Model backlog/Train/142-Tweet-Train-3Fold-roBERTa base MAX 4 separeted heads.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"},"colab":{"name":"142-Tweet-Train-3Fold-roBERTa base MAX 4 separeted heads.ipynb","provenance":[],"collapsed_sections":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"kA4Pu2sPr_eG","colab_type":"code","colab":{}},"source":["# !nvidia-smi"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"rOXNne0OoLTd","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":102},"outputId":"22edec8a-4ec0-4fc5-fc28-437686820f2e","executionInfo":{"status":"ok","timestamp":1589486092575,"user_tz":180,"elapsed":15606,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["!pip --quiet install transformers\n","!pip --quiet install tokenizers"],"execution_count":3,"outputs":[{"output_type":"stream","text":["\u001b[K     |████████████████████████████████| 645kB 2.8MB/s \n","\u001b[K     |████████████████████████████████| 3.8MB 13.9MB/s \n","\u001b[K     |████████████████████████████████| 890kB 38.9MB/s \n","\u001b[K     |████████████████████████████████| 1.1MB 42.1MB/s \n","\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"htKChfMHoYRb","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":122},"outputId":"dc40964e-365d-4729-af4b-3ab88ddf1c99","executionInfo":{"status":"ok","timestamp":1589486109749,"user_tz":180,"elapsed":32606,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"xtDZkoyzod_S","colab_type":"code","colab":{}},"source":["!cp -r '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Scripts/.' ."],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"38KKgQn7owCr","colab_type":"code","colab":{}},"source":["COLAB_BASE_PATH = '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/'\n","MODEL_BASE_PATH = COLAB_BASE_PATH + 'Models/Files/142-roBERTa_base/'\n","\n","import os\n","os.mkdir(MODEL_BASE_PATH)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MOE8CNSAnvq-","colab_type":"text"},"source":["## Dependencies"]},{"cell_type":"code","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","id":"x48gxo3vnvrA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":71},"outputId":"b01a7ee4-64fc-4553-878f-26f4358b4691","executionInfo":{"status":"ok","timestamp":1589486122988,"user_tz":180,"elapsed":13225,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["import json, warnings, shutil\n","from tweet_utility_scripts import *\n","from tweet_utility_preprocess_roberta_scripts import *\n","from transformers import TFRobertaModel, RobertaConfig\n","from tokenizers import ByteLevelBPETokenizer\n","from tensorflow.keras.models import Model\n","from tensorflow.keras import optimizers, metrics, losses, layers\n","\n","SEED = 0\n","seed_everything(SEED)\n","warnings.filterwarnings(\"ignore\")"],"execution_count":7,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"6HW0GQzPnvrF","colab_type":"text"},"source":["# Load data"]},{"cell_type":"code","metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_kg_hide-input":true,"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","id":"hVhA98wenvrG","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"f1af48a8-acca-4ca2-894f-aa934c21ccaa","executionInfo":{"status":"ok","timestamp":1589486129579,"user_tz":180,"elapsed":19336,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["database_base_path = COLAB_BASE_PATH + 'Data/aux/'\n","k_fold = pd.read_csv(database_base_path + '5-fold.csv')\n","display(k_fold.head())\n","\n","# Unzip files\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/aux/fold_1.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/aux/fold_2.tar.gz'\n","!tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/aux/fold_3.tar.gz'\n","# !tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/aux/fold_4.tar.gz'\n","# !tar -xvf '/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Data/aux/fold_5.tar.gz'"],"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>textID</th>\n","      <th>text</th>\n","      <th>selected_text</th>\n","      <th>sentiment</th>\n","      <th>jaccard</th>\n","      <th>text_len</th>\n","      <th>selected_text_len</th>\n","      <th>text_wordCnt</th>\n","      <th>selected_text_wordCnt</th>\n","      <th>fold_1</th>\n","      <th>fold_2</th>\n","      <th>fold_3</th>\n","      <th>fold_4</th>\n","      <th>fold_5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>cb774db0d1</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>35</td>\n","      <td>35</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>549e992a42</td>\n","      <td>sooo sad i will miss you here in san diego!!!</td>\n","      <td>sooo sad</td>\n","      <td>negative</td>\n","      <td>0.200000</td>\n","      <td>45</td>\n","      <td>8</td>\n","      <td>10</td>\n","      <td>2</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>088c60f138</td>\n","      <td>my boss is bullying me...</td>\n","      <td>bullying me</td>\n","      <td>negative</td>\n","      <td>0.166667</td>\n","      <td>25</td>\n","      <td>11</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>9642c003ef</td>\n","      <td>what interview! leave me alone</td>\n","      <td>leave me alone</td>\n","      <td>negative</td>\n","      <td>0.600000</td>\n","      <td>30</td>\n","      <td>14</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>358bd9e861</td>\n","      <td>sons of ****, why couldn`t they put them on th...