FinetuneBert_cross_encoder_sentence_transformers_continuous_outputs.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Trained Model at:\n",
        "[https://huggingface.co/Hammad7/plag-col-rev-en-v2](https://huggingface.co/Hammad7/plag-col-rev-en-v2)"
      ],
      "metadata": {
        "id": "X3zX6cgMVPRB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qQivOuYJKFOP",
        "outputId": "8eaf0487-a331-4435-cbb0-eb586d8e2e41"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  dtype=np.int):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, positive=False):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  precompute=False, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, random_state=None,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n"
          ]
        }
      ],
      "source": [
        "import sentence_transformers\n",
        "# from terminal \n",
        "# pip install sentence-transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iqWSTp98KFOT",
        "outputId": "ffd05b2d-f1e6-4991-f4ce-45b139828b1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=12YB0YDkmLtGjqBcx400HuCSyYnvVXgj8\n",
            "To: /mnt/disks/user/project/data.pickle\n",
            "100%|███████████████████████████████████████| 77.1M/77.1M [00:00<00:00, 183MB/s]\n"
          ]
        }
      ],
      "source": [
        "# !pip install gdown\n",
        "# https://drive.google.com/file/d/12YB0YDkmLtGjqBcx400HuCSyYnvVXgj8/view?usp=sharing\n",
        "!gdown --id \"12YB0YDkmLtGjqBcx400HuCSyYnvVXgj8\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BYk97BXQKFOU",
        "outputId": "9322ede0-7c99-43e6-d7da-5915f6847ba3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(90097, 10000, 4000)"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# !pip3 install pickle5\n",
        "# import pickle5 as pickle\n",
        "import logging\n",
        "logging.basicConfig()\n",
        "logging.getLogger().setLevel(logging.DEBUG\n",
        "\n",
        "import pickle\n",
        "rfile = \"data.pickle\"\n",
        "with open(rfile, 'rb') as handle:\n",
        "    train = pickle.load(handle)\n",
        "\n",
        "train = [[row[1],row[2],row[3]] for row in train]\n",
        "test = train[-4000:] ## already shuffled\n",
        "train = train[0:len(train)-4000]\n",
        "val = train[-10000:] \n",
        "train = train[0:len(train)-10000]\n",
        "len(train),len(val),len(test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQJgbXhdpP8W",
        "outputId": "640df7f1-4d76-405d-9a52-fd7cde2823bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(100097, 4000)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import pandas as pd\n",
        "# rfile = \"drive/MyDrive/Colab Notebooks/train.csv\"\n",
        "# rData = pd.read_csv(rfile)\n",
        "# rData = rData.iloc[:,2].tolist()\n",
        "\n",
        "# # rData = rData[0:10000] ######### take 10%\n",
        "\n",
        "# import nltk,random\n",
        "# nltk.download('punkt')\n",
        "# from tqdm import tqdm\n",
        "# train = []\n",
        "# #positives\n",
        "# for review in tqdm(rData):\n",
        "#     sent = nltk.sent_tokenize(review)\n",
        "#     train.append([random.choice(sent),review,1])\n",
        "\n",
        "# #negatives\n",
        "# for review in tqdm(rData):\n",
        "#     random_review = random.choice(rData)\n",
        "#     sent = nltk.sent_tokenize(random_review)\n",
        "#     train.append([random.choice(sent),review,0])\n",
        "\n",
        "\n",
        "# random.shuffle(train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J-lRio0EqBoX",
        "outputId": "570d66af-662b-4030-c528-5bd1a38ae434"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  dtype=np.int):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:35: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:597: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:836: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, fit_path=True,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, positive=False):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1097: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1344: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1480: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, copy_X=True, positive=False):\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:152: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  precompute=False, eps=np.finfo(np.float).eps,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:320: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=np.finfo(np.float).eps, random_state=None,\n",
