083",
      "votes": 1
    },
    {
      "ref": "jizhouliu/pre-trained-roberta-solution-in-pytorch-b8013f",
      "kernel_id": "19448874",
      "votes": 2
    },
    {
      "ref": "jizhouliu/linear-baseline-with-cv",
      "kernel_id": "19264487",
      "votes": 0
    },
    {
      "ref": "jizhouliu/robertabase-aux-inference",
      "kernel_id": "19344644",
      "votes": 0
    },
    {
      "ref": "jizhouliu/fork-of-roberta-base",
      "kernel_id": "19368987",
      "votes": 1
    },
    {
      "ref": "jizhouliu/roberta-base-finetune-pretrain",
      "kernel_id": "19014793",
      "votes": 7
    },
    {
      "ref": "denisvorobyev/dug-up-some-useful-features-mse-0-25-on-public-ds",
      "kernel_id": "19051552",
      "votes": 1
    },
    {
      "ref": "omkargangan/commonlit-readability-competition",
      "kernel_id": "19021064",
      "votes": 2
    },
    {
      "ref": "jaydeep2401/commonlitreadability",
      "kernel_id": "19017621",
      "votes": 0
    },
    {
      "ref": "mohamedbakrey/eda-for-commonlit-rp-ml-predict",
      "kernel_id": "19436947",
      "votes": 20
    },
    {
      "ref": "ruoxijia/clrp-mp-xgb",
      "kernel_id": "19000484",
      "votes": 10
    },
    {
      "ref": "trnkhnhlinh/kali-word-embedding",
      "kernel_id": "19257156",
      "votes": 0
    },
    {
      "ref": "charliezimmerman/clrp-train-robertabase-maskedlm-model",
      "kernel_id": "19378883",
      "votes": 4
    },
    {
      "ref": "charliezimmerman/clrp-finetune-trained-robertabase",
      "kernel_id": "19628715",
      "votes": 2
    },
    {
      "ref": "charliezimmerman/clrp-finetune-trained-robertalarge",
      "kernel_id": "19776333",
      "votes": 2
    },
    {
      "ref": "charliezimmerman/clrp-train-robertalarge-masked-lm-model",
      "kernel_id": "19390995",
      "votes": 2
    },
    {
      "ref": "pichenguang/fork-of-commonlit",
      "kernel_id": "19041626",
      "votes": 2
    },
    {
      "ref": "liuyuanjiang/test-model",
      "kernel_id": "19021433",
      "votes": 1
    },
    {
      "ref": "nitekot/notebook3d5cda6fca",
      "kernel_id": "19044604",
      "votes": 0
    },
    {
      "ref": "yunfenghu/30randversion0",
      "kernel_id": "19020829",
      "votes": 0
    },
    {
      "ref": "lqtuantk97/group-4-fsb-lasso",
      "kernel_id": "19201083",
      "votes": 1
    },
    {
      "ref": "lqtuantk97/group-4-fsb-ridge",
      "kernel_id": "19203706",
      "votes": 0
    },
    {
      "ref": "rhtsingh/all-the-ways-you-can-compress-transformers",
      "kernel_id": "20276603",
      "votes": 167
    },
    {
      "ref": "pdan93/roberta-base-pretrained",
      "kernel_id": "20316345",
      "votes": 5
    },
    {
      "ref": "pdan93/mantis-clrp-demo",
      "kernel_id": "20908240",
      "votes": 4
    },
    {
      "ref": "crprpr/clasificaci-n-de-texto-tensorflow-transformers",
      "kernel_id": "20429762",
      "votes": 6
    },
    {
      "ref": "marcodemeco/commonlit-project",
      "kernel_id": "20341973",
      "votes": 3
    },
    {
      "ref": "mohamadakram/commonlit-bert-submission",
      "kernel_id": "20489233",
      "votes": 10
    },
    {
      "ref": "mathislucka/fork-of-commonlitreadability-r3-2a94de",
      "kernel_id": "20450902",
      "votes": 3
    },
    {
      "ref": "hoxqzj/commonlit-readability-prize-competition",
      "kernel_id": "20213891",
      "votes": 0
    },
    {
      "ref": "steve5438/commonlit-baseline-model",
      "kernel_id": "21887158",
