diff --git a/Reconstruct_and_RoBERTa_baseline_train_dev_dataset.ipynb b/Reconstruct_and_RoBERTa_baseline_train_dev_dataset.ipynb
index 3ad02ccff44092be88f86a6b113a0b3ecee4ded4..90edad574c7b9389991cd11dce7d30b13826cbf7 100644
--- a/Reconstruct_and_RoBERTa_baseline_train_dev_dataset.ipynb
+++ b/Reconstruct_and_RoBERTa_baseline_train_dev_dataset.ipynb
@@ -1,8440 +1,8486 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "H08esTFOYO99"
-      },
-      "source": [
-        "# Main imports and code"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "EnHQoayhBYlm",
-        "outputId": "eb747576-9a2a-474c-dbc7-4d6c042f68e6"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Sat Feb 26 13:12:45 2022       \n",
-            "+-----------------------------------------------------------------------------+\n",
-            "| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.4     |\n",
-            "|-------------------------------+----------------------+----------------------+\n",
-            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
-            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
-            "|                               |                      |               MIG M. |\n",
-            "|===============================+======================+======================|\n",
-            "|   0  Quadro P4000        Off  | 00000000:00:05.0 Off |                  N/A |\n",
-            "| 46%   33C    P8     5W / 105W |      0MiB /  8119MiB |      0%      Default |\n",
-            "|                               |                      |                  N/A |\n",
-            "+-------------------------------+----------------------+----------------------+\n",
-            "                                                                               \n",
-            "+-----------------------------------------------------------------------------+\n",
-            "| Processes:                                                                  |\n",
-            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
-            "|        ID   ID                                                   Usage      |\n",
-            "|=============================================================================|\n",
-            "|  No running processes found                                                 |\n",
-            "+-----------------------------------------------------------------------------+\n",
-            "WARNING: infoROM is corrupted at gpu 0000:00:05.0\n"
-          ]
-        }
-      ],
-      "source": [
-        "# check which gpu we're using\n",
-        "!nvidia-smi"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 1000
-        },
-        "id": "hYhFR7nSYOjG",
-        "outputId": "23ed0686-29d3-45ff-dc22-b2fe54e86ec4"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
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-            "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (21.1.0)\n",
-            "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.11.0)\n",
-            "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.8.0)\n",
-            "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.12.1)\n",
-            "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (6.2.0)\n",
-            "Requirement already satisfied: cffi>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.14.6)\n",
-            "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (2.20)\n",
-            "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.1)\n",
-            "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.1.2)\n",
-            "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.0)\n",
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-            "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.4)\n",
-            "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
-            "Collecting backports.zoneinfo\n",
-            "  Downloading backports.zoneinfo-0.2.1-cp38-cp38-manylinux1_x86_64.whl (74 kB)\n",
-            "\u001b[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 74 kB 32.2 MB/s eta 0:00:01\n",
-            "\u001b[?25hCollecting pytz-deprecation-shim\n",
-            "  Downloading pytz_deprecation_shim-0.1.0.post0-py2.py3-none-any.whl (15 kB)\n",
-            "Collecting tzdata\n",
-            "  Downloading tzdata-2021.5-py2.py3-none-any.whl (339 kB)\n",
-            "\u001b[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 339 kB 30.1 MB/s eta 0:00:01\n",
-            "\u001b[?25hBuilding wheels for collected packages: termcolor, promise, pathtools, seqeval, blinker\n",
-            "  Building wheel for termcolor (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4847 sha256=067672b2f7f28ac472a6b1cd07b584b83890568140bc85c553cbaa0039a98989\n",
-            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/a0/16/9c/5473df82468f958445479c59e784896fa24f4a5fc024b0f501\n",
-            "  Building wheel for promise (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21502 sha256=504dc84248d0ec44574cc67d80dc391414827e3989a427d5413e1e9700612bc8\n",
-            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/54/aa/01/724885182f93150035a2a91bce34a12877e8067a97baaf5dc8\n",
-            "  Building wheel for pathtools (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=3cb49e1e70fc8dd8f3f0b0ddb00cc631f925b930abe92201655bbd0fc83e8a6c\n",
-            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/4c/8e/7e/72fbc243e1aeecae64a96875432e70d4e92f3d2d18123be004\n",
-            "  Building wheel for seqeval (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=8d4e2b51b4af004a5815585543b1e879616e537a93a913477ffd89a206c5f552\n",
-            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
-            "  Building wheel for blinker (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=3c30aa2407f8981ced9bced863d768115b6e523fe33184a32b3585b400c9e006\n",
-            "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/b7/a5/68/fe632054a5eadd531c7a49d740c50eb6adfbeca822b4eab8d4\n",
-            "Successfully built termcolor promise pathtools seqeval blinker\n",
-            "Installing collected packages: multidict, frozenlist, yarl, widgetsnbextension, tzdata, smmap, jupyterlab-widgets, backports.zoneinfo, async-timeout, aiosignal, zipp, toolz, termcolor, pytz-deprecation-shim, pandas, ipywidgets, gitdb, fsspec, dill, aiohttp, yaspin, xxhash, watchdog, validators, tzlocal, tokenizers, shortuuid, sentry-sdk, semver, pympler, pydeck, pyarrow, promise, pathtools, multiprocess, importlib-metadata, huggingface-hub, GitPython, docker-pycreds, blinker, base58, astor, altair, wrapt, wandb, transformers, tf-estimator-nightly, tensorflow-io-gcs-filesystem, tensorboard, streamlit, seqeval, sentencepiece, opt-einsum, libclang, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, datasets, astunparse, tensorflow, simpletransformers\n",
-            "  Attempting uninstall: tensorboard\n",
-            "    Found existing installation: tensorboard 2.6.0\n",
-            "    Uninstalling tensorboard-2.6.0:\n",
-            "      Successfully uninstalled tensorboard-2.6.0\n",
-            "Successfully installed GitPython-3.1.27 aiohttp-3.8.1 aiosignal-1.2.0 altair-4.2.0 astor-0.8.1 astunparse-1.6.3 async-timeout-4.0.2 backports.zoneinfo-0.2.1 base58-2.1.1 blinker-1.4 datasets-1.18.3 dill-0.3.4 docker-pycreds-0.4.0 flatbuffers-2.0 frozenlist-1.3.0 fsspec-2022.2.0 gast-0.5.3 gitdb-4.0.9 google-pasta-0.2.0 h5py-3.6.0 huggingface-hub-0.4.0 importlib-metadata-4.11.1 ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 keras-2.8.0 keras-preprocessing-1.1.2 libclang-13.0.0 multidict-6.0.2 multiprocess-0.70.12.2 opt-einsum-3.3.0 pandas-1.4.1 pathtools-0.1.2 promise-2.3 pyarrow-7.0.0 pydeck-0.7.1 pympler-1.0.1 pytz-deprecation-shim-0.1.0.post0 semver-2.13.0 sentencepiece-0.1.96 sentry-sdk-1.5.6 seqeval-1.2.2 shortuuid-1.0.8 simpletransformers-0.63.4 smmap-5.0.0 streamlit-1.6.0 tensorboard-2.8.0 tensorflow-2.8.0 tensorflow-io-gcs-filesystem-0.24.0 termcolor-1.1.0 tf-estimator-nightly-2.8.0.dev2021122109 tokenizers-0.11.5 toolz-0.11.2 transformers-4.16.2 tzdata-2021.5 tzlocal-4.1 validators-0.18.2 wandb-0.12.10 watchdog-2.1.6 widgetsnbextension-3.5.2 wrapt-1.13.3 xxhash-3.0.0 yarl-1.7.2 yaspin-2.1.0 zipp-3.7.0\n",
-            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
-            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
-            "Collecting tensorboardx\n",
-            "  Downloading tensorboardX-2.5-py2.py3-none-any.whl (125 kB)\n",
-            "\u001b[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 125 kB 17.3 MB/s eta 0:00:01\n",
-            "\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (1.16.0)\n",
-            "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (1.21.2)\n",
-            "Requirement already satisfied: protobuf>=3.8.0 in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (3.18.1)\n",
-            "Installing collected packages: tensorboardx\n",
-            "Successfully installed tensorboardx-2.5\n",
-            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install simpletransformers tensorflow\n",
-        "!pip install tensorboardx"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {
-        "id": "RJC8wj73Zd_p"
-      },
-      "outputs": [],
-      "source": [
-        "from simpletransformers.classification import ClassificationModel, ClassificationArgs, MultiLabelClassificationModel, MultiLabelClassificationArgs\n",
-        "from urllib import request\n",
-        "import pandas as pd\n",
-        "import logging\n",
-        "import torch\n",
-        "from collections import Counter\n",
-        "from ast import literal_eval"
-      ]
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "H08esTFOYO99"
+   },
+   "source": [
+    "# Main imports and code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "EnHQoayhBYlm",
+    "outputId": "eb747576-9a2a-474c-dbc7-4d6c042f68e6"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "bsX3b7ZNYVZe",
-        "outputId": "845660e8-c68b-4a52-d9ce-3c06bf7356d8"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Cuda available?  True\n"
-          ]
-        }
-      ],
-      "source": [
-        "# prepare logger\n",
-        "logging.basicConfig(level=logging.INFO)\n",
-        "\n",
-        "transformers_logger = logging.getLogger(\"transformers\")\n",
-        "transformers_logger.setLevel(logging.WARNING)\n",
-        "\n",
-        "# check gpu\n",
-        "cuda_available = torch.cuda.is_available()\n",
-        "\n",
-        "print('Cuda available? ',cuda_available)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sat Feb 26 13:12:45 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Quadro P4000        Off  | 00000000:00:05.0 Off |                  N/A |\n",
+      "| 46%   33C    P8     5W / 105W |      0MiB /  8119MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n",
+      "WARNING: infoROM is corrupted at gpu 0000:00:05.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check which gpu we're using\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
+    "id": "hYhFR7nSYOjG",
+    "outputId": "23ed0686-29d3-45ff-dc22-b2fe54e86ec4"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
-            "Requirement already satisfied: tensorflow in /opt/conda/lib/python3.8/site-packages (2.8.0)\n",
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-            "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from tensorflow) (58.2.0)\n",
-            "Requirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (3.10.0.2)\n",
-            "Requirement already satisfied: libclang>=9.0.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (13.0.0)\n",
-            "Requirement already satisfied: numpy>=1.20 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.21.2)\n",
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-            "Requirement already satisfied: gast>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.5.3)\n",
-            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.24.0)\n",
-            "Requirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.2.0)\n",
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-            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.8/site-packages (from astunparse>=1.6.0->tensorflow) (0.37.0)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (3.3.4)\n",
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-            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.6.1)\n",
-            "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.0.2)\n",
-            "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.26.0)\n",
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-            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
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-        }
-      ],
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-        "!pip install tensorflow"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting simpletransformers\n",
+      "  Downloading simpletransformers-0.63.4-py3-none-any.whl (248 kB)\n",
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+      "\u001B[?25hCollecting pandas\n",
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+      "\u001B[?25hCollecting seqeval\n",
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+      "\u001B[?25hCollecting keras-preprocessing>=1.1.1\n",
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+      "Requirement already satisfied: pyzmq>=13 in /opt/conda/lib/python3.8/site-packages (from jupyter-client<8.0->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit->simpletransformers) (22.3.0)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /opt/conda/lib/python3.8/site-packages (from pexpect>4.3->ipython<8.0,>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.0)\n",
+      "Requirement already satisfied: wcwidth in /opt/conda/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython<8.0,>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.2.5)\n",
+      "Requirement already satisfied: notebook>=4.4.1 in /opt/conda/lib/python3.8/site-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (6.4.1)\n",
+      "Requirement already satisfied: argon2-cffi in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (21.1.0)\n",
+      "Requirement already satisfied: prometheus-client in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.11.0)\n",
+      "Requirement already satisfied: Send2Trash>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.8.0)\n",
+      "Requirement already satisfied: terminado>=0.8.3 in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.12.1)\n",
+      "Requirement already satisfied: nbconvert in /opt/conda/lib/python3.8/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (6.2.0)\n",
+      "Requirement already satisfied: cffi>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.14.6)\n",
+      "Requirement already satisfied: pycparser in /opt/conda/lib/python3.8/site-packages (from cffi>=1.0.0->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (2.20)\n",
+      "Requirement already satisfied: defusedxml in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.1)\n",
+      "Requirement already satisfied: jupyterlab-pygments in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.1.2)\n",
+      "Requirement already satisfied: testpath in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.8.4)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.5.0)\n",
+      "Requirement already satisfied: bleach in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (4.1.0)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /opt/conda/lib/python3.8/site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.4)\n",
