From 66088117feb4b0706ad62a3e41c38412dd2cb20c Mon Sep 17 00:00:00 2001
From: Emily Haw <haw.emily@gmail.com>
Date: Tue, 1 Mar 2022 15:44:26 +0000
Subject: [PATCH] add code without merge problems

---
 ...ERTa_baseline_train_dev_dataset copy.ipynb | 8329 +++++++++++++++++
 1 file changed, 8329 insertions(+)
 create mode 100644 Reconstruct_and_RoBERTa_baseline_train_dev_dataset copy.ipynb

diff --git a/Reconstruct_and_RoBERTa_baseline_train_dev_dataset copy.ipynb b/Reconstruct_and_RoBERTa_baseline_train_dev_dataset copy.ipynb
new file mode 100644
index 0000000..369fd39
--- /dev/null
+++ b/Reconstruct_and_RoBERTa_baseline_train_dev_dataset copy.ipynb	
@@ -0,0 +1,8329 @@
+{
+  "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": [
+            "Tue Mar  1 11:31:44 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 M4000        Off  | 00000000:00:05.0 Off |                  N/A |\n",
+            "| 46%   27C    P8    12W / 120W |      0MiB /  8126MiB |      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"
+          ]
+        }
+      ],
+      "source": [
+        "# check which gpu we're using\n",
+        "!nvidia-smi"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "hYhFR7nSYOjG",
+        "outputId": "23ed0686-29d3-45ff-dc22-b2fe54e86ec4"
+      },
+      "outputs": [
+        {
+          "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",
+            "\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| 248 kB 26.5 MB/s eta 0:00:01\n",
+            "\u001b[?25hCollecting tensorflow\n",
+            "  Downloading tensorflow-2.8.0-cp38-cp38-manylinux2010_x86_64.whl (497.6 MB)\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| 497.6 MB 24.2 MB/s eta 0:00:01   |â–‹                               | 9.7 MB 37.2 MB/s eta 0:00:14     |â–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–‰            | 309.2 MB 23.9 MB/s eta 0:00:08     |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–ˆ   | 452.3 MB 24.9 MB/s eta 0:00:02\n",
+            "\u001b[?25hRequirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from simpletransformers) (1.21.2)\n",
+            "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from simpletransformers) (2.26.0)\n",
+            "Requirement already satisfied: tqdm>=4.47.0 in /opt/conda/lib/python3.8/site-packages (from simpletransformers) (4.62.3)\n",
+            "Collecting datasets\n",
+            "  Downloading datasets-1.18.3-py3-none-any.whl (311 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| 311 kB 31.6 MB/s eta 0:00:01\n",
+            "\u001b[?25hRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from simpletransformers) (1.0)\n",
+            "Collecting seqeval\n",
+            "  Downloading seqeval-1.2.2.tar.gz (43 kB)\n",
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+            "\u001b[?25hCollecting streamlit\n",
+            "  Downloading streamlit-1.6.0-py2.py3-none-any.whl (9.7 MB)\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| 9.7 MB 36.0 MB/s eta 0:00:01\n",
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+            "Collecting wandb>=0.10.32\n",
+            "  Downloading wandb-0.12.10-py2.py3-none-any.whl (1.7 MB)\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| 1.7 MB 30.6 MB/s eta 0:00:01\n",
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+            "  Downloading keras-2.8.0-py2.py3-none-any.whl (1.4 MB)\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| 1.4 MB 28.0 MB/s eta 0:00:01\n",
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+            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 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| 42 kB 31.9 MB/s eta 0:00:01\n",
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+            "Collecting tf-estimator-nightly==2.8.0.dev2021122109\n",
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+            "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: 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: 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: 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: 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: 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: 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: 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 35.7 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 45.9 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=c490c0105be70b729d7a26de3e6628e3b531e4ab3a5bb8ec4005c4cecda96504\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-9g_c4y_8/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=19c9cb9fff75124d0e814bd26f15f71a1a1a2b7a93587487a8471fb61eb0512c\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-9g_c4y_8/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=fe96f66170e7602a5a085bd804c9fc52e8d630dd03953f794b7a03db2c0f1f7b\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-9g_c4y_8/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=efb4afac83cee1e4862c210b77c31b659136a35b442e4eddb18e7752b6693e93\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-9g_c4y_8/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=79fd2d88eb00b6a17999dc47d7c7793b40e2400833855f9a4c2041d67764de14\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-9g_c4y_8/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, ipywidgets, gitdb, fsspec, dill, aiohttp, yaspin, xxhash, watchdog, validators, tzlocal, shortuuid, sentry-sdk, semver, pympler, pydeck, pyarrow, promise, pathtools, multiprocess, importlib-metadata, GitPython, docker-pycreds, blinker, base58, astor, altair, wrapt, wandb, tf-estimator-nightly, tensorflow-io-gcs-filesystem, tensorboard, streamlit, seqeval, 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 importlib-metadata-4.11.2 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 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 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 toolz-0.11.2 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 19.5 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: protobuf>=3.8.0 in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (3.18.1)\n",