            "/mnt/disks/user/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/randomized_l1.py:580: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  eps=4 * np.finfo(np.float).eps, n_jobs=None,\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at sentence-transformers/all-MiniLM-L6-v2 and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "# !pip install --upgrade pip\n",
        "# !pip install -U sentence-transformers\n",
        "from sentence_transformers import SentenceTransformer, models, evaluation\n",
        "\n",
        "from sentence_transformers.cross_encoder import CrossEncoder\n",
        "from sentence_transformers.cross_encoder.evaluation import CECorrelationEvaluator,CEBinaryClassificationEvaluator, CEBinaryAccuracyEvaluator\n",
        "\n",
        "model = CrossEncoder('sentence-transformers/all-MiniLM-L6-v2', num_labels=1)  ###\n",
        "# model = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall', num_labels=2)\n",
        "# model = CrossEncoder('sentence-transformers/all-MiniLM-L6-v2', num_labels=2)\n",
        "# model = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall', num_labels=2)\n",
        "# cross-encoder/nli-deberta-v3-xsmall\n",
        "# cross-encoder/ms-marco-MiniLM-L-12-v2\n",
        "# https://huggingface.co/models?sort=downloads&search=cross-encoder%2F\n",
        "# cross-encoder/stsb-roberta-large\n",
        "# cross-encoder/quora-distilroberta-base\n",
        "\n",
        "# scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])\n",
        "\n",
        "#Convert scores to labels\n",
        "# label_mapping = ['contradiction', 'entailment', 'neutral']\n",
        "# labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]\n",
        "\n",
        "\n",
        "## Step 1: use an existing language model\n",
        "# word_embedding_model = models.Transformer('microsoft/deberta-v3-xsmall')\n",
        "# word_embedding_model = models.Transformer('sentence-transformers/all-MiniLM-L6-v2')\n",
        "\n",
        "## Step 2: use a pool function over the token embeddings\n",
        "# pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension())\n",
        "\n",
        "## Join steps 1 and 2 using the modules argument\n",
        "# model = SentenceTransformer(modules=[word_embedding_model, pooling_model])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dF7qbREOqB0x"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import InputExample, losses\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "\n",
        "train_examples = []\n",
        "for i in range(len(train)):\n",
        "  train_examples.append(InputExample(texts=[train[i][0], train[i][1]], label=train[i][2])) ### not int\n",
        "\n",
        "\n",
        "## greater batch better\n",
        "train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)\n",
        "#### not used ???\n",
        "# train_loss = losses.SoftmaxLoss(model=model,sentence_embedding_dimension=384,num_labels=2)  ## OR .ContrastiveLoss\n",
        "num_epochs = 10 \n",
        "warmup_steps = int(len(train_dataloader) * num_epochs * 0.1) #10% of train data\n",
        "# CEBinaryClassificationEvaluator  ###### should works for 0,1\n",
        "evaluator = CECorrelationEvaluator([[row[0],row[1]] for row in val], \n",
        "                [row[2] for row in val])\n",
        "\n",
        "evaluatorAll = CECorrelationEvaluator([[row[0],row[1]] for row in train], \n",
        "                [row[2] for row in train])\n",
        "\n",
        "evaluatortest = CECorrelationEvaluator([[row[0],row[1]] for row in test], \n",
        "                [row[2] for row in test])\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3Ar-ZRfLaPM",
