bert_test_2.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":648,"status":"ok","timestamp":1662040617333,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"4IgHLLU4TZoY"},"outputs":[],"source":["import os\n","\n","class Config:\n","    AUTHOR = \"colum2131\"\n","\n","    # 出力フォルダ名\n","    NAME = \"test-2-bert-deberta-base-epoch10\"\n","\n","    # 学習するモデルの読み込み\n","    # https://huggingface.co/ からモデルのパスを指定\n","    # 例えば, \"microsoft/deberta-base\"\n","    MODEL_PATH = \"microsoft/deberta-base\"\n","\n","    # ベースとなるディレクトリパスの指定\n","    COLAB_PATH = \"/content/drive/MyDrive/signate/MUFG Data Science Champion Ship\" \n","    DRIVE_PATH = \"/content/drive/MyDrive/signate/MUFG Data Science Champion Ship\" \n","    # DRIVE_PATH = os.path.join(COLAB_PATH, AUTHOR)\n","\n","    # シード値\n","    seed = 42\n","    \n","    # cross-validaitonの分割数\n","    num_fold = 3\n","    # 学習するfold\n","    trn_fold = [0, 1, 2]\n","    \n","    # batct_sizeの設定\n","    batch_size = 32\n","    \n","    # epoch数の設定\n","    n_epochs = 5\n","    \n","    # トークン数の最大の長さの設定\n","    max_len = 128\n","\n","    # 学習率の設定\n","    lr = 2e-5\n","\n","    # optimizer等の設定\n","    weight_decay = 2e-5\n","    beta = (0.9, 0.98)\n","    num_warmup_steps_rate = 0.01\n","    clip_grad_norm = None\n","    gradient_accumulation_steps = 1\n","    num_eval = 1"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":1232,"status":"ok","timestamp":1662040618562,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"k80sv8KLTs6S"},"outputs":[],"source":["# ========================================\n","# Library\n","# ========================================\n","import os\n","import gc\n","import re\n","import sys\n","import json\n","import time\n","import shutil\n","import joblib\n","import random\n","import requests\n","import warnings\n","warnings.filterwarnings('ignore')\n","from ast import literal_eval\n","\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","import scipy \n","import itertools\n","from pathlib import Path\n","from glob import glob\n","from tqdm.auto import tqdm\n","from sklearn.model_selection import (\n","    StratifiedKFold, \n","    KFold, \n","    GroupKFold,\n","    StratifiedGroupKFold,\n",")\n","from sklearn.metrics import (\n","    accuracy_score, \n","    f1_score,\n","    roc_auc_score,\n",")\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torch.utils.data import Dataset, DataLoader, Subset\n","from torch.cuda.amp import autocast, GradScaler\n","\n","from google.colab import drive\n","if not os.path.isdir('/content/drive'):\n","    drive.mount('/content/drive') "]},{"cell_type":"code","source":["def cleaning(texts):\n","  clean_texts = []\n","  for text in texts:\n","    text = remove_tag(text)\n","    clean_texts.append(text)\n","  return clean_texts\n","\n","def remove_tag(x):\n","  p = re.compile(r\"<[^>]*?>\")\n","  return p.sub('',x)"],"metadata":{"id":"C7Ip4COF9CCT","executionInfo":{"status":"ok","timestamp":1662040618562,"user_tz":-540,"elapsed":12,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1662040618563,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"DtB2iSqUTw7m"},"outputs":[],"source":["def setup(cfg):\n","    cfg.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","\n","    # mount\n","    from google.colab import drive\n","    if not os.path.isdir('/content/drive'):\n","        drive.mount('/content/drive') \n","\n","    # pip install\n","    ! pip install -q transformers\n","    ! pip install -q sentencepiece\n","\n","    # set dirs\n","    cfg.DRIVE = cfg.DRIVE_PATH\n","    cfg.EXP = (cfg.NAME if cfg.NAME is not None \n","        else requests.get('http://172.28.0.2:9000/api/sessions').json()[0]['name'][:-6]\n","    )\n","    cfg.INPUT = os.path.join(cfg.DRIVE, 'Input')\n","    cfg.OUTPUT = os.path.join(cfg.DRIVE, 'Oututput')\n","    cfg.DATASET = os.path.join(cfg.DRIVE, 'Dataset')\n","\n","    cfg.OUTPUT_EXP = os.path.join(cfg.OUTPUT, cfg.EXP) \n","    cfg.EXP_MODEL = os.path.join(cfg.OUTPUT_EXP, 'model')\n","    cfg.EXP_FIG = os.path.join(cfg.OUTPUT_EXP, 'fig')\n","    cfg.EXP_PREDS = os.path.join(cfg.OUTPUT_EXP, 'preds')\n","\n","    # make dirs\n","    for d in [cfg.INPUT, cfg.EXP_MODEL, cfg.EXP_FIG, cfg.EXP_PREDS]:\n","        os.makedirs(d, exist_ok=True)\n","    return