HindiBBC.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0v6eJOBLwmE",
        "colab_type": "code",
        "outputId": "5f086d67-acfc-4b42-8b9c-e90a7892d535",
        "colab": {
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          "height": 1000
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      },
      "source": [
        "! pip install tensorboardX pandas simpletransformers transformers"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorboardX\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/35/f1/5843425495765c8c2dd0784a851a93ef204d314fc87bcc2bbb9f662a3ad1/tensorboardX-2.0-py2.py3-none-any.whl (195kB)\n",
            "\r\u001b[K     |█▊                              | 10kB 15.4MB/s eta 0:00:01\r\u001b[K     |███▍                            | 20kB 5.0MB/s eta 0:00:01\r\u001b[K     |█████                           | 30kB 6.0MB/s eta 0:00:01\r\u001b[K     |██████▊                         | 40kB 6.7MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 51kB 5.5MB/s eta 0:00:01\r\u001b[K     |██████████                      | 61kB 6.0MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 71kB 6.2MB/s eta 0:00:01\r\u001b[K     |█████████████▍                  | 81kB 6.6MB/s eta 0:00:01\r\u001b[K     |███████████████                 | 92kB 6.6MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 102kB 6.9MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 112kB 6.9MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 122kB 6.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████▊          | 133kB 6.9MB/s eta 0:00:01\r\u001b[K     |███████████████████████▌        | 143kB 6.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▏      | 153kB 6.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▉     | 163kB 6.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▌   | 174kB 6.9MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▏ | 184kB 6.9MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 194kB 6.9MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 204kB 6.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.0.3)\n",
            "Collecting simpletransformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e7/75/836693dae9f2fab7dae172c9f7e2e1292898df377e113be06b061f7f9e0a/simpletransformers-0.23.2-py3-none-any.whl (148kB)\n",
            "\u001b[K     |████████████████████████████████| 153kB 14.4MB/s \n",
            "\u001b[?25hCollecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/37/ba/dda44bbf35b071441635708a3dd568a5ca6bf29f77389f7c7c6818ae9498/transformers-2.7.0-py3-none-any.whl (544kB)\n",
            "\u001b[K     |████████████████████████████████| 552kB 14.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (3.10.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.12.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from tensorboardX) (1.18.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
            "Collecting tokenizers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/73/de/ec55e2d5a8720557b25100dd7dd4a63108a44b6b303978ce2587666931cf/tokenizers-0.6.0-cp36-cp36m-manylinux1_x86_64.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 23.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2.21.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (1.4.1)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (2019.12.20)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (0.22.2.post1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from simpletransformers) (4.38.0)\n",
            "Collecting seqeval\n",
            "  Downloading https://files.pythonhosted.org/packages/34/91/068aca8d60ce56dd9ba4506850e876aba5e66a6f2f29aa223224b50df0de/seqeval-0.0.12.tar.gz\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a6/b4/7a41d630547a4afd58143597d5a49e07bfd4c42914d8335b2a5657efc14b/sacremoses-0.0.38.tar.gz (860kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 40.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/f4/2d5214cbf13d06e7cb2c20d84115ca25b53ea76fa1f0ade0e3c9749de214/sentencepiece-0.1.85-cp36-cp36m-manylinux1_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 41.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from transformers) (1.12.33)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorboardX) (46.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->simpletransformers) (2019.11.28)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->simpletransformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->simpletransformers) (1.24.3)\n",
            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->simpletransformers) (2.8)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->simpletransformers) (0.14.1)\n",
            "Requirement already satisfied: Keras>=2.2.4 in /usr/local/lib/python3.6/dist-packages (from seqeval->simpletransformers) (2.2.5)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.1)\n",
            "Requirement already satisfied: botocore<1.16.0,>=1.15.33 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (1.15.33)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.9.5)\n",
            "Requirement already satisfied: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from boto3->transformers) (0.3.3)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.2.4->seqeval->simpletransformers) (1.0.8)\n",
            "Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from Keras>=2.2.4->seqeval->simpletransformers) (1.1.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from Keras>=2.2.4->seqeval->simpletransformers) (2.10.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from Keras>=2.2.4->seqeval->simpletransformers) (3.13)\n",
            "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.16.0,>=1.15.33->boto3->transformers) (0.15.2)\n",
            "Building wheels for collected packages: seqeval, sacremoses\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-0.0.12-cp36-none-any.whl size=7424 sha256=bb36c58a905235e43526fb4b34434c2e2dc51e57b4b7802306bc1343cf5f5255\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/32/0a/df3b340a82583566975377d65e724895b3fad101a3fb729f68\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.38-cp36-none-any.whl size=884628 sha256=ed20980ffa53978e674444b85dc550d76a4abd9155b5421a52ce1728a2ae1a46\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/ec/1a/21b8912e35e02741306f35f66c785f3afe94de754a0eaf1422\n",
            "Successfully built seqeval sacremoses\n",
            "\u001b[31mERROR: transformers 2.7.0 has requirement tokenizers==0.5.2, but you'll have tokenizers 0.6.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: tensorboardX, tokenizers, sacremoses, sentencepiece, transformers, seqeval, simpletransformers\n",
            "Successfully installed sacremoses-0.0.38 sentencepiece-0.1.85 seqeval-0.0.12 simpletransformers-0.23.2 tensorboardX-2.0 tokenizers-0.6.0 transformers-2.7.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-m1NBtpL13h",
        "colab_type": "code",
        "outputId": "06d96afb-7ce9-4645-a5fc-e45ccc9d3db4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        }
      },
      "source": [
        "!nvcc --version"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2018 NVIDIA Corporation\n",
            "Built on Sat_Aug_25_21:08:01_CDT_2018\n",
            "Cuda compilation tools, release 10.0, V10.0.130\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8z7JP2XS6E3",
        "colab_type": "code",
        "outputId": "ade7547c-e18b-4ce6-ece0-2e438e9b38e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        }
      },
      "source": [
        "! pip install https://download.pytorch.org/whl/cu100/torch-1.4.0%2Bcu100-cp36-cp36m-linux_x86_64.whl"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting torch==1.4.0+cu100\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu100/torch-1.4.0%2Bcu100-cp36-cp36m-linux_x86_64.whl (723.9MB)\n",
            "\u001b[K     |████████████████████████████████| 723.9MB 25kB/s \n",
            "\u001b[?25hInstalling collected packages: torch\n",
            "  Found existing installation: torch 1.4.0\n",
            "    Uninstalling torch-1.4.0:\n",
            "      Successfully uninstalled torch-1.4.0\n",
            "Successfully installed torch-1.4.0+cu100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WdiCz2GsUkqj",
        "colab_type": "code",
        "outputId": "7304c6ba-6632-4919-ea13-2071319043ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "! pip uninstall torchvision -y\n",
        "! pip install torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling torchvision-0.5.0:\n",