        "outputId": "94b79a49-9dfb-4917-a13b-008329b1e3eb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "-0.0543511070468631"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "evaluator(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JNZzNOWeKFOZ",
        "outputId": "e58cd01e-2430-4a8d-c950-8a37958f5f80"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator:CECorrelationEvaluator: Evaluating the model on  dataset:\n",
            "INFO:sentence_transformers.cross_encoder.evaluation.CECorrelationEvaluator:Correlation:\tPearson: 0.0004\tSpearman: -0.0021\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "-0.002125256946879608"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "evaluatortest(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1lM5cXrRKFOZ"
      },
      "outputs": [],
      "source": [
        "def testModel(score, epoch, steps):\n",
        "    print(evaluatortest(model))\n",
        "    print(score, epoch, steps)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599,
          "referenced_widgets": [
            "7549303c56ac4ca1bcc90b2d6a1a2166",
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        "id": "0cVDCtHUqIBa",
        "outputId": "7915c20b-ace4-49f5-e0bb-e707bf0a465e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "I0914 16:51:05.407576 140604938979136 tpu_cluster_resolver.py:35] Falling back to TensorFlow client; we recommended you install the Cloud TPU client directly with pip install cloud-tpu-client.\n",
            "I0914 16:51:05.868639 140604938979136 __init__.py:415] $HOME=/home/ubuntu\n",
            "I0914 16:51:05.869856 140604938979136 __init__.py:415] CONFIGDIR=/home/ubuntu/.config/matplotlib\n",
            "I0914 16:51:05.870787 140604938979136 __init__.py:415] matplotlib data path: /mnt/disks/user/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data\n",
            "I0914 16:51:05.875625 140604938979136 __init__.py:1093] loaded rc file /mnt/disks/user/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc\n",
            "I0914 16:51:05.877369 140604938979136 __init__.py:1819] matplotlib version 3.0.3\n",
            "I0914 16:51:05.877919 140604938979136 __init__.py:1820] interactive is False\n",
            "I0914 16:51:05.878815 140604938979136 __init__.py:1821] platform is linux\n",
            "I0914 16:51:05.879868 140604938979136 __init__.py:1822] loaded modules: ['sys', 'builtins', '_frozen_importlib', '_imp', '_thread', '_warnings', '_weakref', 'zipimport', '_frozen_importlib_external', '_io', 'marshal', 'posix', 'encodings', 'codecs', '_codecs', 'encodings.aliases', 'encodings.utf_8', '_signal', '__main__', 'encodings.latin_1', 'io', 'abc', '_abc', '_bootlocale', '_locale', 'site', 'os', 'stat', '_stat', 'posixpath', 'genericpath', 'os.path', '_collections_abc', '_sitebuiltins', 'pwd', 'types', 'importlib', 'importlib._bootstrap', 'importlib._bootstrap_external', 'warnings', 'importlib.util', 'importlib.abc', 'importlib.machinery', 'contextlib', 'collections', 'operator', '_operator', 'keyword', 'heapq', '_heapq', 'itertools', 'reprlib', '_collections', 'functools', '_functools', 'google', 'mpl_toolkits', 'sphinxcontrib', 'zope', 'runpy', 'pkgutil', 'weakref', '_weakrefset', 'ipykernel', 'ipykernel._version', 'ipykernel.connect', '__future__', 'json', 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'tensorflow.python.distribute.cluster_resolver.cluster_resolver', 'tensorflow.python.training.server_lib', 'tensorflow.python.distribute.cluster_resolver.gce_cluster_resolver', 'tensorflow.python.distribute.cluster_resolver.kubernetes_cluster_resolver', 'tensorflow.python.distribute.cluster_resolver.slurm_cluster_resolver', 'tensorflow.python.distribute.cluster_resolver.tfconfig_cluster_resolver', 'tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver', 'tensorflow.python.tpu', 'tensorflow.python.tpu.client', 'tensorflow.python.tpu.client.client', 'tensorflow.python.distribute.cross_device_ops', 'tensorflow.python.client.device_lib', 'tensorflow.core.framework.device_attributes_pb2', 'tensorflow.python._pywrap_device_lib', 'tensorflow.python.distribute.collective_util', 'tensorflow.python.distribute.cross_device_utils', 'tensorflow.python.distribute.all_reduce', 'tensorflow.python.ops.nccl_ops', 'tensorflow.python.ops.gen_nccl_ops', 'tensorflow.python.distribute.values', 'tensorflow.python.distribute.device_util', 