cfg"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1662040618563,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"Kfvu1-LdT0Wb"},"outputs":[],"source":["# =====================\n","# Utils\n","# =====================\n","# Seed\n","def set_seed(seed=42):\n","    random.seed(seed)\n","    os.environ['PYTHONHASHSEED'] = str(seed)\n","    np.random.seed(seed)\n","    torch.manual_seed(seed)\n","    torch.cuda.manual_seed(seed)\n","    torch.backends.cudnn.deterministic = True\n","\n","# KFold\n","def get_stratifiedkfold(train, target_col, n_splits, seed):\n","    kf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=seed)\n","    generator = kf.split(train, train[target_col])\n","    fold_series = []\n","    for fold, (idx_train, idx_valid) in enumerate(generator):\n","        fold_series.append(pd.Series(fold, index=idx_valid))\n","    fold_series = pd.concat(fold_series).sort_index()\n","    return fold_series\n","\n","# collatte\n","def collatte(inputs, labels=None):\n","    mask_len = int(inputs[\"attention_mask\"].sum(axis=1).max())\n","    if not labels is None:\n","        inputs = {\n","            \"input_ids\" : inputs['input_ids'][:,:mask_len],\n","            \"attention_mask\" : inputs['attention_mask'][:,:mask_len],\n","        }\n","        labels =  labels[:,:mask_len]\n","        return inputs, labels, mask_len\n","                \n","    else:\n","        inputs = {\n","            \"input_ids\" : inputs['input_ids'][:,:mask_len],\n","            \"attention_mask\" : inputs['attention_mask'][:,:mask_len],\n","        }\n","        return inputs, mask_len"]},{"cell_type":"code","execution_count":6,"metadata":{"executionInfo":{"elapsed":9,"status":"ok","timestamp":1662040618564,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"eyHPUDxQT8hq"},"outputs":[],"source":["# =====================\n","# Dataset & Model\n","# =====================\n","class BERTDataset(Dataset):\n","    def __init__(self, cfg, texts, labels=None):\n","        self.cfg = cfg\n","        self.texts = texts\n","        self.labels = labels\n","\n","    def __len__(self):\n","        return len(self.texts)\n","\n","    def __getitem__(self, index):\n","        inputs = self.prepare_input(self.cfg, self.texts[index])\n","        if self.labels is not None:\n","            label = torch.tensor(self.labels[index], dtype=torch.int64)\n","            return inputs, label\n","        else:\n","            return inputs\n","    \n","    @staticmethod\n","    def prepare_input(cfg, text):\n","        inputs = cfg.tokenizer(\n","            text,\n","            add_special_tokens=True,\n","            max_length=cfg.max_len,\n","            padding=\"max_length\",\n","            truncation=True,\n","            return_offsets_mapping=False,\n","        )\n","        for k, v in inputs.items():\n","            inputs[k] = torch.tensor(v, dtype=torch.long)\n","        return inputs\n","\n","\n","class BERTModel(nn.Module):\n","    def __init__(self, cfg, criterion=None):\n","        super().__init__()\n","        self.cfg = cfg\n","        self.criterion = criterion\n","        self.config = AutoConfig.from_pretrained(\n","            cfg.MODEL_PATH,\n","            output_hidden_states=True\n","        )\n","        self.backbone = AutoModel.from_pretrained(\n","            cfg.MODEL_PATH, \n","            config=self.config\n","        )\n","        self.fc = nn.Sequential(\n","            nn.Linear(self.config.hidden_size, 2),\n","        )\n","    \n","    def forward(self, inputs, labels=None):\n","        outputs = self.backbone(**inputs)[\"last_hidden_state\"]\n","        outputs = outputs[:, 0, :]\n","        if labels is not None:\n","            logits = self.fc(outputs)\n","            loss = self.criterion(logits, labels)\n","            return logits, loss\n","        else:\n","            logits = self.fc(outputs)\n","            return logits"]},{"cell_type":"code","execution_count":7,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1662040618565,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"YcCZRBCFT-8l"},"outputs":[],"source":["def training(cfg, train):\n","    # =====================\n","    # Training\n","    # =====================\n","    set_seed(cfg.seed)\n","    oof_pred = np.zeros((len(train), 2), dtype=np.float32)\n","    \n","    # 損失関数\n","    criterion = nn.CrossEntropyLoss()\n","\n","    for fold in cfg.trn_fold:\n","        print('-'*20, fold, '-'*20)\n","        # Dataset,Dataloaderの設定\n","        train_df = train.loc[cfg.folds!