+            "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from tensorboardx) (1.21.2)\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": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+            "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (1.21.2)\n",
+            "Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (2.26.0)\n",
+            "Collecting nlpaug\n",
+            "  Downloading nlpaug-1.1.10-py3-none-any.whl (410 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| 410 kB 21.0 MB/s eta 0:00:01\n",
+            "\u001b[?25hRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests) (1.26.7)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests) (3.1)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.8/site-packages (from requests) (2.0.0)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests) (2021.5.30)\n",
+            "Collecting pandas>=1.2.0\n",
+            "  Downloading pandas-1.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.7 MB)\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| 11.7 MB 31.3 MB/s eta 0:00:01\n",
+            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.8/site-packages (from pandas>=1.2.0->nlpaug) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.8/site-packages (from pandas>=1.2.0->nlpaug) (2021.3)\n",
+            "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas>=1.2.0->nlpaug) (1.16.0)\n",
+            "Installing collected packages: pandas, nlpaug\n",
+            "Successfully installed nlpaug-1.1.10 pandas-1.4.1\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",
+            "\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",
+            "\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 numpy requests nlpaug\n",
+        "!pip install torch>=1.6.0 transformers>=4.11.3 sentencepiece\n",
+        "!pip install nltk>-3.4.5"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "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": 5,
+      "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)"
+      ]
+    },
+    {
+      "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": [
+            "2022-03-01 12:15:47.331960: 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",
+            "2022-03-01 12:15:47.333216: 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",
+            "2022-03-01 12:15:47.334375: 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",
+            "2022-03-01 12:15:47.335463: 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",
+            "2022-03-01 12:15:50.556457: 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",
+            "2022-03-01 12:15:50.557050: 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",
+            "2022-03-01 12:15:50.557563: 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",
+            "2022-03-01 12:15:50.558143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /device:GPU:0 with 7001 MB memory:  -> device: 0, name: Quadro M4000, pci bus id: 0000:00:05.0, compute capability: 5.2\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": [
+        {
+          "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": 13,
+      "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"
+          ]
+        }
+      ],
+      "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": 14,
+      "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": 15,
+      "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": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trids.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "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": 16,
+      "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": 17,
+      "metadata": {
+        "id": "8e3E08Yown5p"
+      },
+      "outputs": [],
+      "source": [
+        "trdf1 = pd.DataFrame(rows)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "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",
+              "    </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": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trdf1.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "8375"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trdf1.shape[0]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0    7581\n",
+              "1     794\n",
+              "Name: label, dtype: int64"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trdf1[\"label\"].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preprocessing of data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:matplotlib.font_manager:generated new fontManager\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\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": [
+        "from nlpaug.util.file.download import DownloadUtil\n",
+        "DownloadUtil.download_word2vec(dest_dir='.') # Download word2vec model\n",
+        "# DownloadUtil.download_glove(model_name='glove.6B', dest_dir='.') # Download GloVe model\n",
+        "# DownloadUtil.download_fasttext(model_name='wiki-news-300d-1M', dest_dir='.') # Download fasttext model\n",
+        "\n",
+        "!pip install gensim>=4.1.2\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import nlpaug.augmenter.word as naw\n",
+        "import nlpaug.augmenter.sentence as nas "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"MODEL_DIR\"] = '../model'\n",
+        "model_dir = '../model'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The scheme saw an estimated 150,000 children from poor families being sent to parts of the British Empire between 1920 and 1974 , by religious orders and charities who said they would lead better lives .\n"
+          ]
+        }
+      ],
+      "source": [
+        "text = trdf1[\"text\"][0]\n",
+        "print(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+            "Collecting wget\n",
+            "  Downloading wget-3.2.zip (10 kB)\n",
+            "Building wheels for collected packages: wget\n",
+            "  Building wheel for wget (setup.py) ... \u001b[?25ldone\n",
+            "\u001b[?25h  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9672 sha256=390fdfe2916c94d3a54c5a47d4ec17cf45d640db731ecef267039deb53667c24\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-4nync2gp/wheels/bd/a8/c3/3cf2c14a1837a4e04bd98631724e81f33f462d86a1d895fae0\n",