</td>\n","      <td>sons of ****,</td>\n","      <td>negative</td>\n","      <td>0.214286</td>\n","      <td>74</td>\n","      <td>13</td>\n","      <td>14</td>\n","      <td>3</td>\n","      <td>train</td>\n","      <td>train</td>\n","      <td>validation</td>\n","      <td>train</td>\n","      <td>train</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       textID  ... fold_5\n","0  cb774db0d1  ...  train\n","1  549e992a42  ...  train\n","2  088c60f138  ...  train\n","3  9642c003ef  ...  train\n","4  358bd9e861  ...  train\n","\n","[5 rows x 14 columns]"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["fold_1/\n","fold_1/y_valid.npy\n","fold_1/x_valid_aux.npy\n","fold_1/y_train_mask.npy\n","fold_1/x_train_aux.npy\n","fold_1/x_valid.npy\n","fold_1/x_valid_aux2.npy\n","fold_1/y_valid_aux.npy\n","fold_1/y_train.npy\n","fold_1/y_train_aux.npy\n","fold_1/y_valid_mask.npy\n","fold_1/x_train.npy\n","fold_1/x_train_aux2.npy\n","fold_2/\n","fold_2/y_valid.npy\n","fold_2/x_valid_aux.npy\n","fold_2/y_train_mask.npy\n","fold_2/x_train_aux.npy\n","fold_2/x_valid.npy\n","fold_2/x_valid_aux2.npy\n","fold_2/y_valid_aux.npy\n","fold_2/y_train.npy\n","fold_2/y_train_aux.npy\n","fold_2/y_valid_mask.npy\n","fold_2/x_train.npy\n","fold_2/x_train_aux2.npy\n","fold_3/\n","fold_3/y_valid.npy\n","fold_3/x_valid_aux.npy\n","fold_3/y_train_mask.npy\n","fold_3/x_train_aux.npy\n","fold_3/x_valid.npy\n","fold_3/x_valid_aux2.npy\n","fold_3/y_valid_aux.npy\n","fold_3/y_train.npy\n","fold_3/y_train_aux.npy\n","fold_3/y_valid_mask.npy\n","fold_3/x_train.npy\n","fold_3/x_train_aux2.npy\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"b0bUTMPynvrM","colab_type":"text"},"source":["# Model parameters"]},{"cell_type":"code","metadata":{"id":"Paexn4ywnvrM","colab_type":"code","colab":{}},"source":["vocab_path = COLAB_BASE_PATH + 'qa-transformers/roberta/roberta-base-vocab.json'\n","merges_path = COLAB_BASE_PATH + 'qa-transformers/roberta/roberta-base-merges.txt'\n","base_path = COLAB_BASE_PATH + 'qa-transformers/roberta/'\n","\n","config = {\n","  \"MAX_LEN\": 96,\n","  \"BATCH_SIZE\": 32,\n","  \"EPOCHS\": 5,\n","  \"LEARNING_RATE\": 3e-5,\n","  \"ES_PATIENCE\": 1,\n","  \"question_size\": 4,\n","  \"N_FOLDS\": 3,\n","  \"base_model_path\": base_path + 'roberta-base-tf_model.h5',\n","  \"config_path\": base_path + 'roberta-base-config.json'\n","}\n","\n","with open(MODEL_BASE_PATH + 'config.json', 'w') as json_file:\n","    json.dump(json.loads(json.dumps(config)), json_file)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"D_8F5nxDnvrW","colab_type":"text"},"source":["# Tokenizer"]},{"cell_type":"code","metadata":{"_kg_hide-output":true,"id":"6qeQlxrDnvrX","colab_type":"code","colab":{}},"source":["tokenizer = ByteLevelBPETokenizer(vocab_file=vocab_path, merges_file=merges_path, \n","                                  lowercase=True, add_prefix_space=True)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"S3FN1pb1nvrb","colab_type":"text"},"source":["## Learning rate schedule"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"XUYfnwPRnvrc","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":438},"outputId":"b4f3b24b-513a-412d-f0e0-e360d1c59a06","executionInfo":{"status":"ok","timestamp":1589486138666,"user_tz":180,"elapsed":25988,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["LR_MIN = 1e-6\n","LR_MAX = config['LEARNING_RATE']\n","LR_EXP_DECAY = .5\n","\n","@tf.function\n","def lrfn(epoch):\n","    lr = LR_MAX * LR_EXP_DECAY**epoch\n","    if lr < LR_MIN:\n","        lr = LR_MIN\n","    return lr\n","    \n","rng = [i for i in range(config['EPOCHS'])]\n","y = [lrfn(x) for x in rng]\n","\n","fig, ax = plt.subplots(figsize=(20, 6))\n","plt.plot(rng, y)\n","print(\"Learning rate schedule: {:.3g} to {:.3g} to {:.3g}\".format(y[0], max(y), y[-1]))"],"execution_count":11,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:5 out of the last 5 calls to <function lrfn at 0x7f6fae2d70d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n","Learning rate schedule: 3e-05 to 3e-05 to 1.87e-06\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1440x432 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"3aHwZDF9nvrR","colab_type":"text"},"source":["# Model"]},{"cell_type":"code","metadata":{"id":"7gIO5wkQnvrS","colab_type":"code","colab":{}},"source":["module_config = RobertaConfig.from_pretrained(config['config_path'], output_hidden_states=True)\n","\n","def model_fn(MAX_LEN):\n","    input_ids = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='input_ids')\n","    attention_mask = layers.Input(shape=(MAX_LEN,), dtype=tf.int32, name='attention_mask')\n","    \n","    base_model = TFRobertaModel.from_pretrained(config['base_model_path'], config=module_config, name=\"base_model\")\n","    _, _, hidden_states = base_model({'input_ids': input_ids, 'attention_mask': attention_mask})\n","    \n","    h12 = hidden_states[-1]\n","    h11 = hidden_states[-2]\n","    h10 = hidden_states[-3]\n","    h09 = hidden_states[-4]\n","\n","    \n","    x_start_09 = layers.Dropout(.1)(h09) \n","    