=fold]\n","        valid_df = train.loc[cfg.folds==fold]\n","        train_idx = list(train_df.index)\n","        valid_idx = list(valid_df.index)\n","\n","        train_dataset = BERTDataset(\n","            cfg,\n","            train_df['html_content'].to_numpy(), \n","            train_df['state'].to_numpy(),\n","        )\n","        valid_dataset = BERTDataset(\n","            cfg, \n","            valid_df['html_content'].to_numpy(), \n","            valid_df['state'].to_numpy()\n","        )\n","        train_loader = DataLoader(\n","            dataset=train_dataset, \n","            batch_size=cfg.batch_size, \n","            shuffle=True,\n","            pin_memory=True,\n","            drop_last=True\n","        )\n","        valid_loader = DataLoader(\n","            dataset=valid_dataset,\n","            batch_size=cfg.batch_size,\n","            shuffle=False,\n","            pin_memory=True,\n","            drop_last=False\n","        )\n","\n","        # 初期化\n","        best_val_preds = None\n","        best_val_score = -1\n","\n","        # modelの読み込み\n","        model = BERTModel(cfg, criterion)\n","        model = model.to(cfg.device)\n","\n","        # optimizer，schedulerの設定\n","        param_optimizer = list(model.named_parameters())\n","        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n","        optimizer_grouped_parameters = []\n","        optimizer_grouped_parameters.append({\n","            'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], \n","            'weight_decay': cfg.weight_decay\n","        })\n","        optimizer_grouped_parameters.append({\n","            'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], \n","            'weight_decay': 0.0\n","        })\n","        optimizer = AdamW(\n","            optimizer_grouped_parameters,\n","            lr=cfg.lr,\n","            betas=cfg.beta,\n","            weight_decay=cfg.weight_decay,\n","        )\n","        num_train_optimization_steps = int(\n","            len(train_loader) * cfg.n_epochs // cfg.gradient_accumulation_steps\n","        )\n","        num_warmup_steps = int(num_train_optimization_steps * cfg.num_warmup_steps_rate)\n","        scheduler = get_linear_schedule_with_warmup(\n","            optimizer,\n","            num_warmup_steps=num_warmup_steps,\n","            num_training_steps=num_train_optimization_steps\n","        )\n","        num_eval_step = len(train_loader) // cfg.num_eval + cfg.num_eval\n","        \n","        for epoch in range(cfg.n_epochs):\n","            # training\n","            print(f\"# ============ start epoch:{epoch} ============== #\")\n","            model.train() \n","            val_losses_batch = []\n","            scaler = GradScaler()\n","            with tqdm(train_loader, total=len(train_loader)) as pbar:\n","                for step, (inputs, labels) in enumerate(pbar):\n","                    inputs, max_len = collatte(inputs)\n","                    for k, v in inputs.items():\n","                        inputs[k] = v.to(cfg.device)\n","                    labels = labels.to(cfg.device)\n","\n","                    optimizer.zero_grad()\n","                    with autocast():\n","                        output, loss = model(inputs, labels)\n","                    pbar.set_postfix({\n","                        'loss': loss.item(),\n","                        'lr': scheduler.get_lr()[0]\n","                    })\n","\n","                    if cfg.gradient_accumulation_steps > 1:\n","                        loss = loss / cfg.gradient_accumulation_steps\n","                    scaler.scale(loss).backward()\n","                    if cfg.clip_grad_norm is not None:\n","                        torch.nn.utils.clip_grad_norm_(\n","                            model.parameters(), \n","                            cfg.clip_grad_norm\n","                        )\n","                    if (step+1) % cfg.gradient_accumulation_steps == 0:\n","                        scaler.step(optimizer)\n","                        scaler.update()\n","                        scheduler.step()\n","                \n","            # evaluating\n","            val_preds = []\n","            val_losses = []\n","            val_nums = []\n","            model.eval()\n","            with torch.no_grad():\n","                with