+      "Requirement already satisfied: webencodings in /opt/conda/lib/python3.8/site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
+      "Collecting backports.zoneinfo\n",
+      "  Downloading backports.zoneinfo-0.2.1-cp38-cp38-manylinux1_x86_64.whl (74 kB)\n",
+      "\u001B[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 74 kB 32.2 MB/s eta 0:00:01\n",
+      "\u001B[?25hCollecting pytz-deprecation-shim\n",
+      "  Downloading pytz_deprecation_shim-0.1.0.post0-py2.py3-none-any.whl (15 kB)\n",
+      "Collecting tzdata\n",
+      "  Downloading tzdata-2021.5-py2.py3-none-any.whl (339 kB)\n",
+      "\u001B[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 339 kB 30.1 MB/s eta 0:00:01\n",
+      "\u001B[?25hBuilding wheels for collected packages: termcolor, promise, pathtools, seqeval, blinker\n",
+      "  Building wheel for termcolor (setup.py) ... \u001B[?25ldone\n",
+      "\u001B[?25h  Created wheel for termcolor: filename=termcolor-1.1.0-py3-none-any.whl size=4847 sha256=067672b2f7f28ac472a6b1cd07b584b83890568140bc85c553cbaa0039a98989\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/a0/16/9c/5473df82468f958445479c59e784896fa24f4a5fc024b0f501\n",
+      "  Building wheel for promise (setup.py) ... \u001B[?25ldone\n",
+      "\u001B[?25h  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21502 sha256=504dc84248d0ec44574cc67d80dc391414827e3989a427d5413e1e9700612bc8\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/54/aa/01/724885182f93150035a2a91bce34a12877e8067a97baaf5dc8\n",
+      "  Building wheel for pathtools (setup.py) ... \u001B[?25ldone\n",
+      "\u001B[?25h  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=3cb49e1e70fc8dd8f3f0b0ddb00cc631f925b930abe92201655bbd0fc83e8a6c\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/4c/8e/7e/72fbc243e1aeecae64a96875432e70d4e92f3d2d18123be004\n",
+      "  Building wheel for seqeval (setup.py) ... \u001B[?25ldone\n",
+      "\u001B[?25h  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16181 sha256=8d4e2b51b4af004a5815585543b1e879616e537a93a913477ffd89a206c5f552\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
+      "  Building wheel for blinker (setup.py) ... \u001B[?25ldone\n",
+      "\u001B[?25h  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=3c30aa2407f8981ced9bced863d768115b6e523fe33184a32b3585b400c9e006\n",
+      "  Stored in directory: /tmp/pip-ephem-wheel-cache-r6lr5xzv/wheels/b7/a5/68/fe632054a5eadd531c7a49d740c50eb6adfbeca822b4eab8d4\n",
+      "Successfully built termcolor promise pathtools seqeval blinker\n",
+      "Installing collected packages: multidict, frozenlist, yarl, widgetsnbextension, tzdata, smmap, jupyterlab-widgets, backports.zoneinfo, async-timeout, aiosignal, zipp, toolz, termcolor, pytz-deprecation-shim, pandas, ipywidgets, gitdb, fsspec, dill, aiohttp, yaspin, xxhash, watchdog, validators, tzlocal, tokenizers, shortuuid, sentry-sdk, semver, pympler, pydeck, pyarrow, promise, pathtools, multiprocess, importlib-metadata, huggingface-hub, GitPython, docker-pycreds, blinker, base58, astor, altair, wrapt, wandb, transformers, tf-estimator-nightly, tensorflow-io-gcs-filesystem, tensorboard, streamlit, seqeval, sentencepiece, opt-einsum, libclang, keras-preprocessing, keras, h5py, google-pasta, gast, flatbuffers, datasets, astunparse, tensorflow, simpletransformers\n",
+      "  Attempting uninstall: tensorboard\n",
+      "    Found existing installation: tensorboard 2.6.0\n",
+      "    Uninstalling tensorboard-2.6.0:\n",
+      "      Successfully uninstalled tensorboard-2.6.0\n",
+      "Successfully installed GitPython-3.1.27 aiohttp-3.8.1 aiosignal-1.2.0 altair-4.2.0 astor-0.8.1 astunparse-1.6.3 async-timeout-4.0.2 backports.zoneinfo-0.2.1 base58-2.1.1 blinker-1.4 datasets-1.18.3 dill-0.3.4 docker-pycreds-0.4.0 flatbuffers-2.0 frozenlist-1.3.0 fsspec-2022.2.0 gast-0.5.3 gitdb-4.0.9 google-pasta-0.2.0 h5py-3.6.0 huggingface-hub-0.4.0 importlib-metadata-4.11.1 ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 keras-2.8.0 keras-preprocessing-1.1.2 libclang-13.0.0 multidict-6.0.2 multiprocess-0.70.12.2 opt-einsum-3.3.0 pandas-1.4.1 pathtools-0.1.2 promise-2.3 pyarrow-7.0.0 pydeck-0.7.1 pympler-1.0.1 pytz-deprecation-shim-0.1.0.post0 semver-2.13.0 sentencepiece-0.1.96 sentry-sdk-1.5.6 seqeval-1.2.2 shortuuid-1.0.8 simpletransformers-0.63.4 smmap-5.0.0 streamlit-1.6.0 tensorboard-2.8.0 tensorflow-2.8.0 tensorflow-io-gcs-filesystem-0.24.0 termcolor-1.1.0 tf-estimator-nightly-2.8.0.dev2021122109 tokenizers-0.11.5 toolz-0.11.2 transformers-4.16.2 tzdata-2021.5 tzlocal-4.1 validators-0.18.2 wandb-0.12.10 watchdog-2.1.6 widgetsnbextension-3.5.2 wrapt-1.13.3 xxhash-3.0.0 yarl-1.7.2 yaspin-2.1.0 zipp-3.7.0\n",
+      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\n",
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting tensorboardx\n",
+      "  Downloading tensorboardX-2.5-py2.py3-none-any.whl (125 kB)\n",
+      "\u001B[K     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| 125 kB 17.3 MB/s eta 0:00:01\n",
+      "\u001B[?25hRequirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (1.16.0)\n",
+      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (1.21.2)\n",
+      "Requirement already satisfied: protobuf>=3.8.0 in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (3.18.1)\n",
+      "Installing collected packages: tensorboardx\n",
+      "Successfully installed tensorboardx-2.5\n",
+      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install simpletransformers tensorflow\n",
+    "!pip install tensorboardx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "RJC8wj73Zd_p"
+   },
+   "outputs": [],
+   "source": [
+    "from simpletransformers.classification import ClassificationModel, ClassificationArgs, MultiLabelClassificationModel, MultiLabelClassificationArgs\n",
+    "from urllib import request\n",
+    "import pandas as pd\n",
+    "import logging\n",
+    "import torch\n",
+    "from collections import Counter\n",
+    "from ast import literal_eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "bsX3b7ZNYVZe",
+    "outputId": "845660e8-c68b-4a52-d9ce-3c06bf7356d8"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "HpRLLRzkTwdL",
-        "outputId": "9dc072ea-e419-4bc1-ad99-507cdd4e1394"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Found GPU at: /device:GPU:0\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "2022-02-26 13:14:29.325853: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
-            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
-            "2022-02-26 13:14:29.327245: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:29.328399: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:29.329348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:32.919742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:32.920448: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:32.921064: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
-            "2022-02-26 13:14:32.921610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 6966 MB memory:  -> device: 0, name: Quadro P4000, pci bus id: 0000:00:05.0, compute capability: 6.1\n"
-          ]
-        }
-      ],
-      "source": [
-        "if cuda_available:\n",
-        "  import tensorflow as tf\n",
-        "  # Get the GPU device name.\n",
-        "  device_name = tf.test.gpu_device_name()\n",
-        "  # The device name should look like the following:\n",
-        "  if device_name == '/device:GPU:0':\n",
-        "      print('Found GPU at: {}'.format(device_name))\n",
-        "  else:\n",
-        "      raise SystemError('GPU device not found')"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cuda available?  True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prepare logger\n",
+    "logging.basicConfig(level=logging.INFO)\n",
+    "\n",
+    "transformers_logger = logging.getLogger(\"transformers\")\n",
+    "transformers_logger.setLevel(logging.WARNING)\n",
+    "\n",
+    "# check gpu\n",
+    "cuda_available = torch.cuda.is_available()\n",
+    "\n",
+    "print('Cuda available? ',cuda_available)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "BMQDATlOZHxu"
-      },
-      "source": [
-        "# Fetch Don't Patronize Me! data manager module"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Requirement already satisfied: tensorflow in /opt/conda/lib/python3.8/site-packages (2.8.0)\n",
+      "Requirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.6.3)\n",
+      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.8/site-packages (from tensorflow) (58.2.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (3.10.0.2)\n",
+      "Requirement already satisfied: libclang>=9.0.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (13.0.0)\n",
+      "Requirement already satisfied: numpy>=1.20 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.21.2)\n",
+      "Requirement already satisfied: keras-preprocessing>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.1.2)\n",
+      "Requirement already satisfied: h5py>=2.9.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (3.6.0)\n",
+      "Requirement already satisfied: gast>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.5.3)\n",
+      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.24.0)\n",
+      "Requirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.2.0)\n",
+      "Requirement already satisfied: flatbuffers>=1.12 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (2.0)\n",
+      "Requirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.1.0)\n",
+      "Requirement already satisfied: tf-estimator-nightly==2.8.0.dev2021122109 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (2.8.0.dev2021122109)\n",
+      "Requirement already satisfied: six>=1.12.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.16.0)\n",
+      "Requirement already satisfied: keras<2.9,>=2.8.0rc0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (2.8.0)\n",
+      "Requirement already satisfied: tensorboard<2.9,>=2.8 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (2.8.0)\n",
+      "Requirement already satisfied: wrapt>=1.11.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.13.3)\n",
+      "Requirement already satisfied: protobuf>=3.9.2 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (3.18.1)\n",
+      "Requirement already satisfied: absl-py>=0.4.0 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (0.14.1)\n",
+      "Requirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (3.3.0)\n",
+      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /opt/conda/lib/python3.8/site-packages (from tensorflow) (1.41.0)\n",
+      "Requirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.8/site-packages (from astunparse>=1.6.0->tensorflow) (0.37.0)\n",
+      "Requirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (3.3.4)\n",
+      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.4.6)\n",
+      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (0.6.1)\n",
+      "Requirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.0.2)\n",
+      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (2.26.0)\n",
+      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.8.0)\n",
+      "Requirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.8/site-packages (from tensorboard<2.9,>=2.8->tensorflow) (1.35.0)\n",
+      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.2.8)\n",
+      "Requirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.7.2)\n",
+      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (4.2.4)\n",
+      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.8/site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (1.3.0)\n",
+      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.9,>=2.8->tensorflow) (0.4.8)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (1.26.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2021.5.30)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (3.1)\n",
+      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard<2.9,>=2.8->tensorflow) (2.0.0)\n",
+      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.9,>=2.8->tensorflow) (3.1.1)\n",
+      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "HpRLLRzkTwdL",
+    "outputId": "9dc072ea-e419-4bc1-ad99-507cdd4e1394"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "UW903YxwThrH",
-        "outputId": "4dc91901-fa9f-446a-a883-dca331443d3d"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Fetching https://raw.githubusercontent.com/Perez-AlmendrosC/dontpatronizeme/master/semeval-2022/dont_patronize_me.py\n"
-          ]
-        }
-      ],
-      "source": [
-        "module_url = f\"https://raw.githubusercontent.com/Perez-AlmendrosC/dontpatronizeme/master/semeval-2022/dont_patronize_me.py\"\n",
-        "module_name = module_url.split('/')[-1]\n",
-        "print(f'Fetching {module_url}')\n",
-        "#with open(\"file_1.txt\") as f1, open(\"file_2.txt\") as f2\n",
-        "with request.urlopen(module_url) as f, open(module_name,'w') as outf:\n",
-        "  a = f.read()\n",
-        "  outf.write(a.decode('utf-8'))"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found GPU at: /device:GPU:0\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "id": "PRxm0179aqzw"
-      },
-      "outputs": [],
-      "source": [
-        "# helper function to save predictions to an output file\n",
-        "def labels2file(p, outf_path):\n",
-        "\twith open(outf_path,'w') as outf:\n",
-        "\t\tfor pi in p:\n",
-        "\t\t\toutf.write(','.join([str(k) for k in pi])+'\\n')"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-02-26 13:14:29.325853: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
+      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2022-02-26 13:14:29.327245: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:29.328399: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:29.329348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:32.919742: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:32.920448: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:32.921064: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] 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",
+      "2022-02-26 13:14:32.921610: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 6966 MB memory:  -> device: 0, name: Quadro P4000, pci bus id: 0000:00:05.0, compute capability: 6.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "if cuda_available:\n",
+    "  import tensorflow as tf\n",
+    "  # Get the GPU device name.\n",
+    "  device_name = tf.test.gpu_device_name()\n",
+    "  # The device name should look like the following:\n",
+    "  if device_name == '/device:GPU:0':\n",
+    "      print('Found GPU at: {}'.format(device_name))\n",
+    "  else:\n",
+    "      raise SystemError('GPU device not found')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BMQDATlOZHxu"
+   },
+   "source": [
+    "# Fetch Don't Patronize Me! data manager module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "UW903YxwThrH",
+    "outputId": "4dc91901-fa9f-446a-a883-dca331443d3d"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "id": "gcDThFWVBxGb"
-      },
-      "outputs": [],
-      "source": [
-        "from dont_patronize_me import DontPatronizeMe"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fetching https://raw.githubusercontent.com/Perez-AlmendrosC/dontpatronizeme/master/semeval-2022/dont_patronize_me.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "module_url = f\"https://raw.githubusercontent.com/Perez-AlmendrosC/dontpatronizeme/master/semeval-2022/dont_patronize_me.py\"\n",