x_start_09 = layers.Dense(1)(x_start_09)\n","    x_start_09 = layers.Flatten()(x_start_09)\n","    y_start_09 = layers.Activation('softmax', name='y_start_09')(x_start_09)\n","    \n","    x_start_10 = layers.Dropout(.1)(h10) \n","    x_start_10 = layers.Dense(1)(x_start_10)\n","    x_start_10 = layers.Flatten()(x_start_10)\n","    y_start_10 = layers.Activation('softmax', name='y_start_10')(x_start_10)\n","    \n","    x_start_11 = layers.Dropout(.1)(h11) \n","    x_start_11 = layers.Dense(1)(x_start_11)\n","    x_start_11 = layers.Flatten()(x_start_11)\n","    y_start_11 = layers.Activation('softmax', name='y_start_11')(x_start_11)\n","    \n","    x_start_12 = layers.Dropout(.1)(h12) \n","    x_start_12 = layers.Dense(1)(x_start_12)\n","    x_start_12 = layers.Flatten()(x_start_12)\n","    y_start_12 = layers.Activation('softmax', name='y_start_12')(x_start_12)\n","\n","    y_start = layers.Maximum(name='y_start')([y_start_12, y_start_11, y_start_10, y_start_09])\n","\n","\n","    x_end_09 = layers.Dropout(.1)(h09) \n","    x_end_09 = layers.Dense(1)(x_end_09)\n","    x_end_09 = layers.Flatten()(x_end_09)\n","    y_end_09 = layers.Activation('softmax', name='y_end_09')(x_end_09)\n","\n","    x_end_10 = layers.Dropout(.1)(h10) \n","    x_end_10 = layers.Dense(1)(x_end_10)\n","    x_end_10 = layers.Flatten()(x_end_10)\n","    y_end_10 = layers.Activation('softmax', name='y_end_10')(x_end_10)\n","\n","    x_end_11 = layers.Dropout(.1)(h11) \n","    x_end_11 = layers.Dense(1)(x_end_11)\n","    x_end_11 = layers.Flatten()(x_end_11)\n","    y_end_11 = layers.Activation('softmax', name='y_end_11')(x_end_11)\n","\n","    x_end_12 = layers.Dropout(.1)(h12) \n","    x_end_12 = layers.Dense(1)(x_end_12)\n","    x_end_12 = layers.Flatten()(x_end_12)\n","    y_end_12 = layers.Activation('softmax', name='y_end_12')(x_end_12)\n","\n","    y_end = layers.Maximum(name='y_end')([y_end_12, y_end_11, y_end_10, y_end_09])\n","    \n","    model = Model(inputs=[input_ids, attention_mask], outputs=[y_start, y_end])\n","    \n","    return model"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jy24FlCgnvrj","colab_type":"text"},"source":["# Train"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"id":"RU2sa648nvrj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"c9bb8032-7e70-45e5-b1a7-db50e9fc4827","executionInfo":{"status":"ok","timestamp":1589489872322,"user_tz":180,"elapsed":3757685,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["AUTO = tf.data.experimental.AUTOTUNE\n","strategy = tf.distribute.get_strategy()\n","history_list = []\n","\n","for n_fold in range(config['N_FOLDS']):\n","    n_fold +=1\n","    print('\\nFOLD: %d' % (n_fold))\n","    # Load data\n","    base_data_path = 'fold_%d/' % (n_fold)\n","    x_train = np.load(base_data_path + 'x_train.npy')\n","    y_train = np.load(base_data_path + 'y_train.npy')\n","    x_valid = np.load(base_data_path + 'x_valid.npy')\n","    y_valid = np.load(base_data_path + 'y_valid.npy')\n","    \n","    step_size = x_train.shape[1] // config['BATCH_SIZE']\n","    valid_step_size = x_valid.shape[1] // config['BATCH_SIZE']\n","\n","    # Build TF datasets\n","    train_dist_ds = strategy.experimental_distribute_dataset(get_training_dataset(x_train, y_train, config['BATCH_SIZE'], AUTO, seed=SEED))\n","    valid_dist_ds = strategy.experimental_distribute_dataset(get_validation_dataset(x_valid, y_valid, config['BATCH_SIZE'], AUTO, repeated=True, seed=SEED))\n","    train_data_iter = iter(train_dist_ds)\n","    valid_data_iter = iter(valid_dist_ds)\n","    \n","    # Step functions\n","    @tf.function\n","    def train_step(data_iter):\n","        def train_step_fn(x, y):\n","            with tf.GradientTape() as tape:\n","                probabilities = model(x, training=True)\n","                loss_start = loss_fn_start(y['y_start'], probabilities[0], label_smoothing=0.2)\n","                loss_end = loss_fn_end(y['y_end'], probabilities[1], label_smoothing=0.2)\n","                loss = tf.math.add(loss_start, loss_end)\n","            grads = tape.gradient(loss, model.trainable_variables)\n","            optimizer.apply_gradients(zip(grads, model.trainable_variables))\n","            # update metrics\n","            train_loss.update_state(loss)\n","            train_loss_start.update_state(loss_start)\n","            train_loss_end.update_state(loss_end)\n","        for _ in tf.range(step_size):\n","            strategy.experimental_run_v2(train_step_fn, next(data_iter))\n","\n","    @tf.function\n","    def valid_step(data_iter):\n","        def valid_step_fn(x, y):\n","            probabilities = model(x, training=False)\n","            loss_start = loss_fn_start(y['y_start'], probabilities[0])\n","            loss_end = loss_fn_end(y['y_end'], probabilities[1])\n","            loss = tf.math.add(loss_start, loss_end)\n","            # update metrics\n","            valid_loss.update_state(loss)\n","            valid_loss_start.update_state(loss_start)\n","            valid_loss_end.update_state(loss_end)\n","        for _ in tf.range(valid_step_size):\n","            strategy.experimental_run_v2(valid_step_fn, next(data_iter))\n","\n","    # Train model\n","    