tqdm(valid_loader, total=len(valid_loader)) as pbar:\n","                    for (inputs, labels) in pbar:\n","                        inputs, max_len = collatte(inputs)\n","                        for k, v in inputs.items():\n","                            inputs[k] = v.to(cfg.device)\n","                        labels = labels.to(cfg.device)\n","                        with autocast():\n","                            output, loss = model(inputs, labels)\n","                        output = output.sigmoid().detach().cpu().numpy()\n","                        val_preds.append(output)\n","                        val_losses.append(loss.item() * len(labels))\n","                        val_nums.append(len(labels))\n","                        pbar.set_postfix({\n","                            'val_loss': loss.item()\n","                        })\n","\n","            val_preds = np.concatenate(val_preds)\n","            val_loss = sum(val_losses) / sum(val_nums)\n","            score = f1_score(np.argmax(val_preds, axis=1), valid_df['state'], average='macro')\n","            val_log = {\n","                'val_loss': val_loss,\n","                'score': score,\n","            }\n","            display(val_log)\n","            if best_val_score < score:\n","                print(\"save model weight\")\n","                best_val_preds = val_preds\n","                best_val_score = score\n","                torch.save(\n","                    model.state_dict(), \n","                    os.path.join(cfg.EXP_MODEL, f\"fold{fold}.pth\")\n","                )\n","\n","        oof_pred[valid_idx] = best_val_preds.astype(np.float32)\n","        np.save(os.path.join(cfg.EXP_PREDS, f'oof_pred_fold{fold}.npy'), best_val_preds)\n","        del model; gc.collect()\n","\n","    # scoring\n","    np.save(os.path.join(cfg.EXP_PREDS, 'oof_pred.npy'), oof_pred)\n","    score = f1_score(np.argmax(oof_pred, axis=1), train['state'], average='macro')\n","    print('CV:', round(score, 5))\n","    return score"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":510,"status":"ok","timestamp":1662040619066,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"},"user_tz":-540},"id":"ChCzELEkUCS7"},"outputs":[],"source":["def inferring(cfg, test):\n","    print('\\n'.join(cfg.model_weights))\n","    sub_pred = np.zeros((len(test), 2), dtype=np.float32)\n","    for fold, model_weight in enumerate(cfg.model_weights):\n","        # dataset, dataloader\n","        test_dataset = BERTDataset(\n","            cfg,\n","            test['html_content'].to_numpy()\n","        )\n","        test_loader = DataLoader(\n","            dataset=test_dataset, \n","            batch_size=cfg.batch_size, \n","            shuffle=False,\n","            pin_memory=True\n","        )\n","        model = BERTModel(cfg)\n","        model.load_state_dict(torch.load(model_weight))\n","        model = model.to(cfg.device)\n","\n","        model.eval()\n","        fold_pred = []\n","        with torch.no_grad():\n","            for inputs in tqdm(test_loader, total=len(test_loader)):\n","                inputs, max_len = collatte(inputs)\n","                for k, v in inputs.items():\n","                    inputs[k] = v.to(cfg.device)\n","                with autocast():\n","                    output = model(inputs)\n","                output = output.softmax(axis=1).detach().cpu().numpy()\n","                fold_pred.append(output)\n","        fold_pred = np.concatenate(fold_pred)\n","        np.save(os.path.join(cfg.EXP_PREDS, f'sub_pred_fold{fold}.npy'), fold_pred)\n","        sub_pred += fold_pred / len(cfg.model_weights)\n","        del model; gc.collect()\n","    np.save(os.path.join(cfg.EXP_PREDS, f'sub_pred.npy'), sub_pred)\n","    return