      "votes": 5
    },
    {
      "ref": "steve5438/commonlit-deep-learning-model",
      "kernel_id": "22069649",
      "votes": 3
    },
    {
      "ref": "tealgreen0503/commonlit-roberta-huggingface-trainer",
      "kernel_id": "22500999",
      "votes": 12
    },
    {
      "ref": "ajaypawar123/training-text-compelxity",
      "kernel_id": "22880896",
      "votes": 0
    },
    {
      "ref": "ajaypawar123/inference-text-complexity",
      "kernel_id": "22879985",
      "votes": 0
    },
    {
      "ref": "nguyntrngngha/nlps-submissions",
      "kernel_id": "22485256",
      "votes": 0
    },
    {
      "ref": "mst8823/baseline-001-infer",
      "kernel_id": "22487812",
      "votes": 2
    },
    {
      "ref": "debanshubiswas/ml-project-2",
      "kernel_id": "22312016",
      "votes": 1
    },
    {
      "ref": "aritribasu/roberta-readability",
      "kernel_id": "22155772",
      "votes": 0
    },
    {
      "ref": "gunjanshinde/readability-bert",
      "kernel_id": "22901756",
      "votes": 4
    },
    {
      "ref": "hasebetakanori/easy-machine-learning",
      "kernel_id": "22446750",
      "votes": 7
    },
    {
      "ref": "pietromarinelli/base-train-commonlit",
      "kernel_id": "23273790",
      "votes": 0
    },
    {
      "ref": "pietromarinelli/baseline-inference",
      "kernel_id": "23273796",
      "votes": 0
    },
    {
      "ref": "pietromarinelli/soluzione-medaglia-argento",
      "kernel_id": "23974413",
      "votes": 1
    },
    {
      "ref": "kroha5/nn-notebook-lstm",
      "kernel_id": "23583743",
      "votes": 17
    },
    {
      "ref": "ammarabbasi1040/commonlit-simple-eda-fe-ml",
      "kernel_id": "24033449",
      "votes": 24
    },
    {
      "ref": "davawalamanan/irs-lab-2",
      "kernel_id": "24502308",
      "votes": 1
    },
    {
      "ref": "cenzop/r-reticulate-longformer-and-readability",
      "kernel_id": "26961993",
      "votes": 4
    },
    {
      "ref": "cenzop/sentence-transformers-mpnetbase-no-tuning",
      "kernel_id": "26959821",
      "votes": 2
    },
    {
      "ref": "fqpang/how-to-install-pytorch-1-9-0",
      "kernel_id": "26092210",
      "votes": 41
    },
    {
      "ref": "hengwdai/commonlit-readability-auto3ml",
      "kernel_id": "26889915",
      "votes": 4
    },
    {
      "ref": "hengwdai/commonlit-readability-h2o",
      "kernel_id": "26890234",
      "votes": 3
    },
    {
      "ref": "hengwdai/commonlit-readability-autogluon",
      "kernel_id": "26890373",
      "votes": 3
    },
    {
      "ref": "hengwdai/mercariprice-h2o",
      "kernel_id": "26891277",
      "votes": 3
    },
    {
      "ref": "hengwdai/mercariprice-auto3ml",
      "kernel_id": "26890779",
      "votes": 8
    },
    {
      "ref": "hengwdai/commonlit-readability-data-split",
      "kernel_id": "26905886",
      "votes": 3
    },
    {
      "ref": "hengwdai/quickstart-auto3ml-nlp",
      "kernel_id": "26933996",
      "votes": 3
    },
    {
      "ref": "dpai12/notebook-1",
      "kernel_id": "26979450",
      "votes": 0
    },
    {
      "ref": "supto137617/commonlit-readability-with-roberta",
      "kernel_id": "27200497",
      "votes": 0
    },
    {
      "ref": "dpai12/notebook-3",
      "kernel_id": "27057825",
      "votes": 10
    },
    {
      "ref": "abee82/amex-using-fast-ai",
      "kernel_id": "28136711",
      "votes": 38
    },
    {
      "ref": "szymonsmg/bertmodelling",
      "kernel_id": "28268997",
      "votes": 2
    },