'tensorflow.python.distribute.distribute_lib', 'tensorflow.python.distribute.distribution_strategy_context', 'tensorflow.python.distribute.numpy_dataset', 'tensorflow.python.distribute.reduce_util', 'tensorflow.python.ops.losses', 'tensorflow.python.ops.losses.loss_reduction', 'tensorflow.python.ops.losses.losses_impl', 'tensorflow.python.ops.confusion_matrix', 'tensorflow.python.ops.nn', 'tensorflow.python.ops.ctc_ops', 'tensorflow.python.ops.gen_ctc_ops', 'tensorflow.python.ops.inplace_ops', 'tensorflow.python.ops.linalg_ops', 'tensorflow.python.ops.map_fn', 'tensorflow.python.ops.nn_grad', 'tensorflow.python.ops.embedding_ops', 'tensorflow.python.ops.data_flow_grad', 'tensorflow.python.ops.nn_impl', 'tensorflow.python.ops.candidate_sampling_ops', 'tensorflow.python.ops.gen_candidate_sampling_ops', 'tensorflow.python.ops.losses.util', 'tensorflow.python.ops.weights_broadcast_ops', 'tensorflow.python.ops.sets', 'tensorflow.python.ops.sets_impl', 'tensorflow.python.ops.gen_set_ops', 'tensorflow.tools', 'tensorflow.tools.docs', 'tensorflow.tools.docs.doc_controls', 'tensorflow.python.ops.collective_ops', 'tensorflow.python.ops.gen_collective_ops', 'tensorflow.python.distribute.tpu_values', 'tensorflow.python.tpu.tpu', 'tensorflow.core.protobuf.tpu', 'tensorflow.core.protobuf.tpu.dynamic_padding_pb2', 'tensorflow.python.compiler', 'tensorflow.python.compiler.xla', 'tensorflow.python.compiler.xla.jit', 'tensorflow.python.compiler.xla.xla', 'tensorflow.compiler', 'tensorflow.compiler.jit', 'tensorflow.compiler.jit.ops', 'tensorflow.compiler.jit.ops.xla_ops', 'tensorflow.compiler.jit.ops.xla_ops_grad', 'tensorflow.python.distribute.summary_op_util', 'tensorflow.python.tpu.tpu_function', 'tensorflow.python.tpu.ops', 'tensorflow.python.tpu.ops.tpu_ops', 'tensorflow.python.ops.gen_tpu_ops', 'tensorflow.python.framework.kernels', 'tensorflow.core.framework.kernel_def_pb2', 'tensorflow.python.distribute.mirrored_strategy', 'tensorflow.python.distribute.input_lib', 'tensorflow.python.data.experimental.ops.distribute', 'tensorflow.python.data.ops.multi_device_iterator_ops', 'tensorflow.python.distribute.input_ops', 'tensorflow.python.distribute.multi_worker_util', 'tensorflow.python.distribute.distribute_coordinator_context', 'tensorflow.python.distribute.shared_variable_creator', 'tensorflow.python.training.coordinator', 'tensorflow.python.distribute.one_device_strategy', 'tensorflow.python.distribute.experimental', 'tensorflow.python.distribute.central_storage_strategy', 'tensorflow.python.distribute.parameter_server_strategy', 'tensorflow.python.training.device_setter', 'tensorflow.python.distribute.collective_all_reduce_strategy', 'tensorflow.python.distribute.tpu_strategy', 'tensorflow.compiler.xla', 'tensorflow.compiler.xla.experimental', 'tensorflow.compiler.xla.experimental.xla_sharding', 'tensorflow.compiler.xla.experimental.xla_sharding.xla_sharding', 'tensorflow.compiler.tf2xla', 'tensorflow.compiler.tf2xla.python', 'tensorflow.compiler.tf2xla.python.xla', 'tensorflow.compiler.tf2xla.ops', 'tensorflow.compiler.tf2xla.ops.gen_xla_ops', 'tensorflow.python.ops.bitwise_ops', 'tensorflow.compiler.xla.xla_data_pb2', 'tensorflow.python.tpu.device_assignment', 'tensorflow.python.tpu.topology', 'tensorflow.core.protobuf.tpu.topology_pb2', 'tensorflow.python.tpu.tpu_strategy_util', 'tensorflow.python.tpu.tpu_system_metadata', 'tensorflow.python.tpu.training_loop', 'tensorflow.python.tpu.tensor_tracer', 'tensorflow.python.platform.analytics', 'tensorflow.python.tpu.tensor_tracer_flags', 'tensorflow.python.tpu.tensor_tracer_report', 'tensorflow.python.tpu.tensor_tracer_pb2', 'tensorflow.python.training.experimental.loss_scale', 'tensorflow.python.ops.array_grad', 'tensorflow.python.ops.cudnn_rnn_grad', 'tensorflow.python.ops.gen_cudnn_rnn_ops', 'tensorflow.python.ops.manip_grad', 'tensorflow.python.ops.manip_ops', 'tensorflow.python.ops.gen_manip_ops', 'tensorflow.python.ops.math_grad', 'tensorflow.python.ops.random_grad', 'tensorflow.python.ops.rnn_grad', 'tensorflow.python.ops.gen_rnn_ops', 'tensorflow.python.ops.sparse_grad', 'tensorflow.python.ops.state_grad', 'tensorflow.python.ops.tensor_array_grad', 'tensorflow.python.ops.special_math_ops', 'opt_einsum', 'opt_einsum.blas', 'opt_einsum.helpers', 