model_path = 'model_fold_%d.h5' % (n_fold)\n","    \n","    model = model_fn(config['MAX_LEN'])\n","    optimizer = optimizers.Adam(learning_rate=lambda: lrfn(tf.cast(optimizer.iterations, tf.float32)//step_size))\n","    loss_fn_start = losses.categorical_crossentropy\n","    loss_fn_end = losses.categorical_crossentropy\n","\n","    train_loss = metrics.Sum()\n","    valid_loss = metrics.Sum()\n","    train_loss_start = metrics.Sum()\n","    valid_loss_start = metrics.Sum()\n","    train_loss_end = metrics.Sum()\n","    valid_loss_end = metrics.Sum()\n","        \n","    metrics_dict = {'loss': train_loss, 'loss_start': train_loss_start, 'loss_end': train_loss_end, \n","                    'val_loss': valid_loss, 'val_loss_start': valid_loss_start, 'val_loss_end': valid_loss_end}\n","        \n","    history = custom_fit(model, metrics_dict, train_step, valid_step, train_data_iter, valid_data_iter, \n","                         step_size, valid_step_size, config['BATCH_SIZE'], config['EPOCHS'], config['ES_PATIENCE'], \n","                         (MODEL_BASE_PATH + model_path), save_last=False)\n","    \n","    history_list.append(history)\n","    model.save_weights(MODEL_BASE_PATH +'last_' + model_path)\n","    model.load_weights(MODEL_BASE_PATH + model_path)\n","\n","    \n","    # Make predictions\n","    train_preds = model.predict(get_test_dataset(x_train, config['BATCH_SIZE']))\n","    valid_preds = model.predict(get_test_dataset(x_valid, config['BATCH_SIZE']))\n","    \n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'start_fold_%d' % (n_fold)] = train_preds[0].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'train', 'end_fold_%d' % (n_fold)] = train_preds[1].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'start_fold_%d' % (n_fold)] = valid_preds[0].argmax(axis=-1)\n","    k_fold.loc[k_fold['fold_%d' % (n_fold)] == 'validation', 'end_fold_%d' % (n_fold)] = valid_preds[1].argmax(axis=-1)\n","    \n","    k_fold['end_fold_%d' % (n_fold)] = k_fold['end_fold_%d' % (n_fold)].astype(int)\n","    k_fold['start_fold_%d' % (n_fold)] = k_fold['start_fold_%d' % (n_fold)].astype(int)\n","    k_fold['end_fold_%d' % (n_fold)].clip(0, k_fold['text_len'], inplace=True)\n","    k_fold['start_fold_%d' % (n_fold)].clip(0, k_fold['end_fold_%d' % (n_fold)], inplace=True)\n","    k_fold['prediction_fold_%d' % (n_fold)] = k_fold.apply(lambda x: decode(x['start_fold_%d' % (n_fold)], x['end_fold_%d' % (n_fold)], x['text'], config['question_size'], tokenizer), axis=1)\n","    k_fold['prediction_fold_%d' % (n_fold)].fillna(k_fold[\"text\"], inplace=True)\n","    k_fold['jaccard_fold_%d' % (n_fold)] = k_fold.apply(lambda x: jaccard(x['selected_text'], x['prediction_fold_%d' % (n_fold)]), axis=1)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["\n","FOLD: 1\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:From <ipython-input-13-8934f9dbd22b>:40: StrategyBase.experimental_run_v2 (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","renamed to `run`\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:6 out of the last 6 calls to <function lrfn at 0x7f6fae2d70d0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings is likely due to passing python objects instead of tensors. Also, tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. Please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 282.4s loss: 4.4941 loss_start: 2.2174 loss_end: 2.2767 val_loss: 2.0571 val_loss_start: 1.0201 val_loss_end: 1.0370\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 2/5\n","time: 267.3s loss: 4.0362 loss_start: 2.0225 loss_end: 2.0137 val_loss: 1.9254 val_loss_start: 0.9985 val_loss_end: 0.9269\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 3/5\n","time: 269.5s loss: 3.9143 loss_start: 1.9711 loss_end: 1.9432 val_loss: 1.8868 val_loss_start: 0.9835 val_loss_end: 0.9033\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 4/5\n","time: 269.5s loss: 3.8574 loss_start: 1.9478 loss_end: 1.9096 val_loss: 1.8850 val_loss_start: 0.9677 val_loss_end: 0.9173\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_1.h5\"\n","\n","EPOCH 5/5\n","time: 269.2s loss: 3.8246 loss_start: 1.9301 loss_end: 1.8946 val_loss: 1.8862 val_loss_start: 0.9737 val_loss_end: 0.9125\n","Training finished\n","\n","FOLD: 2\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 282.4s loss: 4.4822 loss_start: 2.2394 loss_end: 2.2428 val_loss: 2.0123 val_loss_start: 1.0115 val_loss_end: 1.0008\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_2.h5\"\n","\n","EPOCH 2/5\n","time: 267.8s loss: 3.9986 loss_start: 2.0249 loss_end: 1.9736 val_loss: 1.8860 val_loss_start: 0.9682 val_loss_end: 0.9178\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_2.h5\"\n","\n","EPOCH 3/5\n","time: 270.1s loss: 3.8932 loss_start: 1.9739 loss_end: 1.9192 val_loss: 1.9630 val_loss_start: 1.0034 val_loss_end: 0.9596\n","Epoch 00003: early stopping\n","\n","FOLD: 3\n","Train for 687 steps, validate for 171 steps\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","WARNING:tensorflow:Gradients do not exist for variables ['base_model/roberta/pooler/dense/kernel:0', 'base_model/roberta/pooler/dense/bias:0'] when minimizing the loss.