    {
      "ref": "dklvch/mytesttask",
      "kernel_id": "29485101",
      "votes": 0
    },
    {
      "ref": "yagli18/commonlit-readability-prize",
      "kernel_id": "29934775",
      "votes": 3
    },
    {
      "ref": "akshatpattiwar/fake-real-news-detection",
      "kernel_id": "29946341",
      "votes": 12
    },
    {
      "ref": "azminetoushikwasi/regression-eda-stacking-ensembling",
      "kernel_id": "29631911",
      "votes": 96
    },
    {
      "ref": "neilz0211/fork-of-clrp-inference-emsemble-83bac6",
      "kernel_id": "29552987",
      "votes": 2
    },
    {
      "ref": "javigallego/deberta-from-the-ground-up-2-approaches",
      "kernel_id": "30659012",
      "votes": 80
    },
    {
      "ref": "gabriellegaudeau/l101-mybert",
      "kernel_id": "33373223",
      "votes": 1
    },
    {
      "ref": "volodymyrshulga/notebook547e10240d",
      "kernel_id": "34664237",
      "votes": 2
    },
    {
      "ref": "mahluo/pre-trained-roberta-solution-in-pytorch",
      "kernel_id": "35835591",
      "votes": 5
    },
    {
      "ref": "mahluo/pytorch-roberta-pretrain",
      "kernel_id": "35834725",
      "votes": 0
    },
    {
      "ref": "siqicao/commonlit-bert-submission",
      "kernel_id": "35950850",
      "votes": 5
    },
    {
      "ref": "renderhp/distilbert-try",
      "kernel_id": "36208413",
      "votes": 0
    },
    {
      "ref": "natenten/readability-scoring",
      "kernel_id": "37132453",
      "votes": 2
    },
    {
      "ref": "suraj520/roberta-base-know-train-infer",
      "kernel_id": "37794627",
      "votes": 9
    },
    {
      "ref": "manikjatana/commonlit-bert-submission",
      "kernel_id": "37549379",
      "votes": 0
    },
    {
      "ref": "manikjatana/bert-for-regression",
      "kernel_id": "37521146",
      "votes": 0
    },
    {
      "ref": "ineed21eep/commonlitreadability-r3",
      "kernel_id": "38202094",
      "votes": 3
    },
    {
      "ref": "varunmalhotra09/clrp-gpt2-implementation",
      "kernel_id": "39969370",
      "votes": 4
    },
    {
      "ref": "forrestluo/commonlit-readability-roberta-fit",
      "kernel_id": "39788646",
      "votes": 0
    },
    {
      "ref": "suraj520/bart-know-train-infer",
      "kernel_id": "40266227",
      "votes": 9
    },
    {
      "ref": "vikramsandu/commonlit-mlm",
      "kernel_id": "41600870",
      "votes": 17
    },
    {
      "ref": "thedrcat/datasets-wheels",
      "kernel_id": "42818129",
      "votes": 16
    },
    {
      "ref": "uygarkk/youtube-bert",
      "kernel_id": "42031395",
      "votes": 23
    },
    {
      "ref": "lorentzyeung/commonlit-readability-eda-roberta-param-tune",
      "kernel_id": "43798353",
      "votes": 8
    },
    {
      "ref": "vinitkp/text-readability-formulae",
      "kernel_id": "43369790",
      "votes": 2
    },
    {
      "ref": "yuriihavrylko/readability-score",
      "kernel_id": "43219159",
      "votes": 3
    },
    {
      "ref": "xuankhaitrinh/vlsp-ocr",
      "kernel_id": "43534064",
      "votes": 8
    },
    {
      "ref": "focusleft/commonlit-readability-lstm-with-sentence-features",
      "kernel_id": "44585717",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-lstm-with-sentence-pos",
      "kernel_id": "44585727",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-lstm-with-sent-pos-lang",
      "kernel_id": "44585737",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-roberta",