+    "module_name = module_url.split('/')[-1]\n",
+    "print(f'Fetching {module_url}')\n",
+    "#with open(\"file_1.txt\") as f1, open(\"file_2.txt\") as f2\n",
+    "with request.urlopen(module_url) as f, open(module_name,'w') as outf:\n",
+    "  a = f.read()\n",
+    "  outf.write(a.decode('utf-8'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "PRxm0179aqzw"
+   },
+   "outputs": [],
+   "source": [
+    "# helper function to save predictions to an output file\n",
+    "def labels2file(p, outf_path):\n",
+    "\twith open(outf_path,'w') as outf:\n",
+    "\t\tfor pi in p:\n",
+    "\t\t\toutf.write(','.join([str(k) for k in pi])+'\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "gcDThFWVBxGb"
+   },
+   "outputs": [],
+   "source": [
+    "from dont_patronize_me import DontPatronizeMe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "3Ay5_5Y0ThrI"
+   },
+   "outputs": [],
+   "source": [
+    "dpm = DontPatronizeMe('.', '.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "2r3USK4eThrJ",
+    "outputId": "53bbe18a-47df-4079-d28a-cf890c08b306"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {
-        "id": "3Ay5_5Y0ThrI"
-      },
-      "outputs": [],
-      "source": [
-        "dpm = DontPatronizeMe('.', '.')"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Map of label to numerical label:\n",
+      "{'Unbalanced_power_relations': 0, 'Shallow_solution': 1, 'Presupposition': 2, 'Authority_voice': 3, 'Metaphors': 4, 'Compassion': 5, 'The_poorer_the_merrier': 6}\n"
+     ]
+    }
+   ],
+   "source": [
+    "dpm.load_task1()\n",
+    "dpm.load_task2(return_one_hot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "P0YcdU80IbiS"
+   },
+   "source": [
+    "# Load paragraph IDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "8AReWYHYOUqx"
+   },
+   "outputs": [],
+   "source": [
+    "trids = pd.read_csv('./practice_splits/train_semeval_parids-labels.csv')\n",
+    "teids = pd.read_csv('./practice_splits/dev_semeval_parids-labels.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 205
     },
+    "id": "a-_ADoJAOWJA",
+    "outputId": "85dbe757-4ee5-4887-deac-60185515e141"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "2r3USK4eThrJ",
-        "outputId": "53bbe18a-47df-4079-d28a-cf890c08b306"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Map of label to numerical label:\n",
-            "{'Unbalanced_power_relations': 0, 'Shallow_solution': 1, 'Presupposition': 2, 'Authority_voice': 3, 'Metaphors': 4, 'Compassion': 5, 'The_poorer_the_merrier': 6}\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "dpm.load_task1()\n",
-        "dpm.load_task2(return_one_hot=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "P0YcdU80IbiS"
-      },
-      "source": [
-        "# Load paragraph IDs"
+      "text/plain": [
+       "   par_id                  label\n",
+       "0    4341  [1, 0, 0, 1, 0, 0, 0]\n",
+       "1    4136  [0, 1, 0, 0, 0, 0, 0]\n",
+       "2   10352  [1, 0, 0, 0, 0, 1, 0]\n",
+       "3    8279  [0, 0, 0, 1, 0, 0, 0]\n",
+       "4    1164  [1, 0, 0, 1, 1, 1, 0]"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {
-        "id": "8AReWYHYOUqx"
-      },
-      "outputs": [],
-      "source": [
-        "trids = pd.read_csv('./practice_splits/train_semeval_parids-labels.csv')\n",
-        "teids = pd.read_csv('./practice_splits/dev_semeval_parids-labels.csv')"
-      ]
-    },
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trids.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "7IfCZjwQ16MS"
+   },
+   "outputs": [],
+   "source": [
+    "trids.par_id = trids.par_id.astype(str)\n",
+    "teids.par_id = teids.par_id.astype(str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8lXrNj_Ww_FC"
+   },
+   "source": [
+    "\n",
+    "\n",
+    "# Rebuild training set (Task 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "BOxDR1H2g_3p"
+   },
+   "outputs": [],
+   "source": [
+    "rows = [] # will contain par_id, label and text\n",
+    "for idx in range(len(trids)):  \n",
+    "  parid = trids.par_id[idx]\n",
+    "  #print(parid)\n",
+    "  # select row from original dataset to retrieve `text` and binary label\n",
+    "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
+    "  label = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].label.values[0]\n",
+    "  rows.append({\n",
+    "      'par_id':parid,\n",
+    "      'text':text,\n",
+    "      'label':label\n",
+    "  })\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "8e3E08Yown5p"
+   },
+   "outputs": [],
+   "source": [
+    "trdf1 = pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 205
-        },
-        "id": "a-_ADoJAOWJA",
-        "outputId": "85dbe757-4ee5-4887-deac-60185515e141"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>label</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   par_id                  label\n",
-              "0    4341  [1, 0, 0, 1, 0, 0, 0]\n",
-              "1    4136  [0, 1, 0, 0, 0, 0, 0]\n",
-              "2   10352  [1, 0, 0, 0, 0, 1, 0]\n",
-              "3    8279  [0, 0, 0, 1, 0, 0, 0]\n",
-              "4    1164  [1, 0, 0, 1, 1, 1, 0]"
-            ]
-          },
-          "execution_count": 13,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>Durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>The next immediate problem that cropped up was...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>Far more important than the implications for t...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>To strengthen child-sensitive social protectio...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "trids.head()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {
-        "id": "7IfCZjwQ16MS"
-      },
-      "outputs": [],
-      "source": [
-        "trids.par_id = trids.par_id.astype(str)\n",
-        "teids.par_id = teids.par_id.astype(str)"
+      "text/plain": [
+       "  par_id                                               text  label\n",
+       "0   4341  The scheme saw an estimated 150,000 children f...      1\n",
+       "1   4136  Durban 's homeless communities reconciliation ...      1\n",
+       "2  10352  The next immediate problem that cropped up was...      1\n",
+       "3   8279  Far more important than the implications for t...      1\n",
+       "4   1164  To strengthen child-sensitive social protectio...      1"
       ]
-    },
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "8lXrNj_Ww_FC"
-      },
-      "source": [
-        "\n",
-        "\n",
-        "# Rebuild training set (Task 1)"
+     "data": {
+      "text/plain": [
+       "8375"
       ]
-    },
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "id": "BOxDR1H2g_3p"
-      },
-      "outputs": [],
-      "source": [
-        "rows = [] # will contain par_id, label and text\n",
-        "for idx in range(len(trids)):  \n",
-        "  parid = trids.par_id[idx]\n",
-        "  #print(parid)\n",
-        "  # select row from original dataset to retrieve `text` and binary label\n",
-        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
-        "  label = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].label.values[0]\n",
-        "  rows.append({\n",
-        "      'par_id':parid,\n",
-        "      'text':text,\n",
-        "      'label':label\n",
-        "  })\n",
-        "  "
+     "data": {
+      "text/plain": [
+       "0    7581\n",
+       "1     794\n",
+       "Name: label, dtype: int64"
       ]
-    },
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1[\"label\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Discussion regarding Analysis of class labels**\n",
+    "\n",
+    "The dataset is a skewed dataset, with 10 times more sentences not exhibiting pcl compared to sentences exhibiting pcl."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 16,
-      "metadata": {
-        "id": "8e3E08Yown5p"
-      },
-      "outputs": [],
-      "source": [
-        "trdf1 = pd.DataFrame(rows)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+      "Collecting py-readability-metrics\n",
+      "  Downloading py_readability_metrics-1.4.5-py3-none-any.whl (26 kB)\n",
+      "Requirement already satisfied: nltk in /opt/conda/lib/python3.8/site-packages (from py-readability-metrics) (3.6.4)\n",
+      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (4.62.3)\n",
+      "Requirement already satisfied: regex in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (2021.10.8)\n",
+      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (1.1.0)\n",
+      "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (8.0.1)\n",
+      "Installing collected packages: py-readability-metrics\n",
+      "Successfully installed py-readability-metrics-1.4.5\n",
+      "\u001B[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001B[0m\n",
+      "/opt/conda/lib/python3.8/runpy.py:127: RuntimeWarning: 'nltk.downloader' found in sys.modules after import of package 'nltk', but prior to execution of 'nltk.downloader'; this may result in unpredictable behaviour\n",
+      "  warn(RuntimeWarning(msg))\n",
+      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install py-readability-metrics\n",
+    "!python -m nltk.downloader punkt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from readability import Readability\n",
+    "\n",
+    "def calculate_readability(text):\n",
+    "    try:\n",
+    "        r = Readability(text)\n",
+    "        return r.flesch_kincaid().score\n",
+    "    except:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 17,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "\n",
-              "    .dataframe tbody tr th {\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>The next immediate problem that cropped up was...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>Far more important than the implications for t...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>To strengthen child-sensitive social protectio...</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "  par_id                                               text  label\n",
-              "0   4341  The scheme saw an estimated 150,000 children f...      1\n",
-              "1   4136  Durban 's homeless communities reconciliation ...      1\n",
-              "2  10352  The next immediate problem that cropped up was...      1\n",
-              "3   8279  Far more important than the implications for t...      1\n",
-              "4   1164  To strengthen child-sensitive social protectio...      1"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>num_sentences_in_paragraph</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>Durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>The next immediate problem that cropped up was...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>Far more important than the implications for t...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>To strengthen child-sensitive social protectio...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "trdf1.head()"
+      "text/plain": [
+       "  par_id                                               text  label  \\\n",
+       "0   4341  The scheme saw an estimated 150,000 children f...      1   \n",
+       "1   4136  Durban 's homeless communities reconciliation ...      1   \n",
+       "2  10352  The next immediate problem that cropped up was...      1   \n",
+       "3   8279  Far more important than the implications for t...      1   \n",
+       "4   1164  To strengthen child-sensitive social protectio...      1   \n",
+       "\n",
+       "   num_sentences_in_paragraph  \n",
+       "0                           2  \n",
+       "1                           1  \n",
+       "2                           3  \n",
+       "3                           2  \n",
+       "4                           2  "
       ]
-    },
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "paragraphs = trdf1[\"text\"]\n",
+    "\n",
+    "trdf1[\"num_sentences_in_paragraph\"] = paragraphs.apply(lambda x: len(x.split(\".\")))\n",
+    "\n",
+    "trdf1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "trdf1[\"readability_score\"] = trdf1[\"text\"].apply(lambda x: calculate_readability(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def avg_sentence_length(para):\n",
+    "    sentences = para.split(\".\")\n",
+    "    s_len = [len(s.split(\" \")) for s in sentences]\n",
+    "    return sum(s_len) / len(s_len)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trdf1[\"avg_sentence_length\"] = paragraphs.apply(lambda x : avg_sentence_length(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 18,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "8375"
-            ]
-          },
-          "execution_count": 18,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "trdf1.shape[0]"
+     "data": {
+      "text/plain": [
+       "count    8375.000000\n",
+       "mean        0.544217\n",
+       "std         2.946594\n",
+       "min         0.000000\n",
+       "25%         0.000000\n",
+       "50%         0.000000\n",
+       "75%         0.000000\n",
+       "max        67.778221\n",
+       "Name: readability_score, dtype: float64"
       ]
-    },
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1[\"readability_score\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 19,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "0    7581\n",
-              "1     794\n",
-              "Name: label, dtype: int64"
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>num_sentences_in_paragraph</th>\n",
+       "      <th>readability_score</th>\n",
+       "      <th>avg_sentence_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>Durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>The next immediate problem that cropped up was...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>Far more important than the implications for t...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>To strengthen child-sensitive social protectio...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "trdf1[\"label\"].value_counts()"
+      "text/plain": [
+       "  par_id                                               text  label  \\\n",
+       "0   4341  The scheme saw an estimated 150,000 children f...      1   \n",
+       "1   4136  Durban 's homeless communities reconciliation ...      1   \n",
+       "2  10352  The next immediate problem that cropped up was...      1   \n",
+       "3   8279  Far more important than the implications for t...      1   \n",
+       "4   1164  To strengthen child-sensitive social protectio...      1   \n",
+       "\n",
+       "   num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
+       "0                           2                0.0            18.500000  \n",
+       "1                           1                0.0             6.000000  \n",
+       "2                           3                0.0            24.666667  \n",
+       "3                           2                0.0            23.000000  \n",
+       "4                           2                0.0            25.000000  "
       ]
-    },
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pat_sent = trdf1.loc[trdf1['label'] == 1]\n",
+    "non_pat_sent = trdf1.loc[trdf1['label'] == 0]\n",
+    "\n",
+    "pat_sent.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:matplotlib.font_manager:generated new fontManager\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "**Discussion regarding Analysis of class labels**\n",
-        "\n",
-        "The dataset is a skewed dataset, with 10 times more sentences not exhibiting pcl compared to sentences exhibiting pcl."