\n","\n","EPOCH 1/5\n","time: 282.6s loss: 4.5227 loss_start: 2.2304 loss_end: 2.2923 val_loss: 1.9693 val_loss_start: 1.0095 val_loss_end: 0.9598\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 2/5\n","time: 267.7s loss: 4.0690 loss_start: 2.0318 loss_end: 2.0372 val_loss: 1.9676 val_loss_start: 0.9886 val_loss_end: 0.9790\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 3/5\n","time: 270.1s loss: 3.9464 loss_start: 1.9799 loss_end: 1.9665 val_loss: 1.9205 val_loss_start: 0.9807 val_loss_end: 0.9398\n","Saved model weights at \"/content/drive/My Drive/Colab Notebooks/Tweet Sentiment Extraction/Models/Files/142-roBERTa_base/model_fold_3.h5\"\n","\n","EPOCH 4/5\n","time: 269.7s loss: 3.8998 loss_start: 1.9592 loss_end: 1.9406 val_loss: 1.9353 val_loss_start: 0.9864 val_loss_end: 0.9489\n","Epoch 00004: early stopping\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"_59x5WnGnvro","colab_type":"text"},"source":["# Model loss graph"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"Tlq60Zvenvro","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"b881b497-2fba-4d1b-fb28-6e51e44f7b5e","executionInfo":{"status":"ok","timestamp":1589489875462,"user_tz":180,"elapsed":3760503,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["sns.set(style=\"whitegrid\")\n","for n_fold in range(config['N_FOLDS']):\n","    print('Fold: %d' % (n_fold+1))\n","    plot_metrics(history_list[n_fold])"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Fold: 1\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 2\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Fold: 3\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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size 1440x1080 with 3 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"MvzO_AjPnvrt","colab_type":"text"},"source":["# Model evaluation"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"ELe26ujlnvrt","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":313},"outputId":"4563afc6-1ac0-4f14-93d7-5baefa61f69c","executionInfo":{"status":"ok","timestamp":1589489878960,"user_tz":180,"elapsed":3763181,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["display(evaluate_model_kfold(k_fold, config['N_FOLDS']).style.applymap(color_map))"],"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/html":["<style  type=\"text/css\" >\n","    #T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col1 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#T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col6 {\n","            color:  orange;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col0 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col3 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col5 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col8 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col0 {\n","            color:  green;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col1 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col2 {\n","            color:  red;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col3 {\n","            color:  green;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col4 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col5 {\n","            color:  green;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col6 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col7 {\n","            color:  black;\n","        }    #T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col8 {\n","            color:  green;\n","        }</style><table id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Train</th>        <th class=\"col_heading level0 col1\" >Valid</th>        <th class=\"col_heading level0 col2\" >Var</th>        <th class=\"col_heading level0 col3\" >Train_fold_1</th>        <th class=\"col_heading level0 col4\" >Valid_fold_1</th>        <th class=\"col_heading level0 col5\" >Train_fold_2</th>        <th class=\"col_heading level0 col6\" >Valid_fold_2</th>        <th class=\"col_heading level0 col7\" >Train_fold_3</th>        <th class=\"col_heading level0 col8\" >Valid_fold_3</th>    </tr>    <tr>        <th class=\"index_name level0\" >Metric/Jaccard</th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n","                <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >Overall</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col0\" class=\"data row0 col0\" >0.737619</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.703859</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col2\" class=\"data row0 col2\" >0.033760</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.745457</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.706446</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.733897</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.697144</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.733504</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row0_col8\" class=\"data row0 col8\" >0.707987</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >Sentiment negative</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.584804</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col1\" class=\"data row1 