'opt_einsum.parser', 'opt_einsum.paths', 'opt_einsum.path_random', 'opt_einsum.contract', 'opt_einsum.backends', 'opt_einsum.backends.cupy', 'opt_einsum.sharing', 'opt_einsum.backends.dispatch', 'opt_einsum.backends.jax', 'opt_einsum.backends.tensorflow', 'opt_einsum.backends.theano', 'opt_einsum.backends.torch', 'opt_einsum._version', 'tensorflow.python.ops.gen_special_math_ops', 'tensorflow.python.eager.wrap_function', 'tensorflow.python.saved_model.nested_structure_coder', 'tensorflow.python.ops.batch_ops', 'tensorflow.python.ops.gen_batch_ops', 'tensorflow.python.ops.critical_section_ops', 'tensorflow.python.ops.gradients', 'tensorflow.python.eager.forwardprop', 'tensorflow.python.ops.gradients_impl', 'tensorflow.python.ops.control_flow_grad', 'tensorflow.python.ops.image_grad', 'tensorflow.python.ops.gen_image_ops', 'tensorflow.python.ops.linalg_grad', 'tensorflow.python.ops.linalg', 'tensorflow.python.ops.linalg.linalg_impl', 'tensorflow.python.ops.optional_grad', 'tensorflow.python.ops.histogram_ops', 'tensorflow.python.ops.lookup_ops', 'tensorflow.python.ops.gen_lookup_ops', 'tensorflow.python.ops.numerics', 'tensorflow.python.ops.partitioned_variables', 'tensorflow.python.ops.proto_ops', 'tensorflow.python.ops.gen_decode_proto_ops', 'tensorflow.python.ops.gen_encode_proto_ops', 'tensorflow.python.ops.stateless_random_ops', 'tensorflow.python.ops.template', 'tensorflow.python.training.tracking.util', 'tensorflow.python.training.saving.functional_saver', 'tensorflow.python.training.saving.saveable_hook', 'tensorflow.python.training.tracking.graph_view', 'tensorflow.python.training.optimizer', 'tensorflow.python.training.slot_creator', 'tensorflow.python.ops.parallel_for', 'tensorflow.python.ops.parallel_for.control_flow_ops', 'tensorflow.python.ops.parallel_for.pfor', 'tensorflow.python.ops.gen_spectral_ops', 'tensorflow.python.ops.parallel_for.gradients', 'tensorflow.python.compiler.tensorrt', 'tensorflow.python.compiler.tensorrt.trt_convert', 'tensorflow.python.framework.convert_to_constants', 'tensorflow.python.grappler', 'tensorflow.python.grappler.tf_optimizer', 'tensorflow.python._pywrap_tf_optimizer', 'tensorflow.python.grappler.cluster', 'tensorflow.core.grappler', 'tensorflow.core.grappler.costs', 'tensorflow.core.grappler.costs.op_performance_data_pb2', 'tensorflow.core.protobuf.device_properties_pb2', 'tensorflow.python._pywrap_tf_cluster', 'tensorflow.python.saved_model.builder', 'tensorflow.python.saved_model.builder_impl', 'tensorflow.core.protobuf.saved_model_pb2', 'tensorflow.python.saved_model.constants', 'tensorflow.python.saved_model.signature_def_utils', 'tensorflow.python.saved_model.signature_def_utils_impl', 'tensorflow.python.saved_model.signature_constants', 'tensorflow.python.saved_model.utils_impl', 'tensorflow.python.saved_model.load', 'tensorflow.python.saved_model.function_deserialization', 'tensorflow.python.framework.function_def_to_graph', 'tensorflow.python.saved_model.load_v1_in_v2', 'tensorflow.python.saved_model.loader_impl', 'tensorflow.python.saved_model.signature_serialization', 'tensorflow.python.saved_model.function_serialization', 'tensorflow.python.training.monitored_session', 'tensorflow.python.ops.resources', 'tensorflow.python.summary.summary', 'google.protobuf.json_format', 'tensorflow.python.training.queue_runner', 'tensorflow.python.training.queue_runner_impl', 'tensorflow.core.protobuf.queue_runner_pb2', 'tensorflow.python.training.session_manager', 'tensorflow.python.saved_model.loader', 'tensorflow.python.saved_model.save', 'tensorflow.python.saved_model.save_options', 'tensorflow.python.saved_model.tag_constants', 'tensorflow.compiler.tf2tensorrt', 'tensorflow.compiler.tf2tensorrt.ops', 'tensorflow.compiler.tf2tensorrt.ops.gen_trt_ops', 'tensorflow.python.ops.initializers_ns', 'tensorflow.python.keras', 'tensorflow.python.keras.models', 'tensorflow.python.keras.backend', 'tensorflow.python.distribute.distribute_coordinator', 'tensorflow.python.keras.backend_config', 'tensorflow.python.ops.image_ops', 'tensorflow.python.ops.image_ops_impl', 'tensorflow.python.training.moving_averages', 'tensorflow.python.keras.metrics', 'tensorflow.python.keras.engine', 'tensorflow.python.keras.engine.base_layer', 'tensorflow.python.keras.constraints', 