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x576 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(nrows=2)\n",
+    "ax1.hist(pat_sent[\"num_sentences_in_paragraph\"], bins=np.linspace(1, 21))\n",
+    "ax1.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
+    "ax1.set_title(\"Number of sentences in patronising paragraph\")\n",
+    "ax2.hist(non_pat_sent[\"num_sentences_in_paragraph\"], bins=np.linspace(1, 21))\n",
+    "ax2.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
+    "ax2.set_title(\"Number of sentences in non-patronising paragraph\")\n",
+    "fig.set_figwidth(10)\n",
+    "fig.set_figheight(8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()\n",
+    "# ax.xaxis.set_major_locator()\n",
+    "# pat_sent.hist(grid=False,column=\"num_sentences_in_paragraph\", bins=21, figsize=(10, 8), xticks=pat_sent[\"num_sentences_in_paragraph\"], ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 20,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
-            "Collecting py-readability-metrics\n",
-            "  Downloading py_readability_metrics-1.4.5-py3-none-any.whl (26 kB)\n",
-            "Requirement already satisfied: nltk in /opt/conda/lib/python3.8/site-packages (from py-readability-metrics) (3.6.4)\n",
-            "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (4.62.3)\n",
-            "Requirement already satisfied: regex in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (2021.10.8)\n",
-            "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (1.1.0)\n",
-            "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from nltk->py-readability-metrics) (8.0.1)\n",
-            "Installing collected packages: py-readability-metrics\n",
-            "Successfully installed py-readability-metrics-1.4.5\n",
-            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n",
-            "/opt/conda/lib/python3.8/runpy.py:127: RuntimeWarning: 'nltk.downloader' found in sys.modules after import of package 'nltk', but prior to execution of 'nltk.downloader'; this may result in unpredictable behaviour\n",
-            "  warn(RuntimeWarning(msg))\n",
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>num_sentences_in_paragraph</th>\n",
+       "      <th>readability_score</th>\n",
+       "      <th>avg_sentence_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7590</th>\n",
+       "      <td>7525</td>\n",
+       "      <td>The Trawler : targets anyone with a Muslim con...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.294912</td>\n",
+       "      <td>133.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "!pip install py-readability-metrics\n",
-        "!python -m nltk.downloader punkt"
+      "text/plain": [
+       "     par_id                                               text  label  \\\n",
+       "7590   7525  The Trawler : targets anyone with a Muslim con...      0   \n",
+       "\n",
+       "      num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
+       "7590                           1          50.294912                133.0  "
       ]
-    },
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1[trdf1[\"avg_sentence_length\"] == 133]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 21,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from readability import Readability\n",
-        "\n",
-        "def calculate_readability(text):\n",
-        "    try:\n",
-        "        r = Readability(text)\n",
-        "        return r.flesch_kincaid().score\n",
-        "    except:\n",
-        "        return 0"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(nrows=2)\n",
+    "ax1.hist(pat_sent[\"avg_sentence_length\"], bins=np.arange(1, 60))\n",
+    "ax1.xaxis.set_major_locator(plt.MultipleLocator(5))\n",
+    "ax1.set_title(\"Number of sentences in patronising paragraph\")\n",
+    "ax2.hist(non_pat_sent[\"avg_sentence_length\"], bins=np.arange(1, 60))\n",
+    "ax2.xaxis.set_major_locator(plt.MultipleLocator(5))\n",
+    "# ax2.set_title(\"Number of sentences in non-patronising paragraph\")\n",
+    "fig.set_figwidth(15)\n",
+    "fig.set_figheight(8)\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Synonym replacement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "source": [
+    "!pip install nltk\n",
+    "import nltk\n",
+    "nltk.download('wordnet')"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import wordnet\n",
+    "\n",
+    "def get_synonyms(word):\n",
+    "    synonyms = set()\n",
+    "    for syn in wordnet.synsets(word): \n",
+    "        for l in syn.lemmas(): \n",
+    "            synonym = l.name().replace(\"_\", \" \").replace(\"-\", \" \").lower()\n",
+    "            synonym = \"\".join([char for char in synonym if char in ' qwertyuiopasdfghjklzxcvbnm'])\n",
+    "            synonyms.add(synonym) \n",
+    "    \n",
+    "    if word in synonyms:\n",
+    "        synonyms.remove(word)\n",
+    "    \n",
+    "    return list(synonyms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "def synonym_replacement(text):\n",
+    "    sentences = text.split(\".\")\n",
+    "    new_sentences = []\n",
+    "    for sent in sentences:\n",
+    "        words = sent.split(' ')\n",
+    "        synonyms = list(map(lambda w: get_synonyms(w), words))\n",
+    "        non_empty_synonyms_indices = [i for i, arr in enumerate(synonyms) if len(arr) != 0]\n",
+    "        indices = random.sample(non_empty_synonyms_indices, random.randint(0, len(non_empty_synonyms_indices)))\n",
+    "        for i in indices:\n",
+    "            words[i] = random.choice(synonyms[i])\n",
+    "        new_sentences.append(' '.join(words))\n",
+    "    return '.'.join(new_sentences)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trdf1_synonym = trdf1.copy()\n",
+    "for _ in range(9):\n",
+    "    pat_sent_synonym = trdf1.loc[trdf1['label'] == 1].copy()\n",
+    "    pat_sent_synonym['text'] = pat_sent_synonym['text'].apply(lambda x: synonym_replacement(x))\n",
+    "    trdf1_synonym = pd.concat([trdf1_synonym, pat_sent_synonym], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 22,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "      <th>num_sentences_in_paragraph</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>The next immediate problem that cropped up was...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>Far more important than the implications for t...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>To strengthen child-sensitive social protectio...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "  par_id                                               text  label  \\\n",
-              "0   4341  The scheme saw an estimated 150,000 children f...      1   \n",
-              "1   4136  Durban 's homeless communities reconciliation ...      1   \n",
-              "2  10352  The next immediate problem that cropped up was...      1   \n",
-              "3   8279  Far more important than the implications for t...      1   \n",
-              "4   1164  To strengthen child-sensitive social protectio...      1   \n",
-              "\n",
-              "   num_sentences_in_paragraph  \n",
-              "0                           2  \n",
-              "1                           1  \n",
-              "2                           3  \n",
-              "3                           2  \n",
-              "4                           2  "
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "paragraphs = trdf1[\"text\"]\n",
-        "\n",
-        "trdf1[\"num_sentences_in_paragraph\"] = paragraphs.apply(lambda x: len(x.split(\".\")))\n",
-        "\n",
-        "trdf1.head()"
+     "data": {
+      "text/plain": [
+       "1    7940\n",
+       "0    7581\n",
+       "Name: label, dtype: int64"
       ]
-    },
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf1_synonym['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Translation"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 23,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "\n",
-        "trdf1[\"readability_score\"] = trdf1[\"text\"].apply(lambda x: calculate_readability(x))"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "es\n",
+      "it\n",
+      "ta\n",
+      "eo\n",
+      "ga\n",
+      "sv\n",
+      "eo\n",
+      "I will keep my anger as long as I can, but I will pour out my wrath on you like a thousand waves! Stay out, you bastard! Leave me alone! Start a car? This car is a finished car! Abduction of the gods! God now! I am free, and my anger knows no bounds!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import googletrans\n",
+    "import random\n",
+    "# import translate\n",
+    "import deep_translator\n",
+    "\n",
+    "def translate(source_text):\n",
+    "    language_opts = ['fr', 'es', 'da', 'eo', 'ht', 'ga', 'it', 'no', 'ru', 'sv', 'tr', 'ts', 'ta', 'sq', 'be', 'bg', 'nl'] # change to restrict language choices\n",
+    "    # print(language_opts)\n",
+    "    from_lang = 'en'\n",
+    "    to_lang = random.choice(list(language_opts))\n",
+    "    # to_lang='ta'\n",
+    "    print(to_lang)\n",
+    "    translator_to = deep_translator.GoogleTranslator(source=from_lang, target=to_lang)\n",
+    "    translator_from = deep_translator.GoogleTranslator(source=to_lang, target=from_lang)\n",
+    "    translated_sent = translator_to.translate(source_text)\n",
+    "    retranslated_sent = translator_from.translate(translated_sent)\n",
+    "    return retranslated_sent\n",
+    "\n",
+    "sent = \"This is a simple, yet powerful command line translator with google translate behind it. You can also use it as a Python module in your code. \"\n",
+    "\n",
+    "for _ in range(7):\n",
+    "    sent = translate(sent)\n",
+    "print(sent)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O1KGYmpnxDjt"
+   },
+   "source": [
+    "# Rebuild test set (Task 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "id": "T6FLgB6KxGI2"
+   },
+   "outputs": [],
+   "source": [
+    "rows = [] # will contain par_id, label and text\n",
+    "for idx in range(len(teids)):  \n",
+    "  parid = teids.par_id[idx]\n",
+    "  #print(parid)\n",
+    "  # select row from original dataset\n",
+    "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
+    "  label = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].label.values[0]\n",
+    "  rows.append({\n",
+    "      'par_id':parid,\n",
+    "      'text':text,\n",
+    "      'label':label\n",
+    "  })\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "YbB9GdzJxRAH",
+    "outputId": "c78e311e-9502-4644-b6f7-0c64f64aa66f"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 24,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def avg_sentence_length(para):\n",
-        "    sentences = para.split(\".\")\n",
-        "    s_len = [len(s.split(\" \")) for s in sentences]\n",
-        "    return sum(s_len) / len(s_len)\n"
+     "data": {
+      "text/plain": [
+       "2094"
       ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "id": "vhBhTRIyxSaQ"
+   },
+   "outputs": [],
+   "source": [
+    "tedf1 = pd.DataFrame(rows)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "xK6FY70KZ6TY"
+   },
+   "source": [
+    "# RoBERTa Baseline for Task 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
-    {
-      "cell_type": "code",
-      "execution_count": 25,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "trdf1[\"avg_sentence_length\"] = paragraphs.apply(lambda x : avg_sentence_length(x))"
-      ]
+    "id": "Z-pvjbu_8h1n",
+    "outputId": "0a9da7ae-181c-40a5-a438-220f5ab960b5"
+   },
+   "outputs": [],
+   "source": [
+    "# downsample negative instances\n",
+    "pcldf = trdf1[trdf1.label==1]\n",
+    "npos = len(pcldf)\n",
+    "\n",
+    "training_set1 = pd.concat([pcldf,trdf1[trdf1.label==0][:npos*2]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 422
     },
+    "id": "mpSqMp3d8iYu",
+    "outputId": "037d4f45-eab5-4f04-e9a5-1aa64c46323d"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 26,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "count    8375.000000\n",
-              "mean        0.544217\n",
-              "std         2.946594\n",
-              "min         0.000000\n",
-              "25%         0.000000\n",
-              "50%         0.000000\n",
-              "75%         0.000000\n",
-              "max        67.778221\n",
-              "Name: readability_score, dtype: float64"
-            ]
-          },
-          "execution_count": 26,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>num_sentences_in_paragraph</th>\n",
+       "      <th>readability_score</th>\n",
+       "      <th>avg_sentence_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>Durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>The next immediate problem that cropped up was...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>Far more important than the implications for t...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>To strengthen child-sensitive social protectio...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2377</th>\n",
+       "      <td>1775</td>\n",
+       "      <td>Last but not the least element of culpability ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2378</th>\n",
+       "      <td>1776</td>\n",
+       "      <td>Then , taking the art of counter-intuitive non...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2379</th>\n",
+       "      <td>1777</td>\n",
+       "      <td>Kagunga village was reported to lack necessary...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2380</th>\n",
+       "      <td>1778</td>\n",
+       "      <td>\"After her parents high-profile divorce after ...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>38.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2381</th>\n",
+       "      <td>1779</td>\n",
+       "      <td>\"Last night One News reported on leaked Minist...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>20.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2382 rows Ã— 6 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "trdf1[\"readability_score\"].describe()"
+      "text/plain": [
+       "     par_id                                               text  label  \\\n",
+       "0      4341  The scheme saw an estimated 150,000 children f...      1   \n",
+       "1      4136  Durban 's homeless communities reconciliation ...      1   \n",
+       "2     10352  The next immediate problem that cropped up was...      1   \n",
+       "3      8279  Far more important than the implications for t...      1   \n",
+       "4      1164  To strengthen child-sensitive social protectio...      1   \n",
+       "...     ...                                                ...    ...   \n",
+       "2377   1775  Last but not the least element of culpability ...      0   \n",
+       "2378   1776  Then , taking the art of counter-intuitive non...      0   \n",
+       "2379   1777  Kagunga village was reported to lack necessary...      0   \n",
+       "2380   1778  \"After her parents high-profile divorce after ...      0   \n",
+       "2381   1779  \"Last night One News reported on leaked Minist...      0   \n",
+       "\n",
+       "      num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
+       "0                              2                0.0            18.500000  \n",
+       "1                              1                0.0             6.000000  \n",
+       "2                              3                0.0            24.666667  \n",
+       "3                              2                0.0            23.000000  \n",
+       "4                              2                0.0            25.000000  \n",
+       "...                          ...                ...                  ...  \n",
+       "2377                           2                0.0            12.500000  \n",
+       "2378                           2                0.0            23.500000  \n",
+       "2379                           3                0.0            13.333333  \n",
+       "2380                           2                0.0            38.000000  \n",
+       "2381                           2                0.0            20.500000  \n",
+       "\n",
+       "[2382 rows x 6 columns]"
       ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_set1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Normal training\n",