            "  Successfully uninstalled torchvision-0.5.0\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torchvision==0.5.0+cu100\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu100/torchvision-0.5.0%2Bcu100-cp36-cp36m-linux_x86_64.whl (4.0MB)\n",
            "\u001b[K     |████████████████████████████████| 4.1MB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0+cu100) (1.12.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0+cu100) (1.18.2)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0+cu100) (7.0.0)\n",
            "Requirement already satisfied: torch==1.4.0 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.5.0+cu100) (1.4.0+cu100)\n",
            "Installing collected packages: torchvision\n",
            "Successfully installed torchvision-0.5.0+cu100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjvRzZ4DUQtU",
        "colab_type": "code",
        "outputId": "4b81f652-e43f-4534-a505-f377ab8dd4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! pip uninstall apex -y\n",
        "! rm -rf apex\n",
        "! git clone https://www.github.com/nvidia/apex\n",
        "! cd apex && python setup.py install --cpp_ext --cuda_ext"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[33mWARNING: Skipping apex as it is not installed.\u001b[0m\n",
            "Cloning into 'apex'...\n",
            "warning: redirecting to https://github.com/nvidia/apex.git/\n",
            "remote: Enumerating objects: 115, done.\u001b[K\n",
            "remote: Counting objects: 100% (115/115), done.\u001b[K\n",
            "remote: Compressing objects: 100% (71/71), done.\u001b[K\n",
            "remote: Total 6284 (delta 81), reused 70 (delta 44), pack-reused 6169\u001b[K\n",
            "Receiving objects: 100% (6284/6284), 13.66 MiB | 11.05 MiB/s, done.\n",
            "Resolving deltas: 100% (4131/4131), done.\n",
            "torch.__version__  =  1.4.0+cu100\n",
            "setup.py:46: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!\n",
            "  warnings.warn(\"Option --pyprof not specified. Not installing PyProf dependencies!\")\n",
            "\n",
            "Compiling cuda extensions with\n",
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2018 NVIDIA Corporation\n",
            "Built on Sat_Aug_25_21:08:01_CDT_2018\n",
            "Cuda compilation tools, release 10.0, V10.0.130\n",
            "from /usr/local/cuda/bin\n",
            "\n",
            "running install\n",
            "running bdist_egg\n",
            "running egg_info\n",
            "creating apex.egg-info\n",
            "writing apex.egg-info/PKG-INFO\n",
            "writing dependency_links to apex.egg-info/dependency_links.txt\n",
            "writing top-level names to apex.egg-info/top_level.txt\n",
            "writing manifest file 'apex.egg-info/SOURCES.txt'\n",
            "writing manifest file 'apex.egg-info/SOURCES.txt'\n",
            "installing library code to build/bdist.linux-x86_64/egg\n",
            "running install_lib\n",
            "running build_py\n",
            "creating build\n",
            "creating build/lib.linux-x86_64-3.6\n",
            "creating build/lib.linux-x86_64-3.6/apex\n",
            "copying apex/__init__.py -> build/lib.linux-x86_64-3.6/apex\n",
            "creating build/lib.linux-x86_64-3.6/apex/normalization\n",
            "copying apex/normalization/fused_layer_norm.py -> build/lib.linux-x86_64-3.6/apex/normalization\n",
            "copying apex/normalization/__init__.py -> build/lib.linux-x86_64-3.6/apex/normalization\n",
            "creating build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/handle.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/utils.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/_amp_state.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/_process_optimizer.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/amp.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/opt.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/wrap.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/__version__.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/_initialize.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/rnn_compat.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/__init__.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/compat.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/frontend.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "copying apex/amp/scaler.py -> build/lib.linux-x86_64-3.6/apex/amp\n",
            "creating build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "copying apex/optimizers/fused_lamb.py -> build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "copying apex/optimizers/fused_sgd.py -> build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "copying apex/optimizers/fused_adam.py -> build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "copying apex/optimizers/fused_novograd.py -> build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "copying apex/optimizers/__init__.py -> build/lib.linux-x86_64-3.6/apex/optimizers\n",
            "creating build/lib.linux-x86_64-3.6/apex/contrib\n",
            "copying apex/contrib/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib\n",
            "creating build/lib.linux-x86_64-3.6/apex/RNN\n",
            "copying apex/RNN/cells.py -> build/lib.linux-x86_64-3.6/apex/RNN\n",
            "copying apex/RNN/RNNBackend.py -> build/lib.linux-x86_64-3.6/apex/RNN\n",
            "copying apex/RNN/models.py -> build/lib.linux-x86_64-3.6/apex/RNN\n",
            "copying apex/RNN/__init__.py -> build/lib.linux-x86_64-3.6/apex/RNN\n",
            "creating build/lib.linux-x86_64-3.6/apex/pyprof\n",
            "copying apex/pyprof/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof\n",
            "creating build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/multiproc.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/distributed.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/optimized_sync_batchnorm.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/optimized_sync_batchnorm_kernel.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/LARC.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/sync_batchnorm.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/__init__.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "copying apex/parallel/sync_batchnorm_kernel.py -> build/lib.linux-x86_64-3.6/apex/parallel\n",
            "creating build/lib.linux-x86_64-3.6/apex/reparameterization\n",
            "copying apex/reparameterization/weight_norm.py -> build/lib.linux-x86_64-3.6/apex/reparameterization\n",
            "copying apex/reparameterization/__init__.py -> build/lib.linux-x86_64-3.6/apex/reparameterization\n",
            "copying apex/reparameterization/reparameterization.py -> build/lib.linux-x86_64-3.6/apex/reparameterization\n",
            "creating build/lib.linux-x86_64-3.6/apex/fp16_utils\n",
            "copying apex/fp16_utils/fp16util.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils\n",
            "copying apex/fp16_utils/fp16_optimizer.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils\n",
            "copying apex/fp16_utils/loss_scaler.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils\n",
            "copying apex/fp16_utils/__init__.py -> build/lib.linux-x86_64-3.6/apex/fp16_utils\n",
            "creating build/lib.linux-x86_64-3.6/apex/multi_tensor_apply\n",
            "copying apex/multi_tensor_apply/multi_tensor_apply.py -> build/lib.linux-x86_64-3.6/apex/multi_tensor_apply\n",
            "copying apex/multi_tensor_apply/__init__.py -> build/lib.linux-x86_64-3.6/apex/multi_tensor_apply\n",
            "creating build/lib.linux-x86_64-3.6/apex/amp/lists\n",
            "copying apex/amp/lists/tensor_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists\n",
            "copying apex/amp/lists/torch_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists\n",
            "copying apex/amp/lists/functional_overrides.py -> build/lib.linux-x86_64-3.6/apex/amp/lists\n",
            "copying apex/amp/lists/__init__.py -> build/lib.linux-x86_64-3.6/apex/amp/lists\n",
            "creating build/lib.linux-x86_64-3.6/apex/contrib/groupbn\n",
            "copying apex/contrib/groupbn/batch_norm.py -> build/lib.linux-x86_64-3.6/apex/contrib/groupbn\n",
            "copying apex/contrib/groupbn/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/groupbn\n",
            "creating build/lib.linux-x86_64-3.6/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fused_sgd.py -> build/lib.linux-x86_64-3.6/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fused_adam.py -> build/lib.linux-x86_64-3.6/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/fp16_optimizer.py -> build/lib.linux-x86_64-3.6/apex/contrib/optimizers\n",