col1\" >0.522170</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.062633</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.603409</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.516644</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col5\" class=\"data row1 col5\" >0.575544</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.516740</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.575458</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row1_col8\" class=\"data row1 col8\" >0.533126</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >Sentiment neutral</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col0\" class=\"data row2 col0\" >0.974531</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.973743</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col2\" class=\"data row2 col2\" >0.000787</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.975120</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.973166</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.974265</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.973527</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.974207</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row2_col8\" class=\"data row2 col8\" >0.974537</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >Sentiment positive</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.569296</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.518913</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.050383</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col3\" class=\"data row3 col3\" >0.576778</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.532871</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.566136</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.502524</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.564974</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row3_col8\" class=\"data row3 col8\" >0.521342</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >0 <= text wordCnt < 3</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.862507</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.854086</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.008421</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.868007</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.828358</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.858258</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.866446</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.861255</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row4_col8\" class=\"data row4 col8\" >0.867454</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >3 <= text wordCnt < 10</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.788024</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.766036</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.021989</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.797405</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.758660</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.784160</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.766878</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.782509</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row5_col8\" class=\"data row5 col8\" >0.772569</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >10 <= text wordCnt < 30</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col0\" class=\"data row6 col0\" >0.706208</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.666877</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col2\" class=\"data row6 col2\" >0.039330</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.713677</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.673513</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.702401</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.657300</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.702545</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row6_col8\" class=\"data row6 col8\" >0.669820</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >30 <= text wordCnt < 100</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.677433</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.555785</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.121648</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.676675</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.651201</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.685178</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.431691</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.670445</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row7_col8\" class=\"data row7 col8\" >0.584463</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row8\" class=\"row_heading level0 row8\" >0 <= selected text wordCnt < 3</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col0\" class=\"data row8 col0\" >0.677104</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col1\" class=\"data row8 col1\" >0.611462</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col2\" class=\"data row8 col2\" >0.065642</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col3\" class=\"data row8 col3\" >0.674222</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col4\" class=\"data