+    "task1_model_args = ClassificationArgs(num_train_epochs=1, \n",
+    "                                      no_save=True, \n",
+    "                                      no_cache=True, \n",
+    "                                      overwrite_output_dir=True)\n",
+    "task1_model = ClassificationModel(\"roberta\", \n",
+    "                                  'roberta-base', \n",
+    "                                  args = task1_model_args, \n",
+    "                                  num_labels=2, \n",
+    "                                  use_cuda=cuda_available)\n",
+    "# train model\n",
+    "task1_model.train_model(training_set1[['text', 'label']])\n",
+    "# run predictions\n",
+    "preds_task1, _ = task1_model.predict(tedf1.text.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "h5oxHt2R6t2I",
+    "outputId": "27505e5d-896b-4d63-dc53-905cc34d7fd2"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 27,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "      <th>num_sentences_in_paragraph</th>\n",
-              "      <th>readability_score</th>\n",
-              "      <th>avg_sentence_length</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>18.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>6.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>The next immediate problem that cropped up was...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>24.666667</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>Far more important than the implications for t...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>23.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>To strengthen child-sensitive social protectio...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>25.000000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "  par_id                                               text  label  \\\n",
-              "0   4341  The scheme saw an estimated 150,000 children f...      1   \n",
-              "1   4136  Durban 's homeless communities reconciliation ...      1   \n",
-              "2  10352  The next immediate problem that cropped up was...      1   \n",
-              "3   8279  Far more important than the implications for t...      1   \n",
-              "4   1164  To strengthen child-sensitive social protectio...      1   \n",
-              "\n",
-              "   num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
-              "0                           2                0.0            18.500000  \n",
-              "1                           1                0.0             6.000000  \n",
-              "2                           3                0.0            24.666667  \n",
-              "3                           2                0.0            23.000000  \n",
-              "4                           2                0.0            25.000000  "
-            ]
-          },
-          "execution_count": 27,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pat_sent = trdf1.loc[trdf1['label'] == 1]\n",
-        "non_pat_sent = trdf1.loc[trdf1['label'] == 0]\n",
-        "\n",
-        "pat_sent.head()"
+     "data": {
+      "text/plain": [
+       "Counter({0: 1651, 1: 443})"
       ]
-    },
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter(preds_task1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels2file([[k] for k in preds_task1], 'task1.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train with synonym replacement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 28,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "INFO:matplotlib.font_manager:generated new fontManager\n"
-          ]
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>num_sentences_in_paragraph</th>\n",
+       "      <th>readability_score</th>\n",
+       "      <th>avg_sentence_length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>Durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>24.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>Far more important than the implications for t...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>To strengthen child-sensitive social protectio...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15516</th>\n",
+       "      <td>873</td>\n",
+       "      <td>cite the fact that these kids world health org...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>31.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15517</th>\n",
+       "      <td>10070</td>\n",
+       "      <td>Fern ? ndez was a well-known philanthropist wo...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15518</th>\n",
+       "      <td>6484</td>\n",
+       "      <td>touch on a lot away their predicament , comman...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>27.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15519</th>\n",
+       "      <td>6249</td>\n",
+       "      <td>She iterate her ministry 's commitment to put ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15520</th>\n",
+       "      <td>5149</td>\n",
+       "      <td>preach the sermon , the Dean of the St. Peter ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.750000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>15521 rows Ã— 6 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np"
+      "text/plain": [
+       "      par_id                                               text  label  \\\n",
+       "0       4341  The scheme saw an estimated 150,000 children f...      1   \n",
+       "1       4136  Durban 's homeless communities reconciliation ...      1   \n",
+       "2      10352  The next immediate problem that cropped up was...      1   \n",
+       "3       8279  Far more important than the implications for t...      1   \n",
+       "4       1164  To strengthen child-sensitive social protectio...      1   \n",
+       "...      ...                                                ...    ...   \n",
+       "15516    873  cite the fact that these kids world health org...      1   \n",
+       "15517  10070  Fern ? ndez was a well-known philanthropist wo...      1   \n",
+       "15518   6484  touch on a lot away their predicament , comman...      1   \n",
+       "15519   6249  She iterate her ministry 's commitment to put ...      1   \n",
+       "15520   5149  preach the sermon , the Dean of the St. Peter ...      1   \n",
+       "\n",
+       "       num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
+       "0                               2                0.0            18.500000  \n",
+       "1                               1                0.0             6.000000  \n",
+       "2                               3                0.0            24.666667  \n",
+       "3                               2                0.0            23.000000  \n",
+       "4                               2                0.0            25.000000  \n",
+       "...                           ...                ...                  ...  \n",
+       "15516                           2                0.0            31.500000  \n",
+       "15517                           2                0.0            19.500000  \n",
+       "15518                           2                0.0            27.500000  \n",
+       "15519                           2                0.0            18.500000  \n",
+       "15520                           4                0.0            15.750000  \n",
+       "\n",
+       "[15521 rows x 6 columns]"
       ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_set1_synonyms = trdf1_synonym\n",
+    "training_set1_synonyms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 538,
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     },
+    "id": "PoW_s23AZ_DG",
+    "outputId": "cae26801-a680-4d5b-9d76-d56872b5e611"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 29,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 720x576 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, (ax1, ax2) = plt.subplots(nrows=2)\n",
-        "ax1.hist(pat_sent[\"num_sentences_in_paragraph\"], bins=np.linspace(1, 21))\n",
-        "ax1.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
-        "ax1.set_title(\"Number of sentences in patronising paragraph\")\n",
-        "ax2.hist(non_pat_sent[\"num_sentences_in_paragraph\"], bins=np.linspace(1, 21))\n",
-        "ax2.xaxis.set_major_locator(plt.MultipleLocator(1))\n",
-        "ax2.set_title(\"Number of sentences in non-patronising paragraph\")\n",
-        "fig.set_figwidth(10)\n",
-        "fig.set_figheight(8)\n",
-        "fig.tight_layout()\n",
-        "plt.show()\n",
-        "# ax.xaxis.set_major_locator()\n",
-        "# pat_sent.hist(grid=False,column=\"num_sentences_in_paragraph\", bins=21, figsize=(10, 8), xticks=pat_sent[\"num_sentences_in_paragraph\"], ax=ax)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to acquire lock 139946483054384 on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
+      "DEBUG:filelock:Lock 139946483054384 acquired on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 30,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "      <th>num_sentences_in_paragraph</th>\n",
-              "      <th>readability_score</th>\n",
-              "      <th>avg_sentence_length</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>7590</th>\n",
-              "      <td>7525</td>\n",
-              "      <td>The Trawler : targets anyone with a Muslim con...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>50.294912</td>\n",
-              "      <td>133.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     par_id                                               text  label  \\\n",
-              "7590   7525  The Trawler : targets anyone with a Muslim con...      0   \n",
-              "\n",
-              "      num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
-              "7590                           1          50.294912                133.0  "
-            ]
-          },
-          "execution_count": 30,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "trdf1[trdf1[\"avg_sentence_length\"] == 133]"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8be5a63b87d546439dc5b2c102a53333",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 31,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 1080x576 with 2 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          },
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "fig, (ax1, ax2) = plt.subplots(nrows=2)\n",
-        "ax1.hist(pat_sent[\"avg_sentence_length\"], bins=np.arange(1, 60))\n",
-        "ax1.xaxis.set_major_locator(plt.MultipleLocator(5))\n",
-        "ax1.set_title(\"Number of sentences in patronising paragraph\")\n",
-        "ax2.hist(non_pat_sent[\"avg_sentence_length\"], bins=np.arange(1, 60))\n",
-        "ax2.xaxis.set_major_locator(plt.MultipleLocator(5))\n",
-        "# ax2.set_title(\"Number of sentences in non-patronising paragraph\")\n",
-        "fig.set_figwidth(15)\n",
-        "fig.set_figheight(8)\n",
-        "fig.tight_layout()\n",
-        "plt.show()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to release lock 139946483054384 on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
+      "DEBUG:filelock:Lock 139946483054384 released on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
+      "DEBUG:filelock:Attempting to acquire lock 139946480692576 on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
+      "DEBUG:filelock:Lock 139946480692576 acquired on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Synonym replacement"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7de8d7262f2e4875a78e204e47c5c477",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/478M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 32,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
-            "Requirement already satisfied: nltk in /opt/conda/lib/python3.8/site-packages (3.6.4)\n",
-            "Requirement already satisfied: tqdm in /opt/conda/lib/python3.8/site-packages (from nltk) (4.62.3)\n",
-            "Requirement already satisfied: regex in /opt/conda/lib/python3.8/site-packages (from nltk) (2021.10.8)\n",
-            "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from nltk) (8.0.1)\n",
-            "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from nltk) (1.1.0)\n",
-            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
-          ]
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
-            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "True"
-            ]
-          },
-          "execution_count": 32,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "!pip install nltk\n",
-        "import nltk\n",
-        "nltk.download('wordnet')"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to release lock 139946480692576 on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
+      "DEBUG:filelock:Lock 139946480692576 released on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
+      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias']\n",
+      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "DEBUG:filelock:Attempting to acquire lock 139948825858928 on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
+      "DEBUG:filelock:Lock 139948825858928 acquired on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 33,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from nltk.corpus import wordnet\n",
-        "\n",
-        "def get_synonyms(word):\n",
-        "    synonyms = set()\n",
-        "    for syn in wordnet.synsets(word): \n",
-        "        for l in syn.lemmas(): \n",
-        "            synonym = l.name().replace(\"_\", \" \").replace(\"-\", \" \").lower()\n",
-        "            synonym = \"\".join([char for char in synonym if char in ' qwertyuiopasdfghjklzxcvbnm'])\n",
-        "            synonyms.add(synonym) \n",
-        "    \n",
-        "    if word in synonyms:\n",
-        "        synonyms.remove(word)\n",
-        "    \n",
-        "    return list(synonyms)"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b6b8c768b80452f9702586768393fde",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 59,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import random\n",
-        "\n",
-        "def synonym_replacement(text):\n",
-        "    sentences = text.split(\".\")\n",
-        "    new_sentences = []\n",
-        "    for sent in sentences:\n",
-        "        words = sent.split(' ')\n",
-        "        synonyms = list(map(lambda w: get_synonyms(w), words))\n",
-        "        non_empty_synonyms_indices = [i for i, arr in enumerate(synonyms) if len(arr) != 0]\n",
-        "        indices = random.sample(non_empty_synonyms_indices, random.randint(0, len(non_empty_synonyms_indices)))\n",
-        "        for i in indices:\n",
-        "            words[i] = random.choice(synonyms[i])\n",
-        "        new_sentences.append(' '.join(words))\n",
-        "    return '.'.join(new_sentences)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to release lock 139948825858928 on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
+      "DEBUG:filelock:Lock 139948825858928 released on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
+      "DEBUG:filelock:Attempting to acquire lock 139948825440848 on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
+      "DEBUG:filelock:Lock 139948825440848 acquired on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 62,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "trdf1_synonym = trdf1.copy()\n",
-        "for _ in range(9):\n",
-        "    pat_sent_synonym = trdf1.loc[trdf1['label'] == 1].copy()\n",
-        "    pat_sent_synonym['text'] = pat_sent_synonym['text'].apply(lambda x: synonym_replacement(x))\n",
-        "    trdf1_synonym = pd.concat([trdf1_synonym, pat_sent_synonym], ignore_index=True)"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3809e72b9da5471d9b009e1c957e5d6e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "1    7940\n",
-              "0    7581\n",
-              "Name: label, dtype: int64"
-            ]
-          },
-          "execution_count": 65,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "trdf1_synonym['label'].value_counts()"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to release lock 139948825440848 on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
+      "DEBUG:filelock:Lock 139948825440848 released on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