            "copying apex/contrib/optimizers/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/optimizers\n",
            "creating build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/self_multihead_attn.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_encdec_multihead_attn_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_encdec_multihead_attn_norm_add_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/encdec_multihead_attn_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_self_multihead_attn_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/fast_self_multihead_attn_norm_add_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/encdec_multihead_attn.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/self_multihead_attn_func.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "copying apex/contrib/multihead_attn/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/multihead_attn\n",
            "creating build/lib.linux-x86_64-3.6/apex/contrib/xentropy\n",
            "copying apex/contrib/xentropy/softmax_xentropy.py -> build/lib.linux-x86_64-3.6/apex/contrib/xentropy\n",
            "copying apex/contrib/xentropy/__init__.py -> build/lib.linux-x86_64-3.6/apex/contrib/xentropy\n",
            "creating build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/blas.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/output.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/data.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/pooling.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/randomSample.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/conv.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/softmax.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/reduction.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/embedding.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/misc.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/recurrentCell.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/loss.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/pointwise.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/index_slice_join_mutate.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/dropout.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/usage.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/normalization.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/prof.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/base.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/optim.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/convert.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/__main__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/linear.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/activation.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "copying apex/pyprof/prof/utility.py -> build/lib.linux-x86_64-3.6/apex/pyprof/prof\n",
            "creating build/lib.linux-x86_64-3.6/apex/pyprof/nvtx\n",
            "copying apex/pyprof/nvtx/nvmarker.py -> build/lib.linux-x86_64-3.6/apex/pyprof/nvtx\n",
            "copying apex/pyprof/nvtx/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/nvtx\n",
            "creating build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/nvvp.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/kernel.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/db.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/__main__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/__init__.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "copying apex/pyprof/parse/parse.py -> build/lib.linux-x86_64-3.6/apex/pyprof/parse\n",
            "running build_ext\n",
            "building 'apex_C' extension\n",
            "creating build/temp.linux-x86_64-3.6\n",
            "creating build/temp.linux-x86_64-3.6/csrc\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/include/python3.6m -c csrc/flatten_unflatten.cpp -o build/temp.linux-x86_64-3.6/csrc/flatten_unflatten.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=apex_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n",
            "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/csrc/flatten_unflatten.o -o build/lib.linux-x86_64-3.6/apex_C.cpython-36m-x86_64-linux-gnu.so\n",
            "building 'amp_C' extension\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/amp_C_frontend.cpp -o build/temp.linux-x86_64-3.6/csrc/amp_C_frontend.o -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_sgd_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_sgd_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_scale_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_scale_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_axpby_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_axpby_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_l2norm_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_l2norm_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_lamb_stage_1.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_1.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_lamb_stage_2.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_2.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_adam.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_adam.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_novograd.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_novograd.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/multi_tensor_lamb.cu -o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -lineinfo -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=amp_C -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/csrc/amp_C_frontend.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_sgd_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_scale_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_axpby_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_l2norm_kernel.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_1.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb_stage_2.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_adam.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_novograd.o build/temp.linux-x86_64-3.6/csrc/multi_tensor_lamb.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/amp_C.cpython-36m-x86_64-linux-gnu.so\n",
            "building 'syncbn' extension\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/syncbn.cpp -o build/temp.linux-x86_64-3.6/csrc/syncbn.o -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=syncbn -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/welford.cu -o build/temp.linux-x86_64-3.6/csrc/welford.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=syncbn -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/csrc/syncbn.o build/temp.linux-x86_64-3.6/csrc/welford.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/syncbn.cpython-36m-x86_64-linux-gnu.so\n",
            "building 'fused_layer_norm_cuda' extension\n",
            "x86_64-linux-gnu-gcc -pthread -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/layer_norm_cuda.cpp -o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda.o -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11\n",
            "/usr/local/cuda/bin/nvcc -I/usr/local/lib/python3.6/dist-packages/torch/include -I/usr/local/lib/python3.6/dist-packages/torch/include/torch/csrc/api/include -I/usr/local/lib/python3.6/dist-packages/torch/include/TH -I/usr/local/lib/python3.6/dist-packages/torch/include/THC -I/usr/local/cuda/include -I/usr/include/python3.6m -c csrc/layer_norm_cuda_kernel.cu -o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -maxrregcount=50 -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_60,code=sm_60 -std=c++11\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "/usr/local/lib/python3.6/dist-packages/torch/include/c10/core/TensorTypeSet.h(44): warning: integer conversion resulted in a change of sign\n",
            "\n",
            "x86_64-linux-gnu-g++ -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda.o build/temp.linux-x86_64-3.6/csrc/layer_norm_cuda_kernel.o -L/usr/local/cuda/lib64 -lcudart -o build/lib.linux-x86_64-3.6/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so\n",
            "creating build/bdist.linux-x86_64\n",
            "creating build/bdist.linux-x86_64/egg\n",
            "copying build/lib.linux-x86_64-3.6/syncbn.cpython-36m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
            "copying build/lib.linux-x86_64-3.6/apex_C.cpython-36m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