+            "Successfully built wget\n",
+            "Installing collected packages: wget\n",
+            "Successfully installed wget-3.2\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 wget\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model at GoogleNews-vectors-negative300.bin\n"
+          ]
+        }
+      ],
+      "source": [
+        "import gzip\n",
+        "import shutil\n",
+        "import wget\n",
+        "\n",
+        "gn_vec_path = \"GoogleNews-vectors-negative300.bin\"\n",
+        "if not os.path.exists(\"GoogleNews-vectors-negative300.bin\"):\n",
+        "    if not os.path.exists(\"../Ch3/GoogleNews-vectors-negative300.bin\"):\n",
+        "        # Downloading the reqired model\n",
+        "        if not os.path.exists(\"../Ch3/GoogleNews-vectors-negative300.bin.gz\"):\n",
+        "            if not os.path.exists(\"GoogleNews-vectors-negative300.bin.gz\"):\n",
+        "                wget.download(\"https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\")\n",
+        "            gn_vec_zip_path = \"GoogleNews-vectors-negative300.bin.gz\"\n",
+        "        else:\n",
+        "            gn_vec_zip_path = \"../Ch3/GoogleNews-vectors-negative300.bin.gz\"\n",
+        "        # Extracting the required model\n",
+        "        with gzip.open(gn_vec_zip_path, 'rb') as f_in:\n",
+        "            with open(gn_vec_path, 'wb') as f_out:\n",
+        "                shutil.copyfileobj(f_in, f_out)\n",
+        "    else:\n",
+        "        gn_vec_path = \"../Ch3/\" + gn_vec_path\n",
+        "\n",
+        "print(f\"Model at {gn_vec_path}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# use model pre trained word embeddings to insert word randomly\n",
+        "sub_aug = naw.WordEmbsAug(\n",
+        "    model_type='word2vec', model_path='/notebooks/GoogleNews-vectors-negative300.bin',\n",
+        "    action=\"substitute\")\n",
+        "\n",
+        "substitute_txt = sub_aug.augment(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yet there was one occasion when COACH_BLUDER went to the scene of the triple murder. The woman who taken care of her two disabled children, wasn killed bÑƒ robbers. Just put themeselves all against the wall and Lidstrom_slap but the coach. You know, when you have to clorox the blood from children ' s Barbie_dolls_Hot_Wheels and Cribs, it ' s creepy.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(substitute_txt)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "add_aug = naw.WordEmbsAug(\n",
+        "    model_type='word2vec', model_path='/notebooks/GoogleNews-vectors-negative300.bin',\n",
+        "    action=\"substitute\")\n",
+        "\n",
+        "add_txt = add_aug.augment(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yet there was one occasion when we went to the scene of the triple murder . The woman who took care of her two disabled children , was killed by robbers . Just put them all against the wall and shot in the head . You know , when you have to scrub the blood from children 's toys and Cribs , it 's creepy .\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yet LG there was one occasion when we went activists to the scene of the triple Darren murder. The Tra woman who took care of her two disabled children, was killed by ROB robbers. Just put them Forthill all against the wall and shot in the head. You know, when iVendix you have Feargal to scrub the blood still from children ' Skywards s toys and Cribs, it ' s creepy.\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(augmented_txt)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "DEBUG:filelock:Attempting to acquire lock 140461694723792 on /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
+            "DEBUG:filelock:Lock 140461694723792 acquired on /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8672c8f59f3b4faa94c1b73b3f4ef4aa",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "DEBUG:filelock:Attempting to release lock 140461694723792 on /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
+            "DEBUG:filelock:Lock 140461694723792 released on /root/.cache/huggingface/transformers/c1d7f0a763fb63861cc08553866f1fc3e5a6f4f07621be277452d26d71303b7e.20430bd8e10ef77a7d2977accefe796051e01bc2fc4aa146bc862997a1a15e79.lock\n",
+            "DEBUG:filelock:Attempting to acquire lock 140461694723792 on /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e.lock\n",
+            "DEBUG:filelock:Lock 140461694723792 acquired on /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e.lock\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "79598aef988247efb2461eddd0183a68",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "DEBUG:filelock:Attempting to release lock 140461694723792 on /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e.lock\n",
+            "DEBUG:filelock:Lock 140461694723792 released on /root/.cache/huggingface/transformers/3c61d016573b14f7f008c02c4e51a366c67ab274726fe2910691e2a761acf43e.37395cee442ab11005bcd270f3c34464dc1704b715b5d7d52b1a461abe3b9e4e.lock\n",
+            "DEBUG:filelock:Attempting to acquire lock 140457839724240 on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
+            "DEBUG:filelock:Lock 140457839724240 acquired on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n"
+          ]
+        },
+        {
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+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
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+            "DEBUG:filelock:Lock 140457839724240 released on /root/.cache/huggingface/transformers/45c3f7a79a80e1cf0a489e5c62b43f173c15db47864303a55d623bb3c96f72a5.d789d64ebfe299b0e416afc4a169632f903f693095b4629a7ea271d5a0cf2c99.lock\n",
+            "DEBUG:filelock:Attempting to acquire lock 140457840166608 on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
+            "DEBUG:filelock:Lock 140457840166608 acquired on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n"
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "DEBUG:filelock:Attempting to release lock 140457840166608 on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