'tensorflow.python.keras.utils', 'tensorflow.python.keras.utils.generic_utils', 'tensorflow.python.keras.initializers', 'tensorflow.python.ops.init_ops_v2', 'tensorflow.python.keras.regularizers', 'tensorflow.python.keras.engine.base_layer_utils', 'tensorflow.python.keras.engine.input_spec', 'tensorflow.python.keras.engine.node', 'tensorflow.python.keras.mixed_precision', 'tensorflow.python.keras.mixed_precision.experimental', 'tensorflow.python.keras.mixed_precision.experimental.autocast_variable', 'tensorflow.python.keras.mixed_precision.experimental.loss_scale_optimizer', 'tensorflow.python.keras.optimizers', 'tensorflow.python.keras.optimizer_v2', 'tensorflow.python.keras.optimizer_v2.adadelta', 'tensorflow.python.keras.optimizer_v2.optimizer_v2', 'tensorflow.python.keras.optimizer_v2.learning_rate_schedule', 'tensorflow.python.keras.utils.tf_utils', 'tensorflow.python.training.training_ops', 'tensorflow.python.training.gen_training_ops', 'tensorflow.python.keras.optimizer_v2.adagrad', 'tensorflow.python.keras.optimizer_v2.adam', 'tensorflow.python.keras.optimizer_v2.adamax', 'tensorflow.python.keras.optimizer_v2.ftrl', 'tensorflow.python.keras.optimizer_v2.gradient_descent', 'tensorflow.python.keras.optimizer_v2.nadam', 'tensorflow.python.keras.optimizer_v2.rmsprop', 'tensorflow.python.keras.mixed_precision.experimental.loss_scale', 'tensorflow.python.keras.mixed_precision.experimental.policy', 'tensorflow.python.keras.mixed_precision.experimental.device_compatibility_check', 'tensorflow.python.framework.gpu_util', 'tensorflow.python.keras.saving', 'tensorflow.python.keras.saving.saved_model', 'tensorflow.python.keras.saving.saved_model.layer_serialization', 'tensorflow.python.keras.saving.saved_model.base_serialization', 'tensorflow.python.keras.saving.saved_model.json_utils', 'tensorflow.python.util.serialization', 'tensorflow.python.keras.saving.saved_model.constants', 'tensorflow.python.keras.saving.saved_model.save_impl', 'tensorflow.python.keras.saving.saving_utils', 'tensorflow.python.keras.losses', 'tensorflow.python.keras.utils.losses_utils', 'tensorflow.python.keras.utils.io_utils', 'h5py', 'h5py._errors', 'h5py._hl', 'h5py._hl.compat', 'h5py.version', 'h5py.h5', '_cython_0_29_13', 'h5py.defs', 'h5py._objects', 'h5py._conv', 'h5py.h5r', 'h5py.h5t', 'h5py.utils', 'h5py.h5py_warnings', 'h5py.h5z', 'h5py.h5a', 'h5py.h5s', 'h5py.h5p', 'h5py.h5ac', 'h5py._proxy', 'h5py.h5d', 'h5py.h5ds', 'h5py.h5f', 'h5py.h5g', 'h5py.h5i', 'h5py.h5fd', 'h5py.h5pl', 'h5py._hl.filters', 'h5py._hl.base', 'h5py._hl.files', 'h5py._hl.group', 'h5py.h5o', 'h5py.h5l', 'h5py._hl.dataset', 'h5py._hl.selections', 'h5py._hl.selections2', 'h5py._hl.datatype', 'h5py._hl.vds', 'h5py._hl.attrs', 'tensorflow.python.keras.saving.saved_model.load', 'tensorflow.python.keras.saving.saved_model.utils', 'tensorflow.python.keras.saving.saved_model.serialized_attributes', 'tensorflow.python.keras.utils.metrics_utils', 'tensorflow.python.keras.utils.layer_utils', 'tensorflow.python.keras.utils.conv_utils', 'tensorflow.python.keras.utils.version_utils', 'tensorflow.python.module', 'tensorflow.python.module.module', 'tensorflow.python.keras.saving.saved_model.metric_serialization', 'tensorflow.python.keras.engine.network', 'tensorflow.python.keras.engine.compile_utils', 'tensorflow.python.keras.engine.input_layer', 'tensorflow.python.keras.distribute', 'tensorflow.python.keras.distribute.distributed_training_utils', 'tensorflow.python.keras.callbacks', 'tensorflow.python.distribute.distributed_file_utils', 'tensorflow.python.keras.distribute.multi_worker_training_state', 'tensorflow.python.keras.utils.mode_keys', 'tensorflow.python.saved_model.model_utils', 'tensorflow.python.saved_model.model_utils.export_output', 'tensorflow.python.saved_model.model_utils.export_utils', 'tensorflow.python.saved_model.model_utils.mode_keys', 'tensorflow.python.keras.utils.data_utils', 'multiprocessing.dummy', 'multiprocessing.dummy.connection', 'tensorflow.python.profiler.profiler_v2', 'tensorflow.python.keras.engine.training_utils', 'tensorflow.python.framework.composite_tensor_utils', 'tensorflow.python.keras.saving.hdf5_format', 'tensorflow.python.keras.saving.model_config', 'tensorflow.python.keras.saving.save', 