Premise_classification_RoBERTa_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2-xzsdVH0fyg",
   "metadata": {
    "id": "2-xzsdVH0fyg"
   },
   "source": [
    "## Premise Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DqXFEqeJ0fyk",
   "metadata": {
    "id": "DqXFEqeJ0fyk"
   },
   "source": [
    "Here is a simple baseline for Premise Classification using RoBERTa model from [Simple Transformers](https://simpletransformers.ai/) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "htfoLsac0pAT",
   "metadata": {
    "id": "htfoLsac0pAT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /home/gokulakrishnan/anaconda3/lib/python3.8/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /home/gokulakrishnan/anaconda3/lib/python3.8/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->simpletransformers) (3.2.0)\n",
      "Building wheels for collected packages: promise, pathtools, seqeval, validators\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=21494 sha256=298c6a4d4fa5fe7bc02413fd717ce251d6df21ec9d427a12c0cd240543aac542\n",
      "  Stored in directory: /home/gokulakrishnan/.cache/pip/wheels/54/aa/01/724885182f93150035a2a91bce34a12877e8067a97baaf5dc8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  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=8784 sha256=f5364179dbddc3ac7c089853d9e1174739fc1966c1f205054af42fb16d2e2143\n",
      "  Stored in directory: /home/gokulakrishnan/.cache/pip/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=16170 sha256=b23415a55afa246e671232f6aa1224ad0d0a8c75fc78adf43d97d78799928f9c\n",
      "  Stored in directory: /home/gokulakrishnan/.cache/pip/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n",
      "  Building wheel for validators (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for validators: filename=validators-0.20.0-py3-none-any.whl size=19567 sha256=9d2cd36f980854a1113859dca045b5431b593e10c752d0797fdd3e3298d1c68f\n",
      "  Stored in directory: /home/gokulakrishnan/.cache/pip/wheels/19/09/72/3eb74d236bb48bd0f3c6c3c83e4e0c5bbfcbcad7c6c3539db8\n",
      "Successfully built promise pathtools seqeval validators\n",
      "Installing collected packages: urllib3, multidict, frozenlist, yarl, tzdata, charset-normalizer, backports.zoneinfo, async-timeout, aiosignal, typing-extensions, tqdm, pytz-deprecation-shim, fsspec, dill, commonmark, aiohttp, xxhash, validators, tzlocal, tokenizers, shortuuid, setproctitle, sentry-sdk, semver, rich, responses, pympler, pydeck, pyarrow, promise, pathtools, multiprocess, huggingface-hub, docker-pycreds, altair, wandb, transformers, streamlit, seqeval, sentencepiece, datasets, simpletransformers\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 1.26.4\n",
      "    Uninstalling urllib3-1.26.4:\n",
      "      Successfully uninstalled urllib3-1.26.4\n",
      "  Attempting uninstall: typing-extensions\n",
      "    Found existing installation: typing-extensions 3.7.4.3\n",
      "    Uninstalling typing-extensions-3.7.4.3:\n",
      "      Successfully uninstalled typing-extensions-3.7.4.3\n",
      "  Attempting uninstall: tqdm\n",
      "    Found existing installation: tqdm 4.59.0\n",
      "    Uninstalling tqdm-4.59.0:\n",
      "      Successfully uninstalled tqdm-4.59.0\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 0.9.0\n",
      "    Uninstalling fsspec-0.9.0:\n",
      "      Successfully uninstalled fsspec-0.9.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "conda-repo-cli 1.0.4 requires pathlib, which is not installed.\n",
      "anaconda-project 0.9.1 requires ruamel-yaml, which is not installed.\u001b[0m\n",
      "Successfully installed aiohttp-3.8.3 aiosignal-1.2.0 altair-4.2.0 async-timeout-4.0.2 backports.zoneinfo-0.2.1 charset-normalizer-2.1.1 commonmark-0.9.1 datasets-2.5.1 dill-0.3.5.1 docker-pycreds-0.4.0 frozenlist-1.3.1 fsspec-2022.8.2 huggingface-hub-0.10.0 multidict-6.0.2 multiprocess-0.70.13 pathtools-0.1.2 promise-2.3 pyarrow-9.0.0 pydeck-0.8.0b3 pympler-1.0.1 pytz-deprecation-shim-0.1.0.post0 responses-0.18.0 rich-12.5.1 semver-2.13.0 sentencepiece-0.1.97 sentry-sdk-1.9.9 seqeval-1.2.2 setproctitle-1.3.2 shortuuid-1.0.9 simpletransformers-0.63.9 streamlit-1.13.0 tokenizers-0.12.1 tqdm-4.64.1 transformers-4.22.2 typing-extensions-4.3.0 tzdata-2022.4 tzlocal-4.2 urllib3-1.26.12 validators-0.20.0 wandb-0.13.3 xxhash-3.0.0 yarl-1.8.1\n"
     ]
    }
   ],
   "source": [
    "!pip install simpletransformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "QT7jxXDo0fyl",
   "metadata": {
    "id": "QT7jxXDo0fyl"
   },
   "outputs": [],
   "source": [
    "#import libs\n",
    "from simpletransformers.classification import ClassificationModel, ClassificationArgs\n",
    "import pandas as pd\n",
    "import logging\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "LB0GTeId0fyr",
   "metadata": {
    "id": "LB0GTeId0fyr"
   },
   "outputs": [],
   "source": [
    "#logging \n",