+            "DEBUG:filelock:Lock 140457840166608 released on /root/.cache/huggingface/transformers/534479488c54aeaf9c3406f647aa2ec13648c06771ffe269edabebd4c412da1d.7f2721073f19841be16f41b0a70b600ca6b880c8f3df6f3535cbc704371bdfa4.lock\n",
+            "DEBUG:filelock:Attempting to acquire lock 140463416449968 on /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f.lock\n",
+            "DEBUG:filelock:Lock 140463416449968 acquired on /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f.lock\n"
+          ]
+        },
+        {
+          "data": {
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+              "model_id": "12b7ff3d5a0d43ae883ae63782957db9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/420M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "DEBUG:filelock:Attempting to release lock 140463416449968 on /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f.lock\n",
+            "DEBUG:filelock:Lock 140463416449968 released on /root/.cache/huggingface/transformers/a8041bf617d7f94ea26d15e218abd04afc2004805632abc0ed2066aa16d50d04.faf6ea826ae9c5867d12b22257f9877e6b8367890837bd60f7c54a29633f7f2f.lock\n"
+          ]
+        }
+      ],
+      "source": [
+        "wordContextAug = naw.ContextualWordEmbsAug(\n",
+        "    model_path='bert-base-uncased', action=\"insert\"\n",
+        ")\n",
+        "\n",
+        "word_aug_txt = wordContextAug.augment(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "wordContextAug_subs = naw.ContextualWordEmbsAug(\n",
+        "    model_path='bert-base-uncased', action=\"substitute\"\n",
+        ")\n",
+        "\n",
+        "word_aug_sub_txt = wordContextAug.augment(text)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Yet there was one occasion when we went to the scene of the triple murder . The woman who took care of her two disabled children , was killed by robbers . Just put them all against the wall and shot in the head . You know , when you have to scrub the blood from children 's toys and Cribs , it 's creepy .\n",
+            "66\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(text)\n",
+        "print(len(text.split(\" \")))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "well yet there was the one occasion when we went to the scene of the classic triple murder. how the old woman who took great care of her three two disabled children, was basically killed by robbers. just put them all against the wall and shot in back the head. you know, when you have to scrub the blood from children's used toys and cribs, it's creepy.\n",
+            "67\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(word_aug_sub_txt)\n",
+        "print(len(word_aug_sub_txt.split(\" \")))"
+      ]
+    },
+    {
+      "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": 94,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
+            "Requirement already satisfied: py-readability-metrics in /opt/conda/lib/python3.8/site-packages (1.4.5)\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",
+            "\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": 98,
+      "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": 30,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
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+              "\n",
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+              "</style>\n",
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+              "      <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",
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+              "      <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",
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+              "    <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": 30,
+          "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": 99,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "\n",
+        "trdf1[\"readability_score\"] = trdf1[\"text\"].apply(lambda x: calculate_readability(x))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "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": 32,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "trdf1[\"avg_sentence_length\"] = paragraphs.apply(lambda x : avg_sentence_length(x))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 101,
+      "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": 101,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trdf1[\"readability_score\"].describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "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>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>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>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>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>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>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  avg_sentence_length  \n",
+              "0                           2            18.500000  \n",
+              "1                           1             6.000000  \n",
+              "2                           3            24.666667  \n",
+              "3                           2            23.000000  \n",
+              "4                           2            25.000000  "
+            ]
+          },
+          "execution_count": 34,
+          "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": 52,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "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>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>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  avg_sentence_length  \n",
+              "7590                           1                133.0  "
+            ]
+          },
+          "execution_count": 82,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "trdf1[trdf1[\"avg_sentence_length\"] == 133]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 77,
+      "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()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "O1KGYmpnxDjt"
+      },
+      "source": [
+        "# Rebuild test set (Task 1)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YbB9GdzJxRAH",