'tensorflow.python.keras.saving.saved_model.save', 'tensorflow.python.keras.saving.saved_model.network_serialization', 'tensorflow.python.keras.engine.sequential', 'tensorflow.python.keras.layers', 'tensorflow.python.keras.engine.base_preprocessing_layer', 'tensorflow.python.keras.engine.training_generator', 'tensorflow.python.keras.layers.preprocessing', 'tensorflow.python.keras.layers.preprocessing.image_preprocessing', 'tensorflow.python.ops.stateful_random_ops', 'tensorflow.python.ops.gen_stateful_random_ops', 'tensorflow.python.keras.layers.preprocessing.normalization', 'tensorflow.python.keras.layers.preprocessing.normalization_v1', 'tensorflow.python.keras.engine.base_preprocessing_layer_v1', 'tensorflow.python.keras.layers.preprocessing.text_vectorization', 'tensorflow.python.keras.layers.preprocessing.categorical_encoding', 'tensorflow.python.keras.layers.preprocessing.index_lookup', 'tensorflow.python.keras.layers.preprocessing.text_vectorization_v1', 'tensorflow.python.keras.layers.preprocessing.categorical_encoding_v1', 'tensorflow.python.keras.layers.preprocessing.index_lookup_v1', 'tensorflow.python.keras.layers.advanced_activations', 'tensorflow.python.keras.layers.convolutional', 'tensorflow.python.keras.activations', 'tensorflow.python.keras.layers.pooling', 'tensorflow.python.keras.layers.core', 'tensorflow.python.keras.layers.dense_attention', 'tensorflow.python.keras.layers.embeddings', 'tensorflow.python.keras.layers.local', 'tensorflow.python.keras.layers.merge', 'tensorflow.python.keras.layers.noise', 'tensorflow.python.keras.layers.normalization', 'tensorflow.python.keras.layers.normalization_v2', 'tensorflow.python.keras.layers.kernelized', 'tensorflow.python.keras.layers.recurrent', 'tensorflow.python.keras.layers.recurrent_v2', 'tensorflow.python.keras.layers.convolutional_recurrent', 'tensorflow.python.keras.layers.cudnn_recurrent', 'tensorflow.python.keras.layers.wrappers', 'tensorflow.python.keras.layers.rnn_cell_wrapper_v2', 'tensorflow.python.ops.rnn_cell_wrapper_impl', 'tensorflow.python.keras.layers.serialization', 'tensorflow.python.keras.engine.training', 'tensorflow.python.keras.engine.data_adapter', 'pandas', 'pytz', 'pytz.exceptions', 'pytz.lazy', 'pytz.tzinfo', 'pytz.tzfile', 'pandas.compat', 'pandas.compat.numpy', 'pandas._libs', 'pandas._libs.tslibs', 'pandas._libs.tslibs.conversion', 'pandas._libs.tslibs.c_timestamp', '_cython_0_28_2', 'pandas._libs.tslibs.nattype', 'pandas._libs.tslibs.np_datetime', 'pandas._libs.tslibs.timezones', 'pandas._libs.tslibs.tzconversion', 'pandas._libs.tslibs.timedeltas', 'pandas._libs.tslibs.offsets', 'pandas._libs.tslibs.ccalendar', 'pandas._config', 'pandas._config.config', 'pandas._config.dates', 'pandas._config.display', 'pandas._config.localization', 'pandas._libs.tslibs.strptime', 'pandas._libs.tslibs.fields', 'pandas._libs.tslibs.parsing', 'pandas._libs.tslibs.period', 'pandas._libs.tslibs.frequencies', 'pandas._libs.tslibs.timestamps', 'pandas._libs.tslibs.resolution', 'pandas._libs.hashtable', 'pandas._libs.missing', 'pandas._libs.lib', 'pandas._libs.tslib', 'pandas.core', 'pandas.core.config_init', 'pandas.core.api', 'pandas.core.arrays', 'pandas.core.arrays.array_', 'pandas.core.dtypes', 'pandas.core.dtypes.common', 'pandas._libs.algos', 'pandas.core.dtypes.dtypes', 'pandas._libs.interval', 'pandas.core.dtypes.generic', 'pandas.core.dtypes.base', 'pandas.errors', 'pandas.core.dtypes.inference', 'pandas._typing', 'pandas.core.arrays.base', 'pandas.compat.numpy.function', 'pandas.util', 'pandas.util._decorators', 'pandas._libs.properties', 'pandas.core.util', 'pandas.core.util.hashing', 'pandas._libs.hashing', 'pandas.core.dtypes.cast', 'pandas.core.dtypes.missing', 'pandas.util._validators', 'pandas.core.ops', 'pandas._libs.ops', 'pandas.core.common', 'pandas.core.ops.missing', 'pandas.core.ops.roperator', 'pandas.core.ops.docstrings', 'pandas.core.sorting', 'pandas.core.algorithms', 'pandas.core.indexers', 'pandas.core.arrays.categorical', 'pandas.core.accessor', 'pandas.core.base', 'pandas.core.nanops', 'pandas.compat._optional', 'bottleneck', 'bottleneck.reduce', 'bottleneck.nonreduce', 'bottleneck.nonreduce_axis', 'bottleneck.move', 'bottleneck.slow', 'bottleneck.slow.reduce', 'bottleneck.slow.nonreduce', 'bottleneck.slow.nonreduce_axis', 