sub_pred"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["2354df7ce67147f7ba31c6f1efadc9d5","85d8801d5e864f729b05855307ed4adc","dceb2cd5f4334fda9cd724fe55770a07","6cb4cd9f80094c89817cf44437d36251","1ec06e031282422f9b087360317696af","a8f7f9118331402282dddec4aea39b59","e89403d674864b2480e1313ab8ac014e","9b193077fad54783a27e1dd1e72e1abb","966f87b0f7b6400c83d768978d304125","e02b4e9564e240209f781b9600cb4349","e67c54c3493b43fabc94acc5b5bf6a87","8923d422e0e14f73a0e3dc69022b6751","b2e174a42b1f4dd5a55c77632270fb73","0bd56e868bba4a90a45be5c17e40fc51","da965a2e315e479980fb3ab041f54d15","b4a7bfcb2f134a399a72142eaf67ed13","be81523af85846719a1478835d6f955a","530d501782644f92a276e5fa0c2151cb","11739cf242aa4d8286c221bd9f6317bc","3e7b1712f48246cea4ba570826eb952e","12073763b38541b98e81e5e752c6057e","e470fcd827254fd7ad4687e84edfdb30","241114eed7b94588b3d5ed71bf5d33da","335bcbb529f94892943d33fbe32ad72f","bcac537eb0664eb5baf8dca363781fde","fcd87fee041e414ab8eba6a5927ff507","b549679261a1427ca6df1241a73f68fb","da5f908abe9843a890d1a126b3577a90","82432a65bcf44b4faf0f54ea2a1e2a65","0e18b81472c34716828e0bfeda21b2a1","6fbd8f52b2f24d1ba04e50d7370cf72d","8c16dc4f184b42e6bddff79a5edc7227","660a03cb27604f9da94604799bfa4969","045742542d364cfe86708051e74484a2","27a633f41b9745538a3a7c025ea346d3","d23fb992599947059f93b492025d95bd","960201caa0884821a202c082f7429344","8fd84cdc88ae4f389d3ba4f71c295be8","a4e1ec38e165493794fe14e9da0957dc","5e4dca7e4b274122bb69c613eadbd94f","3b469f4793324221842f9c0610cc4331","9c472ef1252b46fe8f072132a307f55f","3836a05092594746bc4d8e554b027890","6c02bcf435b64517b3a47b98a2f6b9ac","c3732ee576694a2ca87d17a1fe59c5ac","77feaf490e244f6582a2651fdd777db6","8397d600b38f40f9a890b1e6b3a495ec","a5e1a9188f7a4eb28f793cd01be4336a","19aa04985c634f0d93cef7f903ea875a","5d711bc4bc7b43dfadb127b368fd7d5d","ad7a85dce5bc4a32933a2bb7cd0e43ab","e94eaaa4c9d2432ea956b4bd3c15b17e","eeb0a532ae96437387b3fd228c255e5b","3113f1f0f9fd4ddba02196a35f3f2542","aaa73e65686a4dd6960c8d54f4414052","b0a7fb35eba94c7f915530e27c935e52","5960101e505a445a9d4a431fc0a4c6e4","375ce83fec08420683d242a08065fb48","78f37d0848cc40aa8ecbc3acf1a7a882","8050903fdf2940c796bc4ab33d9420c5","7736ee96ab5a40f69fa7bd772024ad08","758874ddac9f4e2981842c33652313ff","0bca2a5c846f4cf997822cd25ae29b41","9c6493282fec42aeb35bcaa6752c33d2","232315569f1d4b3393e5cf19d60b6be8","ebb812a254e640408d7bc5f7bbbf7f7e","fe037e4dffb945f3b199e5a7fba5b286","849ebd95d86641a581a6ecc7c7559e9a","be20a36392134bcdb667ed3a869365d1","d83efbdd10e94465a52227d997556626","eb89342cda2d41eb8d243316e1213094","57051ffbd7d1418b85843354b0926853","707dc821093a4327b4142a14d35d9a1e","d3b96a81eee147708f7a7ef13b19d942","9f0e41bc78074145a1b9f4586bcd94d3","5de39ed5ccb448b1a745afb48eaa09be","9456f5a5af8b4f009400045add58ca31","7d2ef413e7b7427595139819f022d5f3","9ac6e276ac4b4d7b8546f9eb0e1e1655","1e0f1a8d71cb46e6bda09d97afac12c4","24e65511a9374da79b702f60ae34ccec","a744379728144578abf3c1890d196c53","b4fc5437de194431af2b0bef7bf25a03","fff10b26dbed49ebaa662da75d0792ba","f7589936478c46bfb8b363cbd1feb7e4","b951e210748a425da7879e90ff516dcb","756b64d3340f42fdb1ccbcd5c53e6748","43844c79c1494587aadfab8d4a93ecbe","a4747276fe7a40e3afc2998dcdf11e92","ba1192e3646f4099ba0e23e3aa317935","358df2a9db7143f7b7106165e29c02f3","b736c526190a48209953aed23ef1ede9","c055fc0c770348a1a93bd6f096107ff2","9f42f788c7a747179e7e3160e80a0f87","ea827c2a6f16423ca97f9515f429407e","52a21bae308b4a69be2c38e63d9bc959","163e84457c1644eaa5bf7540c59a91b1","064e37023588468096e0fea04a1d3837","82b477c967854e34b550c8c9bd3957bf","b1927f12ad664acfb9d55ca2e018e09a","14db35b56b274baaa5f474fbf15fe10f","a624caee0ef74d2a955802174e49cfc9","c4a9a81025df421eac9488dddf56e1b2","1b5ae17b6d964c1887e1b4d61cd67523","e065922bfe19484abdedc1131fb1dd03","1aa123d286934118be21b826a6e9eeff","4da645bc117a42d88972d737fd0b4edd","187b29d7f2454d64b37cdd3f00822a88","1236ad7feb8b44cc81a3e34b4c131cee","5357f30ad5084bb1ab2563c4b74340e9","fd395608b126411081868f5f884085e0","13c364f40d734fd2bd719e07d162824d","71274ed02b1c456cad92ef8d82f241a9","2886adc6c2bf47e3a7fb8b1d6bbfe80a","413bd84a69744105814ebb9ccd8853f8","a64d192961634ff6b9b85d6bc2630d77","2e846ce1e51f472282cf462051f08dcc","482df09d3eb048d78794013c9ee8c5aa","6cfba2a5fa794800b5ab73c07310f6c4","f456595d2b7d4ae3a745fac15dfefb0a","e0a68643d7534404aa7b965f2cc1f396","401279948c7a452999b1360918048f2a","41cb48d494a44c5db128b69990b707ca","023044f6dcdf42418581eaa9d9e59939","d37aebe020b540d6aa782a6898dd7c32","d2adf26b5ca0495f8ebd5354ebc2230a","ecd8d8e71125446ba10987025f04ce78","3e303a80e99d4023b4c8ac37928134cd","eac1bd0bfe3044288ba762d8b0ebc689","565c62fc7948485a8d18a2334403c7cc","8796db2580e34db8b7738c4d8ead3d7c","1d5ab37e156a4f6f8b749bd5ee6bf65d","23276bc38fa144a2bbccc071d5704392","3a303622ca7b44aab3b34b9eff7334ce","d73b2d473559430ca810e85d40ac213f","b5998fc79cdc4dd0a3508f6f1748aedd","45a7ee2a41f94370b2af22e31fb75bc9","ffaaa53741934ad0bcff355e32a0bf80","e0a3ae8f2d1947209d2d488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Kimoto","userId":"15793563350243916832"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["<div class=\"contents\"><div><p><a href=\"http://dummy.com\">http://dummy.com<p>In its first year, The Shillito's Elves Display won an international \n","design award for Shillito's department store.  