    "logging.basicConfig(level=logging.INFO)\n",
    "transformers_logger = logging.getLogger(\"transformers\")\n",
    "transformers_logger.setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iyqhjaPX0fys",
   "metadata": {
    "id": "iyqhjaPX0fys"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "IKdR_Nbb0fyu",
   "metadata": {
    "id": "IKdR_Nbb0fyu"
   },
   "outputs": [],
   "source": [
    "#read train dataset\n",
    "train = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "Mrd1WoG70fyv",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Mrd1WoG70fyv",
    "outputId": "ea3abc74-8e3b-4515-a639-7427bbbeed18"
   },
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Stance</th>\n",
       "      <th>labels</th>\n",
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       "      <td>@BBCNews 1. You are reporting 78% uptake on ma...</td>\n",
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       "      <td>@Leo_girl001 The D-Day is finally here. ...  Y...</td>\n",
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       "      <th>2755</th>\n",
       "      <td>825397</td>\n",
       "      <td>@FOX10Phoenix the boys &amp;amp; girls clubs are O...</td>\n",
       "      <td>school closures</td>\n",
       "      <td>AGAINST</td>\n",
       "      <td>1</td>\n",
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       "                 id                                               text  \\\n",
       "0           3054935  @realDonaldTrump It's too dangerous to hold th...   \n",
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       "2            211600  But if schools close who is gonna occupy those...   \n",
       "3           3049718  @GavinNewsom be the Governor I thought you wer...   \n",
       "4     5039679580998  @BBCNews 1. You are reporting 78% uptake on ma...   \n",
       "...             ...                                                ...   \n",
       "2751        2747057  @JohnCornyn Schools won't be safe until the vi...   \n",
       "2752     2120406711  @MorrisseyHelena @hughosmond @allisonpearson S...   \n",
       "2753        3006200  My family is living in fear because my brother...   \n",
       "2754     2400838291  @Leo_girl001 The D-Day is finally here. ...  Y...   \n",
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       "1              face masks  AGAINST       1  \n",
       "2         school closures    FAVOR       1  \n",
       "3     stay at home orders    FAVOR       1  \n",
       "4              face masks    FAVOR       1  \n",
       "...                   ...      ...     ...  \n",
       "2751      school closures    FAVOR       1  \n",
       "2752           face masks     NONE       1  \n",
       "2753      school closures    FAVOR       1  \n",
       "2754  stay at home orders     NONE       0  \n",
       "2755      school closures  AGAINST       1  \n",
       "\n",
       "[2756 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rename columns to match SimpleTransformers format\n",
    "column_names = {'Tweet': 'text', 'Premise':'labels'}\n",
    "train = train.rename(columns = column_names)\n",
    "\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SkWKBbE50fyw",
   "metadata": {
    "id": "SkWKBbE50fyw"
   },
   "source": [
    "Let's train our model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "mCU0zgrn0fyx",
   "metadata": {
    "id": "mCU0zgrn0fyx"
   },
   "outputs": [],
   "source": [
    "# Optional model configuration\n",
    "model_args = ClassificationArgs(num_train_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "K-NKfT3C0fy0",
   "metadata": {
    "colab": {
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    "id": "K-NKfT3C0fy0",
    "outputId": "f75b03a5-6f9b-4d1b-ee6e-03bdd42bf390"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'roberta.pooler.dense.bias']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.dense.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.out_proj.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": {
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       "model_id": "e8c82e7197cc450380122a798bb148ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2756 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9889888690784b9b8716a9a1b0a284b7",
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       "version_minor": 0
      },
      "text/plain": [
       "Epoch:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Running Epoch 0 of 1:   0%|          | 0/345 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 44.6 s, sys: 8.52 s, total: 53.1 s\n",
      "Wall time: 1min 10s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(345, 0.540279657944389)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model = ClassificationModel(\n",
    "    \"roberta\", \"roberta-base\", args=model_args\n",
    ")\n",
    "model.train_model(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "G1DiT3tf0fy1",