row8 col4\" >0.598094</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col5\" class=\"data row8 col5\" >0.709734</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col6\" class=\"data row8 col6\" >0.644504</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col7\" class=\"data row8 col7\" >0.647356</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row8_col8\" class=\"data row8 col8\" >0.591789</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row9\" class=\"row_heading level0 row9\" >3 <= selected text wordCnt < 10</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col0\" class=\"data row9 col0\" >0.696375</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col1\" class=\"data row9 col1\" >0.671444</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col2\" class=\"data row9 col2\" >0.024931</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col3\" class=\"data row9 col3\" >0.717637</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col4\" class=\"data row9 col4\" >0.688682</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col5\" class=\"data row9 col5\" >0.660963</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col6\" class=\"data row9 col6\" >0.631611</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col7\" class=\"data row9 col7\" >0.710525</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row9_col8\" class=\"data row9 col8\" >0.694037</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row10\" class=\"row_heading level0 row10\" >10 <= selected text wordCnt < 30</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col0\" class=\"data row10 col0\" >0.863070</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col1\" class=\"data row10 col1\" >0.856513</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col2\" class=\"data row10 col2\" >0.006556</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col3\" class=\"data row10 col3\" >0.866999</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col4\" class=\"data row10 col4\" >0.858274</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col5\" class=\"data row10 col5\" >0.855585</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col6\" class=\"data row10 col6\" >0.845216</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col7\" class=\"data row10 col7\" >0.866625</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row10_col8\" class=\"data row10 col8\" >0.866049</td>\n","            </tr>\n","            <tr>\n","                        <th id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002level0_row11\" class=\"row_heading level0 row11\" >30 <= selected text wordCnt < 100</th>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col0\" class=\"data row11 col0\" >0.907930</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col1\" class=\"data row11 col1\" >0.840627</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col2\" class=\"data row11 col2\" >0.067303</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col3\" class=\"data row11 col3\" >0.924523</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col4\" class=\"data row11 col4\" >0.829779</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col5\" class=\"data row11 col5\" >0.912773</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col6\" class=\"data row11 col6\" >0.783012</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col7\" class=\"data row11 col7\" >0.886493</td>\n","                        <td id=\"T_973ecf12_9625_11ea_847a_0242ac1c0002row11_col8\" class=\"data row11 col8\" >0.909091</td>\n","            </tr>\n","    </tbody></table>"],"text/plain":["<pandas.io.formats.style.Styler at 0x7f6d2dca74e0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"yP3xG-Cenvry","colab_type":"text"},"source":["# Visualize predictions"]},{"cell_type":"code","metadata":{"_kg_hide-input":true,"id":"cGxcMVRMnvrz","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":704},"outputId":"adc9fb04-6305-46d2-b0dc-e4719596614d","executionInfo":{"status":"ok","timestamp":1589489878961,"user_tz":180,"elapsed":3762237,"user":{"displayName":"Dimitre Oliveira","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjWI4QgYDUyC8ZcW5KHpuBwcY-MZWVqbPWcZSzxIg=s64","userId":"06256612867315483887"}}},"source":["display(k_fold[[c for c in k_fold.columns if not (c.startswith('textID') or \n","                                                  c.startswith('text_len') or \n","                                                  c.startswith('selected_text_len') or \n","                                                  c.startswith('text_wordCnt') or \n","                                                  c.startswith('selected_text_wordCnt') or \n","                                                  c.startswith('fold_') or \n","                                                  c.startswith('start_fold_') or \n","                                                  c.startswith('end_fold_'))]].head(15))"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>selected_text</th>\n","      <th>sentiment</th>\n","      <th>jaccard</th>\n","      <th>prediction_fold_1</th>\n","      <th>jaccard_fold_1</th>\n","      <th>prediction_fold_2</th>\n","      <th>jaccard_fold_2</th>\n","      <th>prediction_fold_3</th>\n","      <th>jaccard_fold_3</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>i`d have responded, if i were going</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","      <td>i`d have responded, if i were going</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sooo sad i will miss you here in san diego!!!