The elves display is arts\n"," and crafts at its finest.  The mixed media exhibit displays the talents\n"," of local fine arts graduates, and the display, while \"folksy\", is as \n","technologically advanced as Disney World's famous \"It's a Small World\" \n","ride. </p><p>The Shillito's Elves attracted close to 100,000 people each\n"," year.  It was one of the most beloved Christmas traditions in \n","Cincinnati.  For many in the Cincinnati area, it is a fond childhood \n","holiday memory and one that they would love to share with their own \n","families.  In the next 40 days, we are asking for your help to make the \n","entire display viewable again for the first time in 25 years.  In order \n","to make this happen, we must meet our financial goal.  </p><p>Your money will be used in the following ways:</p><ul>\n","<li>Repair broken animated elves (75 motors need repair)</li>\n","<li>Replace faded clothing</li>\n","<li>Fix torn faces and \"hairless elves\"</li>\n","<li>Construct 3000 feet of staging needed to make the elves viewable</li>\n","<li>Prepare backdrops </li>\n","<li>Fix lighting in the space</li>\n","</ul><p>The money that is earned in which we surpass our goal will be \n","used to keep the Workshop open for longer hours.  In addition, you can \n","use extra family passes that you buy to give to others.   Let's make \n","this a great Christmas. </p><div class=\"template asset\" contenteditable=\"false\" data-alt-text=\"\" data-caption=\"\" data-id=\"_xxx_\">\n","<figure>\n","<img alt=\"\" class=\"fit lazyload\" data-src=\"http://dummy.com\"/>\n","</figure>\n","</div>\n","<div class=\"template asset\" contenteditable=\"false\" data-alt-text=\"\" data-caption=\"\" data-id=\"_xxx_\">\n","<figure>\n","<img alt=\"\" class=\"fit lazyload\" data-src=\"http://dummy.com\"/>\n","</figure>\n","</div>\n","</div></div>\n","http://dummy.comIn its first year, The Shillito's Elves Display won an international \n","design award for Shillito's department store.  The elves display is arts\n"," and crafts at its finest.  The mixed media exhibit displays the talents\n"," of local fine arts graduates, and the display, while \"folksy\", is as \n","technologically advanced as Disney World's famous \"It's a Small World\" \n","ride. The Shillito's Elves attracted close to 100,000 people each\n"," year.  It was one of the most beloved Christmas traditions in \n","Cincinnati.  For many in the Cincinnati area, it is a fond childhood \n","holiday memory and one that they would love to share with their own \n","families.  In the next 40 days, we are asking for your help to make the \n","entire display viewable again for the first time in 25 years.  In order \n","to make this happen, we must meet our financial goal.  Your money will be used in the following ways:\n","Repair broken animated elves (75 motors need repair)\n","Replace faded clothing\n","Fix torn faces and \"hairless elves\"\n","Construct 3000 feet of staging needed to make the elves viewable\n","Prepare backdrops \n","Fix lighting in the space\n","The money that is earned in which we surpass our goal will be \n","used to keep the Workshop open for longer hours.  In addition, you can \n","use extra family passes that you buy to give to others.   Let's make \n","this a great Christmas. \n","\n","\n","\n","\n","\n","\n","\n","\n","\n","\n","-------------------- 0 --------------------\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:0 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2354df7ce67147f7ba31c6f1efadc9d5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8923d422e0e14f73a0e3dc69022b6751"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5859441032596663, 'score': 0.6919808366918702}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:1 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"241114eed7b94588b3d5ed71bf5d33da"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"045742542d364cfe86708051e74484a2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5547730922698975, 'score': 0.712341632645238}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:2 