            "copying build/lib.linux-x86_64-3.6/fused_layer_norm_cuda.cpython-36m-x86_64-linux-gnu.so -> build/bdist.linux-x86_64/egg\n",
            "creating build/bdist.linux-x86_64/egg/apex\n",
            "creating build/bdist.linux-x86_64/egg/apex/normalization\n",
            "copying build/lib.linux-x86_64-3.6/apex/normalization/fused_layer_norm.py -> build/bdist.linux-x86_64/egg/apex/normalization\n",
            "copying build/lib.linux-x86_64-3.6/apex/normalization/__init__.py -> build/bdist.linux-x86_64/egg/apex/normalization\n",
            "creating build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/handle.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/utils.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/_amp_state.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "creating build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/lists/tensor_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/lists/torch_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/lists/functional_overrides.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/lists/__init__.py -> build/bdist.linux-x86_64/egg/apex/amp/lists\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/_process_optimizer.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/amp.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/opt.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/wrap.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
            "copying build/lib.linux-x86_64-3.6/apex/amp/__version__.py -> build/bdist.linux-x86_64/egg/apex/amp\n",
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            "copying apex.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
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            "creating dist\n",
            "creating 'dist/apex-0.1-py3.6-linux-x86_64.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
            "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
            "Processing apex-0.1-py3.6-linux-x86_64.egg\n",
            "creating /usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6-linux-x86_64.egg\n",
            "Extracting apex-0.1-py3.6-linux-x86_64.egg to /usr/local/lib/python3.6/dist-packages\n",
            "Adding apex 0.1 to easy-install.pth file\n",
            "\n",
            "Installed /usr/local/lib/python3.6/dist-packages/apex-0.1-py3.6-linux-x86_64.egg\n",
            "Processing dependencies for apex==0.1\n",
            "Finished processing dependencies for apex==0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r0fKNCGVbQu1",
        "colab_type": "code",
        "outputId": "5a6f755c-8632-4b0f-b50a-ea130621a0c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "! wget https://github.com/NirantK/hindi2vec/releases/download/bbc-hindi-v0.1/bbc-hindiv01.tar.gz\n",
        "! tar -xvf bbc-hindiv01.tar.gz"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-03 22:25:01--  https://github.com/NirantK/hindi2vec/releases/download/bbc-hindi-v0.1/bbc-hindiv01.tar.gz\n",
            "Resolving github.com (github.com)... 140.82.118.3\n",
            "Connecting to github.com (github.com)|140.82.118.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/123591003/701307f8-3cb5-11e8-9472-df990c204ce8?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200403%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200403T222501Z&X-Amz-Expires=300&X-Amz-Signature=3b36a817c5ec8fd06f3f394a1e8ed12b2aa8369cc5730b8233dafb2a45d35583&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dbbc-hindiv01.tar.gz&response-content-type=application%2Foctet-stream [following]\n",
            "--2020-04-03 22:25:01--  https://github-production-release-asset-2e65be.s3.amazonaws.com/123591003/701307f8-3cb5-11e8-9472-df990c204ce8?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20200403%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200403T222501Z&X-Amz-Expires=300&X-Amz-Signature=3b36a817c5ec8fd06f3f394a1e8ed12b2aa8369cc5730b8233dafb2a45d35583&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dbbc-hindiv01.tar.gz&response-content-type=application%2Foctet-stream\n",
            "Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.217.42.148\n",
            "Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.217.42.148|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11265715 (11M) [application/octet-stream]\n",
            "Saving to: ‘bbc-hindiv01.tar.gz’\n",
            "\n",
            "bbc-hindiv01.tar.gz 100%[===================>]  10.74M  13.0MB/s    in 0.8s    \n",
            "\n",
            "2020-04-03 22:25:03 (13.0 MB/s) - ‘bbc-hindiv01.tar.gz’ saved [11265715/11265715]\n",
            "\n",
            "bbc-hindi-news.json\n",
            "hindi-test.csv\n",
            "hindi-train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JvwpbLMgJiQQ",
        "colab_type": "code",
        "outputId": "0d79febc-b2f3-49c7-e3df-c177487e36b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "! head -n 5 ./hindi-train.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "india\tमेट्रो की इस लाइन के चलने से दक्षिणी दिल्ली से नोएडा जाने का समय काफी कम हो जाएगा और यात्रियों को राजीव चौक या मंडी हाउस से होकर नहीं जाना पड़ेगा.लेकिन, यह मजेंटा लाइन इसलिए भी महत्वपूर्ण है क्योंकि इस पर ड्राइवलैस यानी बिना ड्राइवर वाली मेट्रो चलाने की योजना है. ऐसा भारत में पहली बार होगा जब कोई मेट्रो बिना ड्राइवर के चलाई जाएगी. मेट्रो के तीसरे फेज में भारत में पहली बार ड्राइवरलेस तकनीक आएगी लेकिन दुनिया भर में कई देशों में ड्राइवरलेस मेट्रो पहले से ही चलती हैं. इन देशों में ड्राइवरलेस मेट्रो सफल भी रही हैं. दक्षिण कोरिया की राजधानी सोल में ड्राइवलेस ट्रेन कामयाबी से चल रही है. ये मेट्रो ज़मीन के नीचे चलती है इसमें ड्राइवर का केबिन भी नहीं होता है. यूरोप में डेनमार्क, स्पेन, इटली, फ्रांस, जर्मनी, हंगरी, स्विट्जरलैंड और ब्रिटेन में भी ड्राइवरलैस मेट्रो चलती है. इन देशों में एक से ज़्यादा शहरों में भी ऐसी मेट्रो चलाई जाती है. इनके अलावा अमेरिका और कनाडा में भी ड्राइवरलेस ट्रेन चलती है. वहीं, ब्राजील, पेरू और चाइल में भी इस तरह की मेट्रो काफ़ी पहले आ चुकी है. भारत के पड़ोसी देश चीन में भी ड्राइवरलेस मेट्रो चलती है. अगर आप सऊदी अरब, क़तर और सिंगापुर जाएं तो वहां भी आप बिना ड्राइवर की मेट्रो का मज़ा उठा सकते हैं. क्या है मेट्रो में ख़ास?ड्राइवरलेस मेट्रो जैसा कि नाम से ही पता चला है कि इसे चलाने के लिए ड्राइवर की ज़रूरत नहीं पड़ती है. साथ ही यह मेट्रो किसी रुकावट को पहचानने और आपातकालीन परिस्थितियों में स्वचालित तरीके से काम करती है. इसमें भले ही ड्राइवर न हो लेकिन इसकी हर गतिविधि पर नज़र रखी जाती है. कौन सी ट्रेन कहां है, किस गति से चल रही है और किसे कहां रुकना है ये सब कुछ स्वचालित होता है. भारत की ड्राइवरलेस मेट्रो के बारे में कहा जा रहा है कि इसमें ज़्यादा यात्री सफ़र कर सकेंगे और इसमें ऊर्जा की भी कम खपत होगी. जिन स्टेशनों से होकर ये ट्रेन गुजरेगी उन प्लेटफॉर्म पर स्क्रीन डोर लगे होंगे. ये स्क्रीन डोर सुरक्षा के लिए लगाए गए हैं ताकि प्लेटफॉर्म पर मौजूद यात्री ट्रैक पर न जा सकें. ये दरवाजे तभी खुलेंगे जब मेट्रो प्लेटफॉर्म पर आकर रुकेगी. साथ ही इस बार मेट्रों में कुर्सियों का रंग भी बदलकर संतरी और लाल रखा गया है. हाल ही में कालिंदी कुंज डिपो में एक ड्राइवरलैस मेट्रो के साथ दुर्घटना भी हो गई थी. मेट्रो यार्ड की दीवार तोड़कर बाहर निकल गई थी. पहले इसे ट्रायल रन के दौरान हुआ हादसा बताया गया लेकिन दिल्ली मेट्रो ने इसे मेंटनेंस के बाद हुई दुर्घटना कहा था. इसमें इंसानी ग़लती होने की आशंका जताई थी. हालांकि, इस हादसे में किसी को चोट नहीं पहुंची थी. (बीबीसी हिन्दी के एंड्रॉएड ऐप के लिए आप यहां क्लिक कर सकते हैं. आप हमें फ़ेसबुक और ट्विटर पर फ़ॉलो भी कर सकते हैं.) \r\n",
            "pakistan\tनेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर पर सरकार द्वारा लगाए प्रतिबंधों के समर्थन या विरोध में अपने विचार व्यक्त करते हैं और वेबसाइट संबंधी सूचना भी जारी करते हैं.कुछ दिन पहले ही शियाओं के खिलाफ़ हिंसा पर नज़र रखनेवाली एक प्रतिबंधित वेबसाइट शियाकिलिंग.कॉम को ट्विटर पर चलाए अभियान और सड़कों पर हुए प्रदर्शन के बाद बहाल कर दिया गया था.उससे कुछ दिन पहले जब अहमदिया संप्रदाय की आधिकारिक वेबसाइट अलइस्लाम.ओआरजी को ब्लॉक कर दिया गया था तो ट्विटर पर अहमदिया शब्द काफी प्रचलित हो गया था.पाकिस्तान के संविधान में अहमदिया संप्रदाय के लोगों को मुसलमान नहीं माना जाता है.अतीत में भी शिया, अहमदिया, बलोच और सिंधी जैसे सांप्रदायिक समूहों की वेबसाइटें प्राय: प्रतिबंधित की जाती रही हैं.पाकिस्तान के दूरसंचार प्राधिकरण के प्रमुख का कहना है कि हाल ही में क़रीब 15,756 वेबसाइटों को ब्लॉक कर दिया गया है जिनमें ईशनिंदा और अश्लील सामग्री वाली वेबसाइटें शामिल हैं.पाकिस्तान में ट्विटर, फेसबुक और यू-ट्यूब को भी कुछ समय के लिए ब्लॉक किया गया था.इस साल की शुरुआत में पाकिस्तान सरकार को वेबसाइटों पर नज़र रखने के लिए एक तंत्र विकसित करने के प्रस्ताव को लेकर आलोचना का सामना भी करना पड़ा था.\r\n",