+      "DEBUG:filelock:Attempting to acquire lock 139946483054384 on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
+      "DEBUG:filelock:Lock 139946483054384 acquired on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "O1KGYmpnxDjt"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70ac437a0b2e457ba4cf52a91af4b433",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "# Rebuild test set (Task 1)"
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 66,
-      "metadata": {
-        "id": "T6FLgB6KxGI2"
-      },
-      "outputs": [],
-      "source": [
-        "rows = [] # will contain par_id, label and text\n",
-        "for idx in range(len(teids)):  \n",
-        "  parid = teids.par_id[idx]\n",
-        "  #print(parid)\n",
-        "  # select row from original dataset\n",
-        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
-        "  label = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].label.values[0]\n",
-        "  rows.append({\n",
-        "      'par_id':parid,\n",
-        "      'text':text,\n",
-        "      'label':label\n",
-        "  })\n",
-        "  "
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "DEBUG:filelock:Attempting to release lock 139946483054384 on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
+      "DEBUG:filelock:Lock 139946483054384 released on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
+      "/opt/conda/lib/python3.8/site-packages/simpletransformers/classification/classification_model.py:585: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
+      "  warnings.warn(\n",
+      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 67,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "YbB9GdzJxRAH",
-        "outputId": "c78e311e-9502-4644-b6f7-0c64f64aa66f"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "33ee72e416fc40338680953e5ae30ed2",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "2094"
-            ]
-          },
-          "execution_count": 67,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "len(rows)"
+      "text/plain": [
+       "  0%|          | 0/15521 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 68,
-      "metadata": {
-        "id": "vhBhTRIyxSaQ"
-      },
-      "outputs": [],
-      "source": [
-        "tedf1 = pd.DataFrame(rows)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xK6FY70KZ6TY"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dcc3d18209bb46c889cf017a9bcb732d",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "# RoBERTa Baseline for Task 1"
+      "text/plain": [
+       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 69,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Z-pvjbu_8h1n",
-        "outputId": "0a9da7ae-181c-40a5-a438-220f5ab960b5"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c6ec7ac84154b36b662a0d69f7e5726",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "# downsample negative instances\n",
-        "pcldf = trdf1[trdf1.label==1]\n",
-        "npos = len(pcldf)\n",
-        "\n",
-        "training_set1 = pd.concat([pcldf,trdf1[trdf1.label==0][:npos*2]])"
+      "text/plain": [
+       "Running Epoch 0 of 1:   0%|          | 0/1941 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": 70,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 422
-        },
-        "id": "mpSqMp3d8iYu",
-        "outputId": "037d4f45-eab5-4f04-e9a5-1aa64c46323d"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
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-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "      <th>num_sentences_in_paragraph</th>\n",
-              "      <th>readability_score</th>\n",
-              "      <th>avg_sentence_length</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>18.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>6.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>The next immediate problem that cropped up was...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>24.666667</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>Far more important than the implications for t...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>23.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>To strengthen child-sensitive social protectio...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>25.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2377</th>\n",
-              "      <td>1775</td>\n",
-              "      <td>Last but not the least element of culpability ...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>12.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2378</th>\n",
-              "      <td>1776</td>\n",
-              "      <td>Then , taking the art of counter-intuitive non...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>23.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2379</th>\n",
-              "      <td>1777</td>\n",
-              "      <td>Kagunga village was reported to lack necessary...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>13.333333</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2380</th>\n",
-              "      <td>1778</td>\n",
-              "      <td>\"After her parents high-profile divorce after ...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>38.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2381</th>\n",
-              "      <td>1779</td>\n",
-              "      <td>\"Last night One News reported on leaked Minist...</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>20.500000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>2382 rows Ã— 6 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     par_id                                               text  label  \\\n",
-              "0      4341  The scheme saw an estimated 150,000 children f...      1   \n",
-              "1      4136  Durban 's homeless communities reconciliation ...      1   \n",
-              "2     10352  The next immediate problem that cropped up was...      1   \n",
-              "3      8279  Far more important than the implications for t...      1   \n",
-              "4      1164  To strengthen child-sensitive social protectio...      1   \n",
-              "...     ...                                                ...    ...   \n",
-              "2377   1775  Last but not the least element of culpability ...      0   \n",
-              "2378   1776  Then , taking the art of counter-intuitive non...      0   \n",
-              "2379   1777  Kagunga village was reported to lack necessary...      0   \n",
-              "2380   1778  \"After her parents high-profile divorce after ...      0   \n",
-              "2381   1779  \"Last night One News reported on leaked Minist...      0   \n",
-              "\n",
-              "      num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
-              "0                              2                0.0            18.500000  \n",
-              "1                              1                0.0             6.000000  \n",
-              "2                              3                0.0            24.666667  \n",
-              "3                              2                0.0            23.000000  \n",
-              "4                              2                0.0            25.000000  \n",
-              "...                          ...                ...                  ...  \n",
-              "2377                           2                0.0            12.500000  \n",
-              "2378                           2                0.0            23.500000  \n",
-              "2379                           3                0.0            13.333333  \n",
-              "2380                           2                0.0            38.000000  \n",
-              "2381                           2                0.0            20.500000  \n",
-              "\n",
-              "[2382 rows x 6 columns]"
-            ]
-          },
-          "execution_count": 70,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "training_set1"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.\n",
+      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "## Normal training\n",
-        "task1_model_args = ClassificationArgs(num_train_epochs=1, \n",
-        "                                      no_save=True, \n",
-        "                                      no_cache=True, \n",
-        "                                      overwrite_output_dir=True)\n",
-        "task1_model = ClassificationModel(\"roberta\", \n",
-        "                                  'roberta-base', \n",
-        "                                  args = task1_model_args, \n",
-        "                                  num_labels=2, \n",
-        "                                  use_cuda=cuda_available)\n",
-        "# train model\n",
-        "task1_model.train_model(training_set1[['text', 'label']])\n",
-        "# run predictions\n",
-        "preds_task1, _ = task1_model.predict(tedf1.text.tolist())"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c45aceb741f34cd7b6c41375d72ac0ce",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2094 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "h5oxHt2R6t2I",
-        "outputId": "27505e5d-896b-4d63-dc53-905cc34d7fd2"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a924855255f4f429268ead11535b7f7",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Counter({0: 1651, 1: 443})"
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "Counter(preds_task1)"
+      "text/plain": [
+       "  0%|          | 0/262 [00:00<?, ?it/s]"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "task1_model_args = ClassificationArgs(num_train_epochs=1, \n",
+    "                                      no_save=True, \n",
+    "                                      no_cache=True, \n",
+    "                                      overwrite_output_dir=True)\n",
+    "task1_model = ClassificationModel(\"roberta\", \n",
+    "                                  'roberta-base', \n",
+    "                                  args = task1_model_args, \n",
+    "                                  num_labels=2, \n",
+    "                                  use_cuda=cuda_available)\n",
+    "# train model\n",
+    "task1_model.train_model(training_set1_synonyms[['text', 'label']])\n",
+    "# run predictions\n",
+    "preds_task1, _ = task1_model.predict(tedf1.text.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "labels2file([[k] for k in preds_task1], 'task1.txt')"
+     "data": {
+      "text/plain": [
+       "Counter({0: 1996, 1: 98})"
       ]
-    },
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Counter(preds_task1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Train with synonym replacement"
+     "data": {
+      "text/plain": [
+       "0.9135625596943648"
       ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_labels = tedf1.label.to_list()\n",
+    "correct = 0\n",
+    "for i in range(len(preds_task1)):\n",
+    "    correct += preds_task1[i] == test_labels[i]\n",
+    "correct / len(preds_task1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "k7Cc_u5Oli7j"
+   },
+   "source": [
+    "# Rebuild training set (Task 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "D2WLYT7wli7k"
+   },
+   "outputs": [],
+   "source": [
+    "rows2 = [] # will contain par_id, label and text\n",
+    "for idx in range(len(trids)):  \n",
+    "  parid = trids.par_id[idx]\n",
+    "  label = trids.label[idx]\n",
+    "  # select row from original dataset to retrieve the `text` value\n",
+    "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
+    "  rows2.append({\n",
+    "      'par_id':parid,\n",
+    "      'text':text,\n",
+    "      'label':label\n",
+    "  })\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "LFqMMb5Jli7l"
+   },
+   "outputs": [],
+   "source": [
+    "trdf2 = pd.DataFrame(rows2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 422
     },
+    "id": "HayrC9q7mQPl",
+    "outputId": "db5f1bdf-c09a-4a57-f81e-612100e32b44"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 72,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
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-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "      <th>num_sentences_in_paragraph</th>\n",
-              "      <th>readability_score</th>\n",
-              "      <th>avg_sentence_length</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4341</td>\n",
-              "      <td>The scheme saw an estimated 150,000 children f...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>18.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>4136</td>\n",
-              "      <td>Durban 's homeless communities reconciliation ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>6.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>The next immediate problem that cropped up was...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>24.666667</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>Far more important than the implications for t...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>23.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>To strengthen child-sensitive social protectio...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>25.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15516</th>\n",
-              "      <td>873</td>\n",
-              "      <td>cite the fact that these kids world health org...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>31.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15517</th>\n",
-              "      <td>10070</td>\n",
-              "      <td>Fern ? ndez was a well-known philanthropist wo...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>19.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15518</th>\n",
-              "      <td>6484</td>\n",
-              "      <td>touch on a lot away their predicament , comman...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>27.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15519</th>\n",
-              "      <td>6249</td>\n",
-              "      <td>She iterate her ministry 's commitment to put ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>18.500000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>15520</th>\n",
-              "      <td>5149</td>\n",
-              "      <td>preach the sermon , the Dean of the St. Peter ...</td>\n",
-              "      <td>1</td>\n",
-              "      <td>4</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>15.750000</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>15521 rows Ã— 6 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "      par_id                                               text  label  \\\n",
-              "0       4341  The scheme saw an estimated 150,000 children f...      1   \n",
-              "1       4136  Durban 's homeless communities reconciliation ...      1   \n",
-              "2      10352  The next immediate problem that cropped up was...      1   \n",
-              "3       8279  Far more important than the implications for t...      1   \n",
-              "4       1164  To strengthen child-sensitive social protectio...      1   \n",
-              "...      ...                                                ...    ...   \n",
-              "15516    873  cite the fact that these kids world health org...      1   \n",
-              "15517  10070  Fern ? ndez was a well-known philanthropist wo...      1   \n",
-              "15518   6484  touch on a lot away their predicament , comman...      1   \n",
-              "15519   6249  She iterate her ministry 's commitment to put ...      1   \n",
-              "15520   5149  preach the sermon , the Dean of the St. Peter ...      1   \n",
-              "\n",
-              "       num_sentences_in_paragraph  readability_score  avg_sentence_length  \n",
-              "0                               2                0.0            18.500000  \n",
-              "1                               1                0.0             6.000000  \n",
-              "2                               3                0.0            24.666667  \n",
-              "3                               2                0.0            23.000000  \n",
-              "4                               2                0.0            25.000000  \n",
-              "...                           ...                ...                  ...  \n",