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c3732ee576694a2ca87d17a1fe59c5ac"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b0a7fb35eba94c7f915530e27c935e52"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5694959245476068, 'score': 0.7227363663840873}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:3 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fe037e4dffb945f3b199e5a7fba5b286"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7d2ef413e7b7427595139819f022d5f3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6185419255611944, 'score': 0.7144126420719533}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:4 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4747276fe7a40e3afc2998dcdf11e92"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1927f12ad664acfb9d55ca2e018e09a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6959869607406504, 'score': 0.705134464197352}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["-------------------- 1 --------------------\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:0 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fd395608b126411081868f5f884085e0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"401279948c7a452999b1360918048f2a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6517207529030594, 'score': 0.6223925032984587}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:1 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"23276bc38fa144a2bbccc071d5704392"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2470187dc6c64dc39ad56240e637e69c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5351819314208686, 'score': 0.7291969178603046}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:2 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"18d1b4eb60af4ae982bf97213f0b70d9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"96869e47fbaa4d3383b9a55d8f2e3692"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5518511644765443, 'score': 0.7294486345525352}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:3 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"49c8b22c15fe44c1b6223cc5c1682a9a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e861f54031fb4779b4ef29fb63e41054"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6500720980704999, 'score': 0.7279402012767812}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:4 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/203 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cac75aa1742345658354d81f44c1819b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"34606bb8adce4185b70bad236f646377"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.7027205062263152, 'score': 0.7278850796716563}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["-------------------- 2 --------------------\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:0 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/204 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3a9d34ea017a4fad845bab7ab97fc91a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"10052a9916bf4793886eb18317411dcb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6005854230149188, 'score': 0.6752700975633603}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:1 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/204 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"33fa7e116829405e9f4a29f3809a261a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d750054002c44989c0abbd86dbaf7f2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.548300403650648, 'score': 0.7206741613296889}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:2 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/204 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"78f9b2d588a44a118cb933d7a1a8923f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2f9e484eb7c642a78f36c1fb5efcc3a9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5527166069704734, 'score': 0.7187115319970437}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["# ============ start epoch:3 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/204 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c2a5517c86e94a10aef6a655ce0bfdad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4ace58c002814b218b369d47ed015533"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.5579808193264364, 'score': 0.7327936523642258}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["save model weight\n","# ============ start epoch:4 ============== #\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/204 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"403e7f9e639340628259107d2353b046"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/102 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d5cb01aee80412abfbaad2505149b38"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["{'val_loss': 0.6601551131825121, 'score': 0.716308018715876}"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["CV: 0.72856\n","/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Oututput/test-2-bert-deberta-base-epoch10/model/fold0.pth\n","/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Oututput/test-2-bert-deberta-base-epoch10/model/fold1.pth\n","/content/drive/MyDrive/signate/MUFG Data Science Champion Ship/Oututput/test-2-bert-deberta-base-epoch10/model/fold2.pth\n"]},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/307 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e38d43c474654b59bc776ad9b6e74bd1"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/307 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a84f0f388b4f46e8b4f098b68d2ddaee"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at microsoft/deberta-base were not used when initializing DebertaModel: ['lm_predictions.lm_head.bias', 'lm_predictions.lm_head.dense.bias', 'lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.LayerNorm.weight']\n","- This IS expected if you are initializing DebertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DebertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/307 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ab143c8b0690447481cf99caa5e36417"}},"metadata":{}}],"source":["# =====================\n","# Main\n","# =====================\n","# セットアップ\n","cfg = setup(Config)\n","\n","import transformers\n","from transformers import AutoConfig, AutoModel, AutoTokenizer\n","from transformers import AdamW, get_linear_schedule_with_warmup\n","\n","# データの読み込み\n","train = pd.read_csv(os.path.join(cfg.INPUT, 'train.csv'))\n","test = pd.read_csv(os.path.join(cfg.INPUT, 'test.csv'))\n","sub = pd.read_csv(os.path.join(cfg.INPUT, 'sample_submit.csv'), header=None)\n","\n","train = train[['html_content', 'state']]\n","test = test[['html_content']]\n","\n","print(train['html_content'][0])\n","\n","train['html_content'] = cleaning(train['html_content'])\n","test['html_content'] = cleaning(test['html_content'])\n","\n","print(train['html_content'][0])\n","\n","\n","# tokenizerの読み込み\n","cfg.tokenizer = AutoTokenizer.from_pretrained(cfg.MODEL_PATH)\n","\n","# validationデータの設定\n","cfg.folds = get_stratifiedkfold(train, 'state', cfg.num_fold, cfg.seed)\n","cfg.folds.to_csv(os.path.join(cfg.EXP_PREDS, 'folds.csv'))\n","\n","# BERTの学習\n","score = training(cfg, train)\n","\n","# BERTの推論\n","cfg.model_weights = [p for p in sorted(glob(os.path.join(cfg.EXP_MODEL, 'fold*.pth')))]\n","sub_pred = inferring(cfg, test)\n","# print(sub_pred)\n","# print(sub_pred.shape())\n","sub[1] = np.argmax(sub_pred, axis=1)\n","sub[1] = sub[1].astype(int) + 1\n","\n","# 提出用ファイル\n","sub.to_csv(os.path.join(cfg.EXP_PREDS, 'submission_bert.csv'), index=False, header=False)"]},{"cell_type":"code","source":["sub[1] = np.argmax(sub_pred, axis=1)\n","sub[1] = sub[1].astype(int)\n","\n","# 提出用ファイル\n","sub.to_csv(os.path.join(cfg.EXP_PREDS, 'submission_bert.csv'), index=False, header=False)"],"metadata":{"id":"V4w-Yp0o-6Dm","executionInfo":{"status":"ok","timestamp":1662044144873,"user_tz":-540,"elapsed":3,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["sub"],"metadata":{"id":"NdUg7BrRw1aI","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1662044148398,"user_tz":-540,"elapsed":414,"user":{"displayName":"Haruhisa Kimoto","userId":"15793563350243916832"}},"outputId":"e917a02f-036b-4203-f166-48fac85f5ab2"},"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["               0  1\n","0     test_00000  1\n","1     test_00001  1\n","2     test_00002  1\n","3     test_00003  0\n","4     test_00004  0\n","...          ... ..\n","9795  test_09795  1\n","9796  test_09796  1\n","9797  test_09797  0\n","9798  test_09798  1\n","9799  test_09799  1\n","\n","[9800 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-5e758662-aec6-4fa5-8a0c-1df963867161\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody 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