            "news\t\"इसमें एक फ़्लाइट एटेनडेंट की मदद की गुहार है और साथ में डिक चेनी के उस निर्देश का ज़िक्र है जिसमें उन्होंने विमानों को मार गिराने की बात की थी.एक अपहरणकर्ता मोहम्मद अता की धमकियाँ भी सुनी जा सकती हैं. ये ऑडियो 9/11 आयोग के लिए तैयार किया गया था. ज़्यादातर बातें लिखित रूप में प्रकाशित की जा चुकी हैं.रिकॉर्डिंग में अमेरिकन एयरलाइन्स के फ़्लाइट नंबर 11 की फ़्लाइट एटेनडेंट बेटी का फ़ोन कॉल भी है.वे बोल रही हैं,\"\"बिज़नस श्रेणी में किसी को स्टैब किया गया है. पता नहीं, लगता है कि हमारा अपहरण किया जा रहा है.\"\"अपहरणकर्ता मोहम्मद अता को बोलते हुए सना जा सकता है, \"\"कोई हिलेगा नहीं, ऐसे में सब ठीक रहेगा. अगर किसी ने कुछ करने की कोशिश की तो आपको नुकसान हो सकता है और विमान को भी. चुपचाप बैठे रहें.\"\"ज़्यादातर रिकॉर्डिंग फ़ेडरल एविएशन एडमिनिस्ट्रेशन की है. जब विमान वर्ल्ड ट्रेड सेंटर से टकरा चुका था तो उसके बाद ऑडियो में सुना जा सकता है कि कन्ट्रोलर एक और विमान की बात कर रहे हैं जो मैनहेटन जा रहा है.ऑडियो में एक आवाज़ है जो बोल रही है,\"\"एक और विमान अभी-अभी वर्ल्ड ट्रेड सेंटर से टकराया है.\"\" फिर कोई कहता है,\"\" पूरी इमारत बिखर गई है.\"\"अफ़रा-तफ़री के आलम में जब कुछ फ़ाइटर जेट पूर्व की ओर उड़ान भरते हैं तो एक सैन्य कमांडर उन्हें जल्द से जल्द वाशिंगटन आने का आदेश देते हुए सुने जा सकते हैं.आख़िरी विमान यूनाइटेड 93 के पेनसल्वेनिया में गिरने से पहले मेजर केविन ये कहते हुए सुने जा सकते हैं, \"\"मुझे इस बात की परवाह नहीं कि आप कितनी खिड़कियाँ तोड़ देते हैं.\"\"\"\r\n",
            "india\t\"प्रतीक खुलेपन का, आज़ाद ख्याली का और भीड़ से अलग हो जाने का.सवा सौ साल के इतिहास वाले इस मोटरसाइकिल ब्रांड ने दोनों विश्व युद्ध का \r\r\n",
            "\r\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBxAA3FNL_2c",
        "colab_type": "code",
        "outputId": "56c802eb-383c-40c0-c5da-0e97f4941aca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "import pandas as pd \n",
        "\n",
        "df_train = pd.read_csv(\"./hindi-train.csv\", header=0, sep=\"\\t\", names=['topic', 'text'])\n",
        "print(df_train.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      topic                                               text\n",
            "0  pakistan  नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर...\n",
            "1      news  इसमें एक फ़्लाइट एटेनडेंट की मदद की गुहार है औ...\n",
            "2     india  प्रतीक खुलेपन का, आज़ाद ख्याली का और भीड़ से अ...\n",
            "3     india  ख़ासकर पिछले 10 साल तक प्रधानमंत्री रहे मनमोहन...\n",
            "4     india  चीनी भाषा में ‘नी-हाव’ (क्या हाल हैं?) कहकर वो...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmllbmrlYaHM",
        "colab_type": "code",
        "outputId": "770e9cbe-e53b-4d7d-ac7f-9d3a95ef8b44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "print(set(df_train[\"topic\"].values))\n",
        "print(len(set(df_train[\"topic\"].values)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'southasia', 'international', 'learningenglish', 'institutional', 'india', 'news', 'pakistan', 'multimedia', 'social', 'china', 'entertainment', 'science', 'business', 'sport'}\n",
            "14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFCEz905cl94",
        "colab_type": "code",
        "outputId": "c422cbd2-f0c3-45aa-8abc-7c12896b0448",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "from sklearn import preprocessing\n",
        "labeler = preprocessing.LabelEncoder()\n",
        "\n",
        "df2 = pd.DataFrame(df_train[\"text\"])\n",
        "df2 = df2.applymap(str)\n",
        "df2['label'] = labeler.fit_transform(df_train[\"topic\"])\n",
        "print(df2.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                                text  label\n",
            "0  नेटिजन यानि इंटरनेट पर सक्रिय नागरिक अब ट्विटर...      9\n",
            "1  इसमें एक फ़्लाइट एटेनडेंट की मदद की गुहार है औ...      8\n",
            "2  प्रतीक खुलेपन का, आज़ाद ख्याली का और भीड़ से अ...      3\n",
            "3  ख़ासकर पिछले 10 साल तक प्रधानमंत्री रहे मनमोहन...      3\n",
            "4  चीनी भाषा में ‘नी-हाव’ (क्या हाल हैं?) कहकर वो...      3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8UXEFknQ8j4",
        "colab_type": "code",
        "outputId": "75b74c64-d3f9-478f-8e2f-a4d136124701",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748,
          "referenced_widgets": [
            "586d68f182a04610ad13117490c07381",
            "b953fe27442a464789d0f821d10ffce7",
            "49da171041c64d93a497825a08d8e6e1",
            "82826da6004943e5a74ca83684a5a08d",
            "62c0e161de2f444c8c64d915e59efdec",
            "81f04db998d3470ab06c7beafaae4977",
            "412e1a71a6d845f0a186a112402426c2",
            "dbf1dcbb1e5942fcb666cc9de1f16d39",
            "6f60da15d02444c2b14a6a3062bb6342",
            "7dbb3f05d6e147dc80e9531caa7de048",
            "42fc72119d4c4490b42f6b1e45edd0e3",
            "63c7767769824fe8bc72c207f346a78f",
            "10560cc10d9d43ebbeab517273bcbb33",
            "7aac654789a643c19e98c01c22f6e846",
            "849f2f548ddc4d22aa4152b4d5a60e6e",
            "801b5e5c2de24886832a03830a1e172e",
            "a0634c4f8dc34376a6aa5a8d7d50cf9b",
            "f6b38f64e3f44ae2aad99d1a08af4b0f",
            "896780625d5d499bbc47580fab7556bb",
            "987781831adb461ebb2e4aaec6e1f683",
            "44518e4cc21a40059b7caab397078480",
            "bba4123e984f4772a8a7a77ecb4dc867",
            "7b841b4aefc448188c7cf39ff81cb51c",
            "637dbbff8eed4201ba0ca04cc388ec81",
            "04599f0ab3e944e6b931ab5dd24a5139",
            "09b98c56544a4686888d701de4f8d8bd",
            "9722c4f6646341619c18e39c9d803991",
            "8c8869e729a248dca3535bbe3d283851",
            "9971ab49cb4f4254ba96642e943f3a61",
            "cd24397ad0bb41ca85cf60d4b5534e37",
            "195a79f1f5f0482ea3a73b332dbdd583",
            "2f811d96574b4342bb6b7f2dc4cec5af",
            "f376f1f7330f48028b4a5e749d01f7ef",
            "ffd2014545c043f4a49d2b071182f690",
            "eed79c269eba4511adda860f52563444",
            "e87ea6458a1941fb917ee861c1f93399",
            "f0a80ba8edae44289cb0fe9e8eaedd4c",
            "d6b6e708c655481aa2674c47579acd9c",
            "b5e21b4851c0404a9eb6bfd68657c8d7",
            "15b6fbdd55774d23af86ca6839964274"
          ]
        }
      },
      "source": [
        "from simpletransformers.classification import ClassificationModel\n",
        "\n",
        "# set use_cuda=False on CPU-only platforms\n",
        "model = ClassificationModel('bert', 'bert-base-multilingual-uncased', num_labels=14, use_cuda=True, args={\n",
        "    'reprocess_input_data': True,\n",
        "    'use_cached_eval_features': False,\n",
        "    'overwrite_output_dir': True,\n",
        "    'num_train_epochs': 3\n",
        "})\n",
        "model.train_model(df2)\n",
        "print(\"trained model\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:250: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "586d68f182a04610ad13117490c07381",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=3467), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.\n",
            "\n",
            "Defaults for this optimization level are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n",
            "Processing user overrides (additional kwargs that are not None)...\n",
            "After processing overrides, optimization options are:\n",
            "enabled                : True\n",
            "opt_level              : O1\n",
            "cast_model_type        : None\n",
            "patch_torch_functions  : True\n",
            "keep_batchnorm_fp32    : None\n",
            "master_weights         : None\n",
            "loss_scale             : dynamic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f60da15d02444c2b14a6a3062bb6342",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Epoch', max=3, style=ProgressStyle(description_width='initial…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a0634c4f8dc34376a6aa5a8d7d50cf9b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=434, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 2.657103Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 32768.0\n",
            "Running loss: 2.638085"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:113: UserWarning: Seems like `optimizer.step()` has been overridden after learning rate scheduler initialization. Please, make sure to call `optimizer.step()` before `lr_scheduler.step()`. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 1.123532"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`.\n",
            "  warnings.warn(\"To get the last learning rate computed by the scheduler, \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 1.700933Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 16384.0\n",
            "Running loss: 2.297620Gradient overflow.  Skipping step, loss scaler 0 reducing loss scale to 8192.0\n",