-              "15516                           2                0.0            31.500000  \n",
-              "15517                           2                0.0            19.500000  \n",
-              "15518                           2                0.0            27.500000  \n",
-              "15519                           2                0.0            18.500000  \n",
-              "15520                           4                0.0            15.750000  \n",
-              "\n",
-              "[15521 rows x 6 columns]"
-            ]
-          },
-          "execution_count": 72,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>the scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>the next immediate problem that cropped up was...</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>far more important than the implications for t...</td>\n",
+       "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>to strengthen child-sensitive social protectio...</td>\n",
+       "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8370</th>\n",
+       "      <td>8380</td>\n",
+       "      <td>rescue teams search for survivors on the rubbl...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8371</th>\n",
+       "      <td>8381</td>\n",
+       "      <td>the launch of ' happy birthday ' took place la...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8372</th>\n",
+       "      <td>8382</td>\n",
+       "      <td>the unrest has left at least 20,000 people dea...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8373</th>\n",
+       "      <td>8383</td>\n",
+       "      <td>you have to see it from my perspective . i may...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8374</th>\n",
+       "      <td>8384</td>\n",
+       "      <td>yet there was one occasion when we went to the...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8375 rows Ã— 3 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "training_set1_synonyms = trdf1_synonym\n",
-        "training_set1_synonyms"
+      "text/plain": [
+       "     par_id  ...                  label\n",
+       "0      4341  ...  [1, 0, 0, 1, 0, 0, 0]\n",
+       "1      4136  ...  [0, 1, 0, 0, 0, 0, 0]\n",
+       "2     10352  ...  [1, 0, 0, 0, 0, 1, 0]\n",
+       "3      8279  ...  [0, 0, 0, 1, 0, 0, 0]\n",
+       "4      1164  ...  [1, 0, 0, 1, 1, 1, 0]\n",
+       "...     ...  ...                    ...\n",
+       "8370   8380  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "8371   8381  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "8372   8382  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "8373   8383  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "8374   8384  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "\n",
+       "[8375 rows x 3 columns]"
       ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trdf2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "MxHLB_g0pfEb"
+   },
+   "outputs": [],
+   "source": [
+    "trdf2.label = trdf2.label.apply(literal_eval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gukbmv0bli7l"
+   },
+   "source": [
+    "# Rebuild test set (Task 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "gjH-AJK1li7m"
+   },
+   "outputs": [],
+   "source": [
+    "rows2 = [] # will contain par_id, label and text\n",
+    "for idx in range(len(teids)):  \n",
+    "  parid = teids.par_id[idx]\n",
+    "  label = teids.label[idx]\n",
+    "  #print(parid)\n",
+    "  # select row from original dataset to access the `text` value\n",
+    "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
+    "  rows2.append({\n",
+    "      'par_id':parid,\n",
+    "      'text':text,\n",
+    "      'label':label\n",
+    "  })\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SRP-tn5wli7n"
+   },
+   "outputs": [],
+   "source": [
+    "tedf2 = pd.DataFrame(rows2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 422
     },
+    "id": "8U2lrfJiolku",
+    "outputId": "6bf1181c-3e95-4913-cceb-9cc9e08b6c29"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 73,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 538,
-          "referenced_widgets": [
-            "de026e2d1ec848fbb35faf1746e7579d",
-            "7a0d26d292e54498a15bc2e54e6c3aee",
-            "13bc966911b1433ab6f8245f88b86e2d",
-            "ec69c0f693b74ac8b737816b1efaa8bf",
-            "cf4fb870564043cfbc171ae05143ec5f",
-            "abc4d60485bf40d7abf3cbcf212e39a2",
-            "85ea942b0e194d2a9160f82494fff8e0",
-            "6b2a3cde2cdb4bc6a9d0623363c66266",
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-        {
-          "name": "stderr",
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-          "text": [
-            "DEBUG:filelock:Attempting to acquire lock 139946483054384 on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
-            "DEBUG:filelock:Lock 139946483054384 acquired on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n"
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-            "DEBUG:filelock:Attempting to release lock 139946483054384 on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
-            "DEBUG:filelock:Lock 139946483054384 released on /root/.cache/huggingface/transformers/733bade19e5f0ce98e6531021dd5180994bb2f7b8bd7e80c7968805834ba351e.35205c6cfc956461d8515139f0f8dd5d207a2f336c0c3a83b4bc8dca3518e37b.lock\n",
-            "DEBUG:filelock:Attempting to acquire lock 139946480692576 on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
-            "DEBUG:filelock:Lock 139946480692576 acquired on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n"
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-            "text/plain": [
-              "Downloading:   0%|          | 0.00/478M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "DEBUG:filelock:Attempting to release lock 139946480692576 on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
-            "DEBUG:filelock:Lock 139946480692576 released on /root/.cache/huggingface/transformers/51ba668f7ff34e7cdfa9561e8361747738113878850a7d717dbc69de8683aaad.c7efaa30a0d80b2958b876969faa180e485944a849deee4ad482332de65365a7.lock\n",
-            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.layer_norm.weight', 'lm_head.bias', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias']\n",
-            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight']\n",
-            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
-            "DEBUG:filelock:Attempting to acquire lock 139948825858928 on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
-            "DEBUG:filelock:Lock 139948825858928 acquired on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n"
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-            "text/plain": [
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-            ]
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-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "DEBUG:filelock:Attempting to release lock 139948825858928 on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
-            "DEBUG:filelock:Lock 139948825858928 released on /root/.cache/huggingface/transformers/d3ccdbfeb9aaa747ef20432d4976c32ee3fa69663b379deb253ccfce2bb1fdc5.d67d6b367eb24ab43b08ad55e014cf254076934f71d832bbab9ad35644a375ab.lock\n",
-            "DEBUG:filelock:Attempting to acquire lock 139948825440848 on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
-            "DEBUG:filelock:Lock 139948825440848 acquired on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n"
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-            "text/plain": [
-              "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "DEBUG:filelock:Attempting to release lock 139948825440848 on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
-            "DEBUG:filelock:Lock 139948825440848 released on /root/.cache/huggingface/transformers/cafdecc90fcab17011e12ac813dd574b4b3fea39da6dd817813efa010262ff3f.5d12962c5ee615a4c803841266e9c3be9a691a924f72d395d3a6c6c81157788b.lock\n",
-            "DEBUG:filelock:Attempting to acquire lock 139946483054384 on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
-            "DEBUG:filelock:Lock 139946483054384 acquired on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n"
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-            "text/plain": [
-              "Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "DEBUG:filelock:Attempting to release lock 139946483054384 on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
-            "DEBUG:filelock:Lock 139946483054384 released on /root/.cache/huggingface/transformers/d53fc0fa09b8342651efd4073d75e19617b3e51287c2a535becda5808a8db287.fc9576039592f026ad76a1c231b89aee8668488c671dfbe6616bab2ed298d730.lock\n",
-            "/opt/conda/lib/python3.8/site-packages/simpletransformers/classification/classification_model.py:585: UserWarning: Dataframe headers not specified. Falling back to using column 0 as text and column 1 as labels.\n",
-            "  warnings.warn(\n",
-            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
-          ]
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-            "text/plain": [
-              "  0%|          | 0/15521 [00:00<?, ?it/s]"
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-          "metadata": {},
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-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
-            "  warnings.warn(\n"
-          ]
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-        {
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-              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
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-              "Running Epoch 0 of 1:   0%|          | 0/1941 [00:00<?, ?it/s]"
-            ]
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-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
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-          "text": [
-            "INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.\n",
-            "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
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+       "      <td>[0, 0, 1, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4063</td>\n",
+       "      <td>\"budding chefs , like \"\" fred \"\" , \"\" winston ...</td>\n",
+       "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4089</td>\n",
+       "      <td>\"in a 90-degree view of his constituency , one...</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2089</th>\n",
+       "      <td>10462</td>\n",
+       "      <td>the sad spectacle , which occurred on saturday...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2090</th>\n",
+       "      <td>10463</td>\n",
+       "      <td>\"\"\" the pakistani police came to our house and...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2091</th>\n",
+       "      <td>10464</td>\n",
+       "      <td>\"when marie o'donoghue went looking for a spec...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2092</th>\n",
+       "      <td>10465</td>\n",
+       "      <td>\"sri lankan norms and culture inhibit women fr...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2093</th>\n",
+       "      <td>10466</td>\n",
+       "      <td>he added that the afp will continue to bank on...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2094 rows Ã— 3 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "task1_model_args = ClassificationArgs(num_train_epochs=1, \n",
-        "                                      no_save=True, \n",
-        "                                      no_cache=True, \n",
-        "                                      overwrite_output_dir=True)\n",
-        "task1_model = ClassificationModel(\"roberta\", \n",
-        "                                  'roberta-base', \n",
-        "                                  args = task1_model_args, \n",
-        "                                  num_labels=2, \n",
-        "                                  use_cuda=cuda_available)\n",
-        "# train model\n",
-        "task1_model.train_model(training_set1_synonyms[['text', 'label']])\n",
-        "# run predictions\n",
-        "preds_task1, _ = task1_model.predict(tedf1.text.tolist())"
+      "text/plain": [
+       "     par_id  ...                  label\n",
+       "0      4046  ...  [1, 0, 0, 1, 0, 0, 0]\n",
+       "1      1279  ...  [0, 1, 0, 0, 0, 0, 0]\n",
+       "2      8330  ...  [0, 0, 1, 0, 0, 0, 0]\n",
+       "3      4063  ...  [1, 0, 0, 1, 1, 1, 0]\n",
+       "4      4089  ...  [1, 0, 0, 0, 0, 0, 0]\n",
+       "...     ...  ...                    ...\n",
+       "2089  10462  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "2090  10463  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "2091  10464  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "2092  10465  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "2093  10466  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "\n",
+       "[2094 rows x 3 columns]"
       ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tedf2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "81aQFjWqpbe2"
+   },
+   "outputs": [],
+   "source": [
+    "tedf2.label = tedf2.label.apply(literal_eval)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YKFiVaslbAiC"
+   },
+   "source": [
+    "# RoBERTa baseline for Task 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hmr5ZZf5Ik5T"
+   },
+   "outputs": [],
+   "source": [
+    "all_negs = trdf2[trdf2.label.apply(lambda x:sum(x) == 0)]\n",
+    "all_pos = trdf2[trdf2.label.apply(lambda x:sum(x) > 0)]\n",
+    "\n",
+    "training_set2 = pd.concat([all_pos,all_negs[:round(len(all_pos)*0.5)]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 422
     },
+    "id": "zyBcJoHtJHE2",
+    "outputId": "983b27a6-3bec-47bc-e564-79face4b061c"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 74,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Counter({0: 1996, 1: 98})"
-            ]
-          },
-          "execution_count": 74,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>par_id</th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4341</td>\n",
+       "      <td>the scheme saw an estimated 150,000 children f...</td>\n",
+       "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4136</td>\n",
+       "      <td>durban 's homeless communities reconciliation ...</td>\n",
+       "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10352</td>\n",
+       "      <td>the next immediate problem that cropped up was...</td>\n",
+       "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8279</td>\n",
+       "      <td>far more important than the implications for t...</td>\n",
+       "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1164</td>\n",
+       "      <td>to strengthen child-sensitive social protectio...</td>\n",
+       "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1186</th>\n",
+       "      <td>434</td>\n",
+       "      <td>\"\"\" i was absolutely useless at school , hopel...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1187</th>\n",
+       "      <td>435</td>\n",
+       "      <td>i also noticed the change in socio-economic le...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1188</th>\n",
+       "      <td>436</td>\n",
+       "      <td>can donald trump win ? it 's possible , but ce...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1189</th>\n",
+       "      <td>437</td>\n",
+       "      <td>he added that any introduction of new law must...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1190</th>\n",
+       "      <td>439</td>\n",
+       "      <td>lusaka zambia ( xinhua ) -- zambia ? s immigra...</td>\n",
+       "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1191 rows Ã— 3 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "Counter(preds_task1)"
+      "text/plain": [
+       "     par_id  ...                  label\n",
+       "0      4341  ...  [1, 0, 0, 1, 0, 0, 0]\n",
+       "1      4136  ...  [0, 1, 0, 0, 0, 0, 0]\n",
+       "2     10352  ...  [1, 0, 0, 0, 0, 1, 0]\n",
+       "3      8279  ...  [0, 0, 0, 1, 0, 0, 0]\n",
+       "4      1164  ...  [1, 0, 0, 1, 1, 1, 0]\n",
+       "...     ...  ...                    ...\n",