      "kernel_id": "44586585",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-roberta-with-sent-features",
      "kernel_id": "44586595",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-roberta-with-sent-pos",
      "kernel_id": "44586603",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-roberta-with-sent-pos-lang",
      "kernel_id": "44586605",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-lstm",
      "kernel_id": "44585582",
      "votes": 0
    },
    {
      "ref": "focusleft/commonlit-readability-xgboost",
      "kernel_id": "44614831",
      "votes": 0
    },
    {
      "ref": "munashesamapundo/samapundo-munashe",
      "kernel_id": "47883789",
      "votes": 0
    },
    {
      "ref": "yuriykim/custom-model",
      "kernel_id": "48409511",
      "votes": 2
    },
    {
      "ref": "yuriykim/lightautoml",
      "kernel_id": "48456020",
      "votes": 0
    },
    {
      "ref": "shubhbansal9/prompt-tuning-bert",
      "kernel_id": "50510581",
      "votes": 7
    },
    {
      "ref": "sparsh35/creating-whl-files-to-install-external-libr-38ff72",
      "kernel_id": "55212119",
      "votes": 0
    },
    {
      "ref": "aplycaebous/tb-banglabert-fpt",
      "kernel_id": "56320416",
      "votes": 4
    },
    {
      "ref": "cardata/commonlit-readability-prize-analysis",
      "kernel_id": "56280793",
      "votes": 1
    },
    {
      "ref": "yaramamdouh/commonlit-readability-lstm-gru",
      "kernel_id": "67242639",
      "votes": 2
    },
    {
      "ref": "ayush07jaiswal/commonlit-readabilty-sub-4",
      "kernel_id": "73414738",
      "votes": 0
    },
    {
      "ref": "viktorurushkin/xlm-roberta-pytorch",
      "kernel_id": "74397586",
      "votes": 1
    },
    {
      "ref": "onnoboru/commonlit-readability-baseline",
      "kernel_id": "74217862",
      "votes": 9
    },
    {
      "ref": "lime1123/commonlit-v2-bert",
      "kernel_id": "74262625",
      "votes": 1
    },
    {
      "ref": "lime1123/commonlit-v2-deberta-submission",
      "kernel_id": "74426172",
      "votes": 1
    },
    {
      "ref": "csmohamedayman/eda-txt-commonlit-readability-prize",
      "kernel_id": "77789751",
      "votes": 1
    },
    {
      "ref": "csmohamedayman/ml-numc-nlp-commonlit-readability-prize",
      "kernel_id": "77791010",
      "votes": 3
    },
    {
      "ref": "enginetinta/excerpt-score-prediction-with-bert",
      "kernel_id": "77251636",
      "votes": 2
    },
    {
      "ref": "alemzhann/basic-roberta",
      "kernel_id": "85891111",
      "votes": 0
    },
    {
      "ref": "dmitryvolovikov/commonlit-readability-prize",
      "kernel_id": "88376022",
      "votes": 1
    },
    {
      "ref": "emrullahyediyildiz/commonlit-readability-prize",
      "kernel_id": "90196390",
      "votes": 2
    },
    {
      "ref": "lunhuanzha/utilizing-transformer-representations-efficiently",
      "kernel_id": "98871699",
      "votes": 0
    },
    {
      "ref": "taseensadatteam/utilizing-transformer-representations-efficiently",
      "kernel_id": "106236935",
      "votes": 1
    },
    {
      "ref": "eymanurserdar/competition-notebook",
      "kernel_id": "116772958",
      "votes": 0
    },
    {
      "ref": "hahahaeun1232/commonlit",
      "kernel_id": "120116121",
      "votes": 0
    },
    {
      "ref": "hanyeseo/notebookf20a12d004",
      "kernel_id": "120659374",
      "votes": 2
    },
    {
      "ref": "mdshafayeturrahman/commonlit-readability-nlp",
      "kernel_id": "127585704",
      "votes": 1
    }
  ],
  "count": 1130
}