            "Running loss: 0.925745\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04599f0ab3e944e6b931ab5dd24a5139",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=434, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 0.617821\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f376f1f7330f48028b4a5e749d01f7ef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Current iteration', max=434, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Running loss: 0.128591\n",
            "\n",
            "trained model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Qco4_PoYi50",
        "colab_type": "code",
        "outputId": "c5843bd0-44f0-4c10-ad0f-059ce5057134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        }
      },
      "source": [
        "orig_test = pd.read_csv(\"./hindi-test.csv\", header=0, sep=\"\\t\", names=['topic', 'text'])\n",
        "eval_frame = pd.DataFrame(orig_test[\"text\"])\n",
        "eval_frame = eval_frame.applymap(str)\n",
        "eval_frame['label'] = labeler.fit_transform(orig_test[\"topic\"])\n",
        "print(eval_frame.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                                                text  label\n",
            "0  लखनऊ स्थित पत्रकार समीरात्मज मिश्र को बुलंदशहर...      3\n",
            "1  लगभग 1300 हेक्टेयर ज़मीन का अधिग्रहण किया जा च...      3\n",
            "2  हालांकि उनके अंगरक्षकों को बमों को जाम करने वा...      5\n",
            "3  आयोग का कहना है कि इस तरह के परीक्षण से महिलाओ...      3\n",
            "4  वह कटी हुई भिंडियां कड़ाही में डाल देती है. चू...      3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qkc7k9iHZRPK",
        "colab_type": "code",
        "outputId": "03cd71ba-3b6e-4b94-e87e-5a94fd6b447d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "print(len(eval_frame))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "866\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bKdqe_dYm5m",
        "colab_type": "code",
        "outputId": "96a71d3a-2f13-4a16-91cf-3cfa0a8cbb9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198,
          "referenced_widgets": [
            "9a1b1d0839824c45ba6f3e1628ec3662",
            "497a54bde8034e1397b3c672ced57caf",
            "5e99506bb0504776bbb09ae9903da948",
            "398f954087e54cea90f454b3cc490253",
            "08a37fd719cd419ab36ae1810caa2d0f",
            "73507db14533415fae4e715a6b6d5666",
            "a736064fd9204d0d878fde97b89ef431",
            "d7f354db7ad342469b4d3e589b0dfba2",
            "abfd9f4b3a014f15ad029fdf71732fef",
            "f1583d4cab3b41158e7269e36f7150de",
            "05447a10ad4947d5bb9455012c15f8aa",
            "0c66307d97bd4ccc9056405d6211d6bd",
            "8e2ef59af1ac4cae8d1cc0e412160540",
            "32d4e0b87f94408ca82ca2201791576a",
            "962432d03278478c842f38f137ae9631",
            "bd8c71526d4944598e280282a62da383"
          ]
        }
      },
      "source": [
        "result, model_outputs, wrong_predictions = model.eval_model(eval_frame)\n",
        "bads = {}\n",
        "for pred in wrong_predictions:\n",
        "    if pred.label in bads:\n",
        "        bads[pred.label] += 1\n",
        "    else:\n",
        "        bads[pred.label] = 1\n",
        "print(\"wrong predictions (by input I think?):\")\n",
        "print(bads)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/simpletransformers/classification/classification_model.py:612: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
            "  \"Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a1b1d0839824c45ba6f3e1628ec3662",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=866), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "abfd9f4b3a014f15ad029fdf71732fef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, max=109), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "wrong predictions (by input I think?):\n",
            "{3: 74, 2: 23, 12: 10, 5: 69, 4: 4, 13: 20, 8: 49, 10: 25, 6: 3, 11: 6, 0: 7, 9: 8, 1: 5, 7: 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLGONGR0Ye4P",
        "colab_type": "code",
        "outputId": "0d5ac0eb-7043-4458-a218-05b1748d0277",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "(1 - (74 + 23 + 10 + 69 + 4 + 20 + 49 + 25 + 3 + 6 + 7 + 8 + 5 + 1) / 866) * 100"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "64.89607390300232"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hv1bhXGCigkY",
        "colab_type": "text"
      },
      "source": [
        "64.9% Accuracy, 3 epochs, SimpleTransformers + Multilingual BERT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTqFLdp8Wl7X",
        "colab_type": "code",
        "outputId": "eaff286e-698a-4132-da14-9459f056d8a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        }
      },
      "source": [
        "! pip install tensorflow==1.15"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==1.15\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3f/98/5a99af92fb911d7a88a0005ad55005f35b4c1ba8d75fba02df726cd936e6/tensorflow-1.15.0-cp36-cp36m-manylinux2010_x86_64.whl (412.3MB)\n",
            "\u001b[K     |████████████████████████████████| 412.3MB 17kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.18.2)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.0.8)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.34.2)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.8.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.1.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.12.0)\n",
            "Collecting tensorflow-estimator==1.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
            "\u001b[K     |████████████████████████████████| 512kB 39.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.12.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.27.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (3.2.0)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.2.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (0.9.0)\n",
            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 37.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15) (3.10.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15) (2.10.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15) (3.2.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15) (46.0.0)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7540 sha256=41bf33108f16316da14f0adb2427ab4e35472a69fa687617cfffdf99c2337eba\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "Successfully built gast\n",
            "Installing collected packages: tensorflow-estimator, gast, tensorboard, tensorflow\n",
            "  Found existing installation: tensorflow-estimator 2.2.0rc0\n",
            "    Uninstalling tensorflow-estimator-2.2.0rc0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0rc0\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorboard 2.2.0\n",
            "    Uninstalling tensorboard-2.2.0:\n",
            "      Successfully uninstalled tensorboard-2.2.0\n",
            "  Found existing installation: tensorflow 2.2.0rc2\n",
            "    Uninstalling tensorflow-2.2.0rc2:\n",
            "      Successfully uninstalled tensorflow-2.2.0rc2\n",
            "Successfully installed gast-0.2.2 tensorboard-1.15.0 tensorflow-1.15.0 tensorflow-estimator-1.15.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gast",
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T34iLaWMYpEM",
        "colab_type": "code",
        "outputId": "b3e1bf89-e584-47c0-a531-b7c60f344699",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "! rm -rf electra\n",
        "! git clone https://github.com/MonsoonNLP/electra"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'electra'...\n",
            "remote: Enumerating objects: 22, done.\u001b[K\n",
            "remote: Counting objects: 100% (22/22), done.\u001b[K\n",
            "remote: Compressing objects: 100% (13/13), done.\u001b[K\n",
            "remote: Total 103 (delta 13), reused 18 (delta 9), pack-reused 81\u001b[K\n",
            "Receiving objects: 100% (103/103), 95.19 KiB | 580.00 KiB/s, done.\n",
            "Resolving deltas: 100% (46/46), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wAyBpK2nZpg_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! mkdir ./drive/My\\ Drive/mlin/hindicorpus/trainer/finetuning_data/bbc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ni3ONet2Y8yk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! cp hindi-train.csv ./drive/My\\ Drive/mlin/hindicorpus/trainer/finetuning_data/bbc/train.csv\n",
        "! cp hindi-test.csv ./drive/My\\ Drive/mlin/hindicorpus/trainer/finetuning_data/bbc/dev.csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJQ50tkwYuz5",