+       "1186    434  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "1187    435  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "1188    436  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "1189    437  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "1190    439  ...  [0, 0, 0, 0, 0, 0, 0]\n",
+       "\n",
+       "[1191 rows x 3 columns]"
       ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_set2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 379,
+     "referenced_widgets": [
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+      "c4dbb10f007f483a950a95555583dc2a",
+      "d66f8f8d82f9499488b6ffa16193b320",
+      "25fdc4ac2d714e5ba99b4f297726d36c",
+      "5e5ac5500b974cfcbf76d2e5d70adb3d",
+      "e4ccd1d746ab4f1f826477bb021c34a8",
+      "16d4a580157446daaa4f721d28d095fa",
+      "ec1e8ea22a8f4272b258fa19a2c3ec83",
+      "fba89fa62d6a413b90f3961755794a61",
+      "22f9701e81034b5ea8d1bdd38ab0d191",
+      "95392827ab8149388dd9af86d1fb03a1",
+      "92b7ffe0dd1b49d1acdb2b588f72569d",
+      "afd5528b85874795ac1d736dde15b810",
+      "99a8a52b7dfb4fa994a4b09feba8fe58"
+     ]
     },
+    "id": "ECb7_mwzbFa6",
+    "outputId": "d433e1aa-c3c4-4a00-9b2a-c04ccdc20588"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 79,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "0.9135625596943648"
-            ]
-          },
-          "execution_count": 79,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "test_labels = tedf1.label.to_list()\n",
-        "correct = 0\n",
-        "for i in range(len(preds_task1)):\n",
-        "    correct += preds_task1[i] == test_labels[i]\n",
-        "correct / len(preds_task1)"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForMultiLabelSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight']\n",
+      "- This IS expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of RobertaForMultiLabelSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+      "/usr/local/lib/python3.7/dist-packages/simpletransformers/classification/classification_model.py:586: 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",
+      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "k7Cc_u5Oli7j"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "22e0c7f26e6e462ba7f63d02ed4cc1f0",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "# Rebuild training set (Task 2)"
+      "text/plain": [
+       "  0%|          | 0/1191 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "D2WLYT7wli7k"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1e8ea21a9e6457e8dcd35b21be75b22",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "rows2 = [] # will contain par_id, label and text\n",
-        "for idx in range(len(trids)):  \n",
-        "  parid = trids.par_id[idx]\n",
-        "  label = trids.label[idx]\n",
-        "  # select row from original dataset to retrieve the `text` value\n",
-        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
-        "  rows2.append({\n",
-        "      'par_id':parid,\n",
-        "      'text':text,\n",
-        "      'label':label\n",
-        "  })\n",
-        "  "
+      "text/plain": [
+       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "LFqMMb5Jli7l"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7473c3bed5854eacbc818363f4a1ab9a",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "trdf2 = pd.DataFrame(rows2)"
+      "text/plain": [
+       "Running Epoch 0 of 1:   0%|          | 0/149 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 422
-        },
-        "id": "HayrC9q7mQPl",
-        "outputId": "db5f1bdf-c09a-4a57-f81e-612100e32b44"
-      },
-      "outputs": [
-        {
-          "data": {
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-              "  <tbody>\n",
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-              "      <th>0</th>\n",
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-              "      <td>the scheme saw an estimated 150,000 children f...</td>\n",
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-              "      <th>1</th>\n",
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-              "      <td>durban 's homeless communities reconciliation ...</td>\n",
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-              "      <th>2</th>\n",
-              "      <td>10352</td>\n",
-              "      <td>the next immediate problem that cropped up was...</td>\n",
-              "      <td>[1, 0, 0, 0, 0, 1, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>8279</td>\n",
-              "      <td>far more important than the implications for t...</td>\n",
-              "      <td>[0, 0, 0, 1, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>1164</td>\n",
-              "      <td>to strengthen child-sensitive social protectio...</td>\n",
-              "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8370</th>\n",
-              "      <td>8380</td>\n",
-              "      <td>rescue teams search for survivors on the rubbl...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8371</th>\n",
-              "      <td>8381</td>\n",
-              "      <td>the launch of ' happy birthday ' took place la...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8372</th>\n",
-              "      <td>8382</td>\n",
-              "      <td>the unrest has left at least 20,000 people dea...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8373</th>\n",
-              "      <td>8383</td>\n",
-              "      <td>you have to see it from my perspective . i may...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8374</th>\n",
-              "      <td>8384</td>\n",
-              "      <td>yet there was one occasion when we went to the...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>8375 rows Ã— 3 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     par_id  ...                  label\n",
-              "0      4341  ...  [1, 0, 0, 1, 0, 0, 0]\n",
-              "1      4136  ...  [0, 1, 0, 0, 0, 0, 0]\n",
-              "2     10352  ...  [1, 0, 0, 0, 0, 1, 0]\n",
-              "3      8279  ...  [0, 0, 0, 1, 0, 0, 0]\n",
-              "4      1164  ...  [1, 0, 0, 1, 1, 1, 0]\n",
-              "...     ...  ...                    ...\n",
-              "8370   8380  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "8371   8381  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "8372   8382  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "8373   8383  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "8374   8384  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "\n",
-              "[8375 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 26,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "trdf2"
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:simpletransformers.classification.classification_model: Training of roberta model complete. Saved to outputs/.\n",
+      "INFO:simpletransformers.classification.classification_utils: Converting to features started. Cache is not used.\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "MxHLB_g0pfEb"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "31e1a0a3fe2f4228887c1abc7443e8ea",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "outputs": [],
-      "source": [
-        "trdf2.label = trdf2.label.apply(literal_eval)"
+      "text/plain": [
+       "  0%|          | 0/2094 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Gukbmv0bli7l"
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25fdc4ac2d714e5ba99b4f297726d36c",
+       "version_major": 2,
+       "version_minor": 0
       },
-      "source": [
-        "# Rebuild test set (Task 2)"
+      "text/plain": [
+       "  0%|          | 0/262 [00:00<?, ?it/s]"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "task2_model_args = MultiLabelClassificationArgs(num_train_epochs=1,\n",
+    "                                                no_save=True, \n",
+    "                                                no_cache=True, \n",
+    "                                                overwrite_output_dir=True\n",
+    "                                                )\n",
+    "task2_model = MultiLabelClassificationModel(\"roberta\", \n",
+    "                                            'roberta-base', \n",
+    "                                            num_labels=7,\n",
+    "                                            args = task2_model_args, \n",
+    "                                            use_cuda=cuda_available)\n",
+    "# train model\n",
+    "task2_model.train_model(training_set2[['text', 'label']])\n",
+    "# run predictions\n",
+    "preds_task2, _ = task2_model.predict(tedf2.text.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0211sxhsbbWZ"
+   },
+   "outputs": [],
+   "source": [
+    "labels2file(preds_task2, 'task2.txt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RT8hjnxbbfJq"
+   },
+   "source": [
+    "## Prepare submission"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "U7HICl8MJQf0",
+    "outputId": "794d8101-dd24-4dd5-b315-93c931a6b040"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gjH-AJK1li7m"
-      },
-      "outputs": [],
-      "source": [
-        "rows2 = [] # will contain par_id, label and text\n",
-        "for idx in range(len(teids)):  \n",
-        "  parid = teids.par_id[idx]\n",
-        "  label = teids.label[idx]\n",
-        "  #print(parid)\n",
-        "  # select row from original dataset to access the `text` value\n",
-        "  text = dpm.train_task1_df.loc[dpm.train_task1_df.par_id == parid].text.values[0]\n",
-        "  rows2.append({\n",
-        "      'par_id':parid,\n",
-        "      'text':text,\n",
-        "      'label':label\n",
-        "  })\n",
-        "  "
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "1\n",
+      "0\n",
+      "1\n",
+      "0\n",
+      "0\n",
+      "1\n",
+      "1\n",
+      "0\n",
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat task1.txt | head -n 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "qCjziGtxJRif",
+    "outputId": "aef99217-9b4d-46f7-f3b9-c9313bc165dc"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "SRP-tn5wli7n"
-      },
-      "outputs": [],
-      "source": [
-        "tedf2 = pd.DataFrame(rows2)"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,0,0,0,0,0,0\n",
+      "1,0,0,0,0,0,0\n",
+      "0,0,0,0,0,0,0\n",
+      "1,0,0,0,0,1,0\n",
+      "0,0,0,0,0,0,0\n",
+      "0,0,0,0,0,0,0\n",
+      "1,0,0,0,0,0,0\n",
+      "1,0,0,0,0,0,0\n",
+      "0,0,0,0,0,0,0\n",
+      "0,0,1,0,0,1,0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat task2.txt | head -n 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
     },
+    "id": "GZDLUcYZbhYg",
+    "outputId": "7586017d-83f2-4665-eb44-e2264201ac30"
+   },
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 422
-        },
-        "id": "8U2lrfJiolku",
-        "outputId": "6bf1181c-3e95-4913-cceb-9cc9e08b6c29"
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>par_id</th>\n",
-              "      <th>text</th>\n",
-              "      <th>label</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>4046</td>\n",
-              "      <td>we also know that they can benefit by receivin...</td>\n",
-              "      <td>[1, 0, 0, 1, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>1279</td>\n",
-              "      <td>pope francis washed and kissed the feet of mus...</td>\n",
-              "      <td>[0, 1, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>8330</td>\n",
-              "      <td>many refugees do n't want to be resettled anyw...</td>\n",
-              "      <td>[0, 0, 1, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>4063</td>\n",
-              "      <td>\"budding chefs , like \"\" fred \"\" , \"\" winston ...</td>\n",
-              "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>4089</td>\n",
-              "      <td>\"in a 90-degree view of his constituency , one...</td>\n",
-              "      <td>[1, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2089</th>\n",
-              "      <td>10462</td>\n",
-              "      <td>the sad spectacle , which occurred on saturday...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2090</th>\n",
-              "      <td>10463</td>\n",
-              "      <td>\"\"\" the pakistani police came to our house and...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2091</th>\n",
-              "      <td>10464</td>\n",
-              "      <td>\"when marie o'donoghue went looking for a spec...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2092</th>\n",
-              "      <td>10465</td>\n",
-              "      <td>\"sri lankan norms and culture inhibit women fr...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2093</th>\n",
-              "      <td>10466</td>\n",
-              "      <td>he added that the afp will continue to bank on...</td>\n",
-              "      <td>[0, 0, 0, 0, 0, 0, 0]</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>2094 rows Ã— 3 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "     par_id  ...                  label\n",
-              "0      4046  ...  [1, 0, 0, 1, 0, 0, 0]\n",
-              "1      1279  ...  [0, 1, 0, 0, 0, 0, 0]\n",
-              "2      8330  ...  [0, 0, 1, 0, 0, 0, 0]\n",
-              "3      4063  ...  [1, 0, 0, 1, 1, 1, 0]\n",
-              "4      4089  ...  [1, 0, 0, 0, 0, 0, 0]\n",
-              "...     ...  ...                    ...\n",
-              "2089  10462  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "2090  10463  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "2091  10464  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "2092  10465  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "2093  10466  ...  [0, 0, 0, 0, 0, 0, 0]\n",
-              "\n",
-              "[2094 rows x 3 columns]"
-            ]
-          },
-          "execution_count": 30,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  adding: task1.txt (deflated 92%)\n",
+      "  adding: task2.txt (deflated 97%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!zip submission.zip task1.txt task2.txt"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Reconstruct and RoBERTa baseline train-dev dataset.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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+    "name": "ipython",
+    "version": 3
+   },
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+   "mimetype": "text/x-python",
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-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
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-              "      <td>durban 's homeless communities reconciliation ...</td>\n",
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-              "      <td>far more important than the implications for t...</td>\n",
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-              "      <td>to strengthen child-sensitive social protectio...</td>\n",
-              "      <td>[1, 0, 0, 1, 1, 1, 0]</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>...</th>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
-              "      <td>...</td>\n",
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-              "    <tr>\n",
-              "      <th>1186</th>\n",
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-            "- This IS expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-            "- This IS NOT expected if you are initializing RobertaForMultiLabelSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
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-            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
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-        "task2_model_args = MultiLabelClassificationArgs(num_train_epochs=1,\n",
-        "                                                no_save=True, \n",
-        "                                                no_cache=True, \n",
-        "                                                overwrite_output_dir=True\n",
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-        "                                            num_labels=7,\n",
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-        "# train model\n",
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-        "# run predictions\n",
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