+        "outputId": "c78e311e-9502-4644-b6f7-0c64f64aa66f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "2094"
+            ]
+          },
+          "execution_count": 17,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "len(rows)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z-pvjbu_8h1n",
+        "outputId": "0a9da7ae-181c-40a5-a438-220f5ab960b5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
+          ]
+        }
+      ],
+      "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": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "mpSqMp3d8iYu",
+        "outputId": "037d4f45-eab5-4f04-e9a5-1aa64c46323d"
+      },
+      "outputs": [
+        {
+          "data": {
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+              "</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",
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+              "    </tr>\n",
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+              "      <td>1164</td>\n",
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+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
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+              "      <td>...</td>\n",
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+              "    <tr>\n",
+              "      <th>2378</th>\n",
+              "      <td>1776</td>\n",
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+              "      <td>0</td>\n",
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+              "    <tr>\n",
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+              "    <tr>\n",
+              "      <th>2380</th>\n",
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+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>2382 rows Ã— 3 columns</p>\n",
+              "</div>"
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+              "     par_id                                               text  label\n",
+              "0      4341  the scheme saw an estimated 150,000 children f...      1\n",
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+              "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",
+              "[2382 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
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+        "id": "PoW_s23AZ_DG",
+        "outputId": "cae26801-a680-4d5b-9d76-d56872b5e611"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "de026e2d1ec848fbb35faf1746e7579d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a90282c995cc4c6db59eaa51e3414ad1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/478M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.weight', 'lm_head.decoder.weight']\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.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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "df46941823a540f787e2e9dad52243d3",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "59458022ec4547beba130ce755946427",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "2c3f59321aac4ea78e4c70618f907e08",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "b8654137c93f4a70b0fb8f7e692f7673",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/2382 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d32db030dff24fe3bd1983a44c703311",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "82f733de38ef4c5f8734cbdf47ec1a01",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Running Epoch 0 of 1:   0%|          | 0/298 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a308011d2efd403fb6b8d250d8f98fa9",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/2094 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6851a6f291a04bd59da6cf04d6f762aa",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/262 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\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": [
+        {
+          "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": {
+        "id": "kzupJuwTafKa"
+      },
+      "outputs": [],
+      "source": [
+        "labels2file([[k] for k in preds_task1], 'task1.txt')"
+      ]
+    },
+    {
+      "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": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\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, 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>"
+            ],
+            "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": [
+        {
+          "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"
+        }
+      ],
+      "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": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\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",
+              "    </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>"
+            ],
+            "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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+        },
+        "id": "ECb7_mwzbFa6",
+        "outputId": "d433e1aa-c3c4-4a00-9b2a-c04ccdc20588"
+      },
+      "outputs": [
+        {
+          "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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "22e0c7f26e6e462ba7f63d02ed4cc1f0",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/1191 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f1e8ea21a9e6457e8dcd35b21be75b22",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7473c3bed5854eacbc818363f4a1ab9a",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Running Epoch 0 of 1:   0%|          | 0/149 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/2094 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "25fdc4ac2d714e5ba99b4f297726d36c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "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": [
+        {
+          "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": [
+        {
+          "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": [
+        {
+          "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": []
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+      "language": "python",
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GitLab