        "colab_type": "code",
        "outputId": "9578868d-0140-4282-a930-6e04086566c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! cd electra && python run_finetuning.py --data-dir ../drive/My\\ Drive/mlin/hindicorpus/trainer --model-name babylil --hparams '{\"task_names\": [\"bbc\"], \"num_train_epochs\": 10}'"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "================================================================================\n",
            "Config: model=babylil, trial 1/1\n",
            "================================================================================\n",
            "answerable_classifier True\n",
            "answerable_uses_start_logits True\n",
            "answerable_weight 0.5\n",
            "beam_size 20\n",
            "data_dir ../drive/My Drive/mlin/hindicorpus/trainer\n",
            "debug False\n",
            "do_eval True\n",
            "do_lower_case True\n",
            "do_train True\n",
            "doc_stride 128\n",
            "double_unordered True\n",
            "embedding_size 128\n",
            "eval_batch_size 32\n",
            "gcp_project None\n",
            "init_checkpoint ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil\n",
            "iterations_per_loop 1000\n",
            "joint_prediction True\n",
            "keep_all_models True\n",
            "layerwise_lr_decay 0.8\n",
            "learning_rate 0.0001\n",
            "log_examples False\n",
            "max_answer_length 30\n",
            "max_query_length 64\n",
            "max_seq_length 128\n",
            "model_dir ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil/finetuning_models/bbc_model\n",
            "model_hparam_overrides {}\n",
            "model_name babylil\n",
            "model_size small\n",
            "n_best_size 20\n",
            "n_writes_test 5\n",
            "num_tpu_cores 1\n",
            "num_train_epochs 10\n",
            "num_trials 1\n",
            "predict_batch_size 32\n",
            "preprocessed_data_dir ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil/finetuning_tfrecords/bbc_tfrecords\n",
            "qa_eval_file <built-in method format of str object at 0x7f55a887d7a0>\n",
            "qa_na_file <built-in method format of str object at 0x7f55a887c270>\n",
            "qa_na_threshold -2.75\n",
            "qa_preds_file <built-in method format of str object at 0x7f55a887d828>\n",
            "raw_data_dir <built-in method format of str object at 0x7f55a885fab0>\n",
            "results_pkl ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil/results/bbc_results.pkl\n",
            "results_txt ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil/results/bbc_results.txt\n",
            "save_checkpoints_steps 1000000\n",
            "task_names ['bbc']\n",
            "test_predictions <built-in method format of str object at 0x7f55a9bb7dd8>\n",
            "tpu_job_name None\n",
            "tpu_name None\n",
            "tpu_zone None\n",
            "train_batch_size 32\n",
            "use_tfrecords_if_existing True\n",
            "use_tpu False\n",
            "vocab_file ../drive/My Drive/mlin/hindicorpus/trainer/vocab.txt\n",
            "vocab_size 30522\n",
            "warmup_proportion 0.1\n",
            "weight_decay_rate 0.01\n",
            "write_test_outputs False\n",
            "\n",
            "Loading dataset bbc_train\n",
            "================================================================================\n",
            "Start training: model=babylil, trial 1/1\n",
            "================================================================================\n",
            "Training for 1090 steps\n",
            "Building model...\n",
            "Building complete\n",
            "2020-04-03 23:13:43.159838: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
            "2020-04-03 23:13:43.165073: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2020-04-03 23:13:43.165309: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1b25800 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-03 23:13:43.165345: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-04-03 23:13:43.167460: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-04-03 23:13:43.276380: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.277429: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1b259c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-04-03 23:13:43.277465: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-04-03 23:13:43.277679: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.278570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-04-03 23:13:43.278955: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2020-04-03 23:13:43.280349: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2020-04-03 23:13:43.281598: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2020-04-03 23:13:43.281993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2020-04-03 23:13:43.283534: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2020-04-03 23:13:43.284673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2020-04-03 23:13:43.288295: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-03 23:13:43.288453: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.289421: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.290273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-04-03 23:13:43.290355: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2020-04-03 23:13:43.291950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-03 23:13:43.291984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-04-03 23:13:43.292000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-04-03 23:13:43.292167: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.293111: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:13:43.293969: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-04-03 23:13:43.294021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "2020-04-03 23:14:09.057161: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "10/1090 = 0.9%, SPS: 0.8, ELAP: 12, ETA: 21:27 - loss: 106.3752\n",
            "20/1090 = 1.8%, SPS: 1.5, ELAP: 13, ETA: 11:39 - loss: 60.4105\n",
            "30/1090 = 2.8%, SPS: 2.1, ELAP: 14, ETA: 8:22 - loss: 60.7150\n",
            "40/1090 = 3.7%, SPS: 2.6, ELAP: 15, ETA: 6:43 - loss: 60.5596\n",
            "50/1090 = 4.6%, SPS: 3.0, ELAP: 17, ETA: 5:43 - loss: 54.9625\n",
            "60/1090 = 5.5%, SPS: 3.4, ELAP: 18, ETA: 5:03 - loss: 50.4303\n",
            "70/1090 = 6.4%, SPS: 3.7, ELAP: 19, ETA: 4:34 - loss: 56.7001\n",
            "80/1090 = 7.3%, SPS: 4.0, ELAP: 20, ETA: 4:12 - loss: 45.8699\n",
            "90/1090 = 8.3%, SPS: 4.3, ELAP: 21, ETA: 3:54 - loss: 67.6048\n",
            "100/1090 = 9.2%, SPS: 4.5, ELAP: 22, ETA: 3:40 - loss: 53.2900\n",
            "110/1090 = 10.1%, SPS: 4.7, ELAP: 23, ETA: 3:28 - loss: 70.7242\n",
            "120/1090 = 11.0%, SPS: 4.9, ELAP: 25, ETA: 3:18 - loss: 55.0860\n",
            "130/1090 = 11.9%, SPS: 5.1, ELAP: 26, ETA: 3:10 - loss: 44.9913\n",
            "140/1090 = 12.8%, SPS: 5.2, ELAP: 27, ETA: 3:02 - loss: 53.5939\n",
            "150/1090 = 13.8%, SPS: 5.4, ELAP: 28, ETA: 2:55 - loss: 59.2980\n",
            "160/1090 = 14.7%, SPS: 5.5, ELAP: 29, ETA: 2:49 - loss: 37.1402\n",
            "170/1090 = 15.6%, SPS: 5.6, ELAP: 30, ETA: 2:44 - loss: 44.7313\n",
            "180/1090 = 16.5%, SPS: 5.7, ELAP: 31, ETA: 2:39 - loss: 47.0172\n",
            "190/1090 = 17.4%, SPS: 5.8, ELAP: 33, ETA: 2:34 - loss: 57.2359\n",
            "200/1090 = 18.3%, SPS: 5.9, ELAP: 34, ETA: 2:30 - loss: 46.2623\n",
            "210/1090 = 19.3%, SPS: 6.0, ELAP: 35, ETA: 2:26 - loss: 41.1586\n",
            "220/1090 = 20.2%, SPS: 6.1, ELAP: 36, ETA: 2:22 - loss: 36.5462\n",
            "230/1090 = 21.1%, SPS: 6.2, ELAP: 37, ETA: 2:19 - loss: 42.3173\n",
            "240/1090 = 22.0%, SPS: 6.3, ELAP: 38, ETA: 2:16 - loss: 32.9638\n",
            "250/1090 = 22.9%, SPS: 6.3, ELAP: 39, ETA: 2:13 - loss: 33.7127\n",
            "260/1090 = 23.9%, SPS: 6.4, ELAP: 41, ETA: 2:10 - loss: 52.6548\n",
            "270/1090 = 24.8%, SPS: 6.5, ELAP: 42, ETA: 2:07 - loss: 32.8576\n",
            "280/1090 = 25.7%, SPS: 6.5, ELAP: 43, ETA: 2:04 - loss: 38.7631\n",
            "290/1090 = 26.6%, SPS: 6.6, ELAP: 44, ETA: 2:01 - loss: 38.5996\n",
            "300/1090 = 27.5%, SPS: 6.6, ELAP: 45, ETA: 1:59 - loss: 36.5342\n",
            "310/1090 = 28.4%, SPS: 6.7, ELAP: 46, ETA: 1:57 - loss: 43.4187\n",
            "320/1090 = 29.4%, SPS: 6.7, ELAP: 47, ETA: 1:54 - loss: 39.0296\n",