'bottleneck.slow.move', 'bottleneck.version', 'bottleneck.benchmark', 'bottleneck.benchmark.bench', 'bottleneck.benchmark.autotimeit', 'bottleneck.benchmark.bench_detailed', 'bottleneck.tests', 'bottleneck.tests.util', 'pandas.core.missing', 'pandas.io', 'pandas.io.formats', 'pandas.io.formats.console', 'pandas.core.arrays.datetimes', 'pandas.core.arrays.datetimelike', 'pandas.tseries', 'pandas.tseries.frequencies', 'pandas.tseries.offsets', 'dateutil.easter', 'pandas.core.tools', 'pandas.core.tools.datetimes', 'pandas.core.arrays._ranges', 'pandas.core.arrays.integer', 'pandas.core.tools.numeric', 'pandas.core.arrays.interval', 'pandas.core.indexes', 'pandas.core.indexes.base', 'pandas._libs.index', 'pandas._libs.join', 'pandas.core.dtypes.concat', 'pandas.core.indexes.frozen', 'pandas.io.formats.printing', 'pandas.core.strings', 'pandas.core.arrays.numpy_', 'pandas.core.arrays.period', 'pandas.core.arrays.sparse', 'pandas._libs.sparse', 'pandas.core.arrays.timedeltas', 'pandas.core.groupby', 'pandas.core.groupby.generic', 'pandas.core.frame', 'pandas.core.generic', 'pandas.core.index', 'pandas.core.indexes.api', 'pandas.core.indexes.category', 'pandas.core.indexes.datetimes', 'pandas.core.indexes.datetimelike', 'pandas.core.tools.timedeltas', 'pandas.core.indexes.numeric', 'pandas.core.indexes.interval', 'pandas.util._exceptions', 'pandas.core.indexes.multi', 'pandas.core.indexes.timedeltas', 'pandas.core.indexes.period', 'pandas.core.indexes.range', 'pandas.core.indexing', 'pandas._libs.indexing', 'pandas.core.internals', 'pandas.core.internals.blocks', 'pandas._libs.internals', 'pandas.core.internals.arrays', 'pandas.core.internals.managers', 'pandas.core.internals.concat', 'pandas.io.formats.format', 'pandas.io.common', 'pandas.core.internals.construction', 'pandas.core.series', 'pandas._libs.reshape', 'pandas.core.indexes.accessors', 'pandas.plotting', 'pandas.plotting._core', 'pandas.plotting._matplotlib', 'pandas.plotting._matplotlib.boxplot', 'matplotlib', 'matplotlib.cbook', 'matplotlib.cbook.deprecation', 'matplotlib.rcsetup', 'matplotlib.fontconfig_pattern', 'pyparsing', 'matplotlib.colors', 'matplotlib._color_data', 'cycler', 'matplotlib._version']\n",
            "I0914 16:51:05.916604 140604938979136 __init__.py:415] CACHEDIR=/home/ubuntu/.cache/matplotlib\n",
            "I0914 16:51:05.918007 140604938979136 font_manager.py:1362] Using fontManager instance from /home/ubuntu/.cache/matplotlib/fontlist-v300.json\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "57d858ebdc1c4ecebb07d80fb903544b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Epoch', max=10, style=ProgressStyle(description_width='initia…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "881ce66421284074b023063be31a161e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Iteration', max=5632, style=ProgressStyle(description_width='…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "response = model.fit(train_dataloader=train_dataloader,\n",
        "          epochs=num_epochs,\n",
        "          warmup_steps=warmup_steps, \n",
        "          evaluator=evaluator, \n",
        "          evaluation_steps=500,\n",
        "            callback = testModel,\n",
        "          output_path=\"models\")\n",
        "\n",
        "\n",
        "\n",
        "# evaluator2 = evaluation.BinaryClassificationEvaluator ([row[0] for row in train[0:10000]], \n",
        "#                 [row[1] for row in train[0:10000]], \n",
        "#                 [row[2] for row in train[0:10000]], \n",
        "#                 show_progress_bar = True)\n",
        "# evaluator2(model)#0.9179824272299182"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcxXxZJF_S9k",
        "outputId": "09c36647-30c0-43ad-bd20-2f43172a14f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9666181938058408"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "evaluator(model)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7ri0nrhGhvb",
        "outputId": "2a620300-8079-43ca-aceb-30eebb2e5649"
      },
      "outputs": [
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        "## add min limit in sent rand step, and in case < 5 words match, add as 0, \n"
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