</td>\n","      <td>sooo sad</td>\n","      <td>negative</td>\n","      <td>0.200000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","      <td>sooo sad</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>my boss is bullying me...</td>\n","      <td>bullying me</td>\n","      <td>negative</td>\n","      <td>0.166667</td>\n","      <td>bullying me...</td>\n","      <td>0.333333</td>\n","      <td>bullying me...</td>\n","      <td>0.333333</td>\n","      <td>my boss is bullying me...</td>\n","      <td>0.166667</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>what interview! leave me alone</td>\n","      <td>leave me alone</td>\n","      <td>negative</td>\n","      <td>0.600000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","      <td>leave me alone</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>sons of ****, why couldn`t they put them on th...</td>\n","      <td>sons of ****,</td>\n","      <td>negative</td>\n","      <td>0.214286</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","      <td>sons of ****,</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","      <td>http://www.dothebouncy.com/smf - some shameles...</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2am feedings for the baby are fun when he is a...</td>\n","      <td>fun</td>\n","      <td>positive</td>\n","      <td>0.071429</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","      <td>fun</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>soooo high</td>\n","      <td>soooo high</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","      <td>soooo high</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>both of you</td>\n","      <td>both of you</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","      <td>both of you</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>journey!? wow... u just became cooler.  hehe.....</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>positive</td>\n","      <td>0.500000</td>\n","      <td>wow...</td>\n","      <td>0.200000</td>\n","      <td>wow... u just became cooler.</td>\n","      <td>1.000000</td>\n","      <td>wow...</td>\n","      <td>0.200000</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","      <td>as much as i love to be hopeful, i reckon the ...</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>i really really like the song love story by ta...</td>\n","      <td>like</td>\n","      <td>positive</td>\n","      <td>0.100000</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","      <td>i really really like</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>my sharpie is running dangerously low on ink</td>\n","      <td>dangerously</td>\n","      <td>negative</td>\n","      <td>0.125000</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","      <td>dangerously</td>\n","      <td>1.000000</td>\n","      <td>dangerously low on ink</td>\n","      <td>0.250000</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>i want to go to music tonight but i lost my vo...</td>\n","      <td>lost</td>\n","      <td>negative</td>\n","      <td>0.100000</td>\n","      <td>lost my voice.</td>\n","      <td>0.333333</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","      <td>lost</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>test test from the lg env2</td>\n","      <td>test test from the lg env2</td>\n","      <td>neutral</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","      <td>test test from the lg env2</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                                 text  ... jaccard_fold_3\n","0                 i`d have responded, if i were going  ...       1.000000\n","1       sooo sad i will miss you here in san diego!!!  ...       1.000000\n","2                           my boss is bullying me...  ...       0.166667\n","3                      what interview! leave me alone  ...       1.000000\n","4   sons of ****, why couldn`t they put them on th...  ...       1.000000\n","5   http://www.dothebouncy.com/smf - some shameles...  ...       1.000000\n","6   2am feedings for the baby are fun when he is a...  ...       1.000000\n","7                                          soooo high  ...       1.000000\n","8                                         both of you  ...       1.000000\n","9   journey!? wow... u just became cooler.  hehe.....  ...       0.200000\n","10  as much as i love to be hopeful, i reckon the ...  ...       1.000000\n","11  i really really like the song love story by ta...  ...       0.333333\n","12       my sharpie is running dangerously low on ink  ...       0.250000\n","13  i want to go to music tonight but i lost my vo...  ...       1.000000\n","14                         test test from the lg env2  ...       1.000000\n","\n","[15 rows x 10 columns]"]},"metadata":{"tags":[]}}]}]}