            "330/1090 = 30.3%, SPS: 6.8, ELAP: 49, ETA: 1:52 - loss: 29.1170\n",
            "340/1090 = 31.2%, SPS: 6.8, ELAP: 50, ETA: 1:50 - loss: 30.8255\n",
            "350/1090 = 32.1%, SPS: 6.9, ELAP: 51, ETA: 1:48 - loss: 29.8319\n",
            "360/1090 = 33.0%, SPS: 6.9, ELAP: 52, ETA: 1:46 - loss: 31.1690\n",
            "370/1090 = 33.9%, SPS: 7.0, ELAP: 53, ETA: 1:44 - loss: 37.2806\n",
            "380/1090 = 34.9%, SPS: 7.0, ELAP: 54, ETA: 1:42 - loss: 33.1193\n",
            "390/1090 = 35.8%, SPS: 7.0, ELAP: 56, ETA: 1:40 - loss: 36.2587\n",
            "400/1090 = 36.7%, SPS: 7.1, ELAP: 57, ETA: 1:38 - loss: 39.4412\n",
            "410/1090 = 37.6%, SPS: 7.1, ELAP: 58, ETA: 1:36 - loss: 27.9696\n",
            "420/1090 = 38.5%, SPS: 7.1, ELAP: 59, ETA: 1:34 - loss: 33.8698\n",
            "430/1090 = 39.4%, SPS: 7.2, ELAP: 1:00, ETA: 1:32 - loss: 27.8437\n",
            "440/1090 = 40.4%, SPS: 7.2, ELAP: 1:01, ETA: 1:30 - loss: 26.3857\n",
            "450/1090 = 41.3%, SPS: 7.2, ELAP: 1:02, ETA: 1:29 - loss: 32.6587\n",
            "460/1090 = 42.2%, SPS: 7.2, ELAP: 1:04, ETA: 1:27 - loss: 42.6338\n",
            "470/1090 = 43.1%, SPS: 7.3, ELAP: 1:05, ETA: 1:25 - loss: 32.4096\n",
            "480/1090 = 44.0%, SPS: 7.3, ELAP: 1:06, ETA: 1:24 - loss: 27.8784\n",
            "490/1090 = 45.0%, SPS: 7.3, ELAP: 1:07, ETA: 1:22 - loss: 26.7534\n",
            "500/1090 = 45.9%, SPS: 7.3, ELAP: 1:08, ETA: 1:20 - loss: 38.0546\n",
            "510/1090 = 46.8%, SPS: 7.4, ELAP: 1:09, ETA: 1:19 - loss: 31.7880\n",
            "520/1090 = 47.7%, SPS: 7.4, ELAP: 1:10, ETA: 1:17 - loss: 33.9299\n",
            "530/1090 = 48.6%, SPS: 7.4, ELAP: 1:12, ETA: 1:16 - loss: 39.0723\n",
            "540/1090 = 49.5%, SPS: 7.4, ELAP: 1:13, ETA: 1:14 - loss: 24.2173\n",
            "550/1090 = 50.5%, SPS: 7.4, ELAP: 1:14, ETA: 1:13 - loss: 29.5776\n",
            "560/1090 = 51.4%, SPS: 7.5, ELAP: 1:15, ETA: 1:11 - loss: 22.4268\n",
            "570/1090 = 52.3%, SPS: 7.5, ELAP: 1:16, ETA: 1:10 - loss: 24.9106\n",
            "580/1090 = 53.2%, SPS: 7.5, ELAP: 1:17, ETA: 1:08 - loss: 41.7821\n",
            "590/1090 = 54.1%, SPS: 7.5, ELAP: 1:19, ETA: 1:07 - loss: 29.2495\n",
            "600/1090 = 55.0%, SPS: 7.5, ELAP: 1:20, ETA: 1:05 - loss: 20.5626\n",
            "610/1090 = 56.0%, SPS: 7.5, ELAP: 1:21, ETA: 1:04 - loss: 36.2406\n",
            "620/1090 = 56.9%, SPS: 7.6, ELAP: 1:22, ETA: 1:02 - loss: 25.7795\n",
            "630/1090 = 57.8%, SPS: 7.6, ELAP: 1:23, ETA: 1:01 - loss: 34.0136\n",
            "640/1090 = 58.7%, SPS: 7.6, ELAP: 1:24, ETA: 59 - loss: 20.1298\n",
            "650/1090 = 59.6%, SPS: 7.6, ELAP: 1:25, ETA: 58 - loss: 25.0697\n",
            "660/1090 = 60.6%, SPS: 7.6, ELAP: 1:27, ETA: 56 - loss: 25.5739\n",
            "670/1090 = 61.5%, SPS: 7.6, ELAP: 1:28, ETA: 55 - loss: 19.4230\n",
            "680/1090 = 62.4%, SPS: 7.7, ELAP: 1:29, ETA: 54 - loss: 41.0632\n",
            "690/1090 = 63.3%, SPS: 7.7, ELAP: 1:30, ETA: 52 - loss: 44.5441\n",
            "700/1090 = 64.2%, SPS: 7.7, ELAP: 1:31, ETA: 51 - loss: 34.1567\n",
            "710/1090 = 65.1%, SPS: 7.7, ELAP: 1:32, ETA: 49 - loss: 23.5240\n",
            "720/1090 = 66.1%, SPS: 7.7, ELAP: 1:33, ETA: 48 - loss: 30.5253\n",
            "730/1090 = 67.0%, SPS: 7.7, ELAP: 1:35, ETA: 47 - loss: 24.7404\n",
            "740/1090 = 67.9%, SPS: 7.7, ELAP: 1:36, ETA: 45 - loss: 17.9688\n",
            "750/1090 = 68.8%, SPS: 7.7, ELAP: 1:37, ETA: 44 - loss: 18.2113\n",
            "760/1090 = 69.7%, SPS: 7.8, ELAP: 1:38, ETA: 43 - loss: 31.5788\n",
            "770/1090 = 70.6%, SPS: 7.8, ELAP: 1:39, ETA: 41 - loss: 21.1902\n",
            "780/1090 = 71.6%, SPS: 7.8, ELAP: 1:40, ETA: 40 - loss: 27.0739\n",
            "790/1090 = 72.5%, SPS: 7.8, ELAP: 1:41, ETA: 39 - loss: 40.7702\n",
            "800/1090 = 73.4%, SPS: 7.8, ELAP: 1:43, ETA: 37 - loss: 34.1421\n",
            "810/1090 = 74.3%, SPS: 7.8, ELAP: 1:44, ETA: 36 - loss: 27.5100\n",
            "820/1090 = 75.2%, SPS: 7.8, ELAP: 1:45, ETA: 35 - loss: 21.9963\n",
            "830/1090 = 76.1%, SPS: 7.8, ELAP: 1:46, ETA: 33 - loss: 26.4111\n",
            "840/1090 = 77.1%, SPS: 7.8, ELAP: 1:47, ETA: 32 - loss: 29.0017\n",
            "850/1090 = 78.0%, SPS: 7.8, ELAP: 1:48, ETA: 31 - loss: 29.3484\n",
            "860/1090 = 78.9%, SPS: 7.9, ELAP: 1:49, ETA: 29 - loss: 27.1212\n",
            "870/1090 = 79.8%, SPS: 7.9, ELAP: 1:51, ETA: 28 - loss: 20.1221\n",
            "880/1090 = 80.7%, SPS: 7.9, ELAP: 1:52, ETA: 27 - loss: 29.1447\n",
            "890/1090 = 81.7%, SPS: 7.9, ELAP: 1:53, ETA: 25 - loss: 35.5786\n",
            "900/1090 = 82.6%, SPS: 7.9, ELAP: 1:54, ETA: 24 - loss: 25.2032\n",
            "910/1090 = 83.5%, SPS: 7.9, ELAP: 1:55, ETA: 23 - loss: 29.3058\n",
            "920/1090 = 84.4%, SPS: 7.9, ELAP: 1:56, ETA: 22 - loss: 25.7716\n",
            "930/1090 = 85.3%, SPS: 7.9, ELAP: 1:58, ETA: 20 - loss: 27.4794\n",
            "940/1090 = 86.2%, SPS: 7.9, ELAP: 1:59, ETA: 19 - loss: 27.4442\n",
            "950/1090 = 87.2%, SPS: 7.9, ELAP: 2:00, ETA: 18 - loss: 27.5786\n",
            "960/1090 = 88.1%, SPS: 7.9, ELAP: 2:01, ETA: 16 - loss: 18.1137\n",
            "970/1090 = 89.0%, SPS: 7.9, ELAP: 2:02, ETA: 15 - loss: 24.0730\n",
            "980/1090 = 89.9%, SPS: 8.0, ELAP: 2:03, ETA: 14 - loss: 22.6597\n",
            "990/1090 = 90.8%, SPS: 8.0, ELAP: 2:04, ETA: 13 - loss: 37.3418\n",
            "1000/1090 = 91.7%, SPS: 8.0, ELAP: 2:06, ETA: 11 - loss: 22.1092\n",
            "1010/1090 = 92.7%, SPS: 8.0, ELAP: 2:07, ETA: 10 - loss: 44.8285\n",
            "1020/1090 = 93.6%, SPS: 8.0, ELAP: 2:08, ETA: 9 - loss: 34.2528\n",
            "1030/1090 = 94.5%, SPS: 8.0, ELAP: 2:09, ETA: 8 - loss: 21.6028\n",
            "1040/1090 = 95.4%, SPS: 8.0, ELAP: 2:10, ETA: 6 - loss: 16.0057\n",
            "1050/1090 = 96.3%, SPS: 8.0, ELAP: 2:11, ETA: 5 - loss: 29.7091\n",
            "1060/1090 = 97.2%, SPS: 8.0, ELAP: 2:12, ETA: 4 - loss: 24.6724\n",
            "1070/1090 = 98.2%, SPS: 8.0, ELAP: 2:14, ETA: 2 - loss: 49.0791\n",
            "1080/1090 = 99.1%, SPS: 8.0, ELAP: 2:15, ETA: 1 - loss: 41.1758\n",
            "1090/1090 = 100.0%, SPS: 8.0, ELAP: 2:16, ETA: 0 - loss: 20.8336\n",
            "1090/1090 = 100.0%, SPS: 7.9, ELAP: 2:17, ETA: 0\n",
            "\n",
            "================================================================================\n",
            "Run dev set evaluation: model=babylil, trial 1/1\n",
            "================================================================================\n",
            "Evaluating bbc\n",
            "Loading dataset bbc_dev\n",
            "Building model...\n",
            "Building complete\n",
            "2020-04-03 23:16:24.986610: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:16:24.987497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-04-03 23:16:24.987630: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0\n",
            "2020-04-03 23:16:24.987681: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0\n",
            "2020-04-03 23:16:24.987726: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0\n",
            "2020-04-03 23:16:24.987768: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0\n",
            "2020-04-03 23:16:24.987845: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0\n",
            "2020-04-03 23:16:24.987894: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0\n",
            "2020-04-03 23:16:24.987940: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-04-03 23:16:24.988070: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:16:24.988893: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:16:24.989576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-04-03 23:16:24.989644: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-04-03 23:16:24.989673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-04-03 23:16:24.989693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-04-03 23:16:24.989928: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:16:24.990925: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-04-03 23:16:24.991633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 15216 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "bbc: accuracy: 64.32 - loss: 1.17\n",
            "\n",
            "Writing results to ../drive/My Drive/mlin/hindicorpus/trainer/models/babylil/results/bbc_results.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RW16_5sR2KB",
        "colab_type": "text"
      },
      "source": [
        "58.3% accuracy on 3 epochs, 64.7% on 9 epochs"
      ]
    }
  ]
}