   "metadata": {
    "id": "G1DiT3tf0fy1"
   },
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "gG8M8sOO0fy2",
   "metadata": {
    "id": "gG8M8sOO0fy2"
   },
   "outputs": [],
   "source": [
    "#read validation dataset\n",
    "val = pd.read_csv('data/validation.csv')\n",
    "\n",
    "#rename columns and values to match the format\n",
    "val = val.rename(columns=column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9QNYH_5112k",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "a9QNYH_5112k",
    "outputId": "146ab015-5e2b-4403-f569-e0417b68c3a6"
   },
   "outputs": [
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       "      <th>Claim</th>\n",
       "      <th>Stance</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7212131785749</td>\n",
       "      <td>Ordered a mask that had a cute chain attached ...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2367433035</td>\n",
       "      <td>Who is ready for some #baseball?  #BaseballisB...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>NONE</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>496011</td>\n",
       "      <td>@Mystere07623203 @va_shiva @Liberty13046 130,0...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1490686</td>\n",
       "      <td>When they ask me what I did with my life I wil...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1209366</td>\n",
       "      <td>Taylor not putting her album in physical store...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>2949472</td>\n",
       "      <td>@BrianKempGA is one of the few #Governors that...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>AGAINST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>208269</td>\n",
       "      <td>I tested positive for Covid. Got it from a fri...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2316008</td>\n",
       "      <td>If you are under retirement age, you have a *m...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>AGAINST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>659269</td>\n",
       "      <td>@TheLalasventure @doqholliday @realDonaldTrump...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1940282</td>\n",
       "      <td>@Caissie Obviously I was simply practicing my ...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 5 columns</p>\n",
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      "text/plain": [
       "                id                                               text  \\\n",
       "0    7212131785749  Ordered a mask that had a cute chain attached ...   \n",
       "1       2367433035  Who is ready for some #baseball?  #BaseballisB...   \n",
       "2           496011  @Mystere07623203 @va_shiva @Liberty13046 130,0...   \n",
       "3          1490686  When they ask me what I did with my life I wil...   \n",
       "4          1209366  Taylor not putting her album in physical store...   \n",
       "..             ...                                                ...   \n",
       "595        2949472  @BrianKempGA is one of the few #Governors that...   \n",
       "596         208269  I tested positive for Covid. Got it from a fri...   \n",
       "597        2316008  If you are under retirement age, you have a *m...   \n",
       "598         659269  @TheLalasventure @doqholliday @realDonaldTrump...   \n",
       "599        1940282  @Caissie Obviously I was simply practicing my ...   \n",
       "\n",
       "                   Claim   Stance  labels  \n",
       "0             face masks    FAVOR       0  \n",
       "1    stay at home orders     NONE       0  \n",
       "2             face masks    FAVOR       1  \n",
       "3             face masks    FAVOR       0  \n",
       "4    stay at home orders    FAVOR       0  \n",
       "..                   ...      ...     ...  \n",
       "595           face masks  AGAINST       1  \n",
       "596           face masks    FAVOR       1  \n",
       "597  stay at home orders  AGAINST       1  \n",
       "598           face masks    FAVOR       0  \n",
       "599           face masks    FAVOR       0  \n",
       "\n",
       "[600 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "xyiBNOu50fy3",
   "metadata": {
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    },
    "id": "xyiBNOu50fy3",
    "outputId": "e2eb4407-3197-4b6d-ff73-1bfe1d533fb2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1ca08e3aa0e4054b452daf8111841d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/600 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6ccba9a92e34e1094d1eb9ce6e68445",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Running Evaluation:   0%|          | 0/75 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "{'mcc': 0.5849057224361446,\n",
       " 'tp': 170,\n",
       " 'tn': 312,\n",
       " 'fp': 68,\n",
       " 'fn': 50,\n",
       " 'auroc': 0.8847906698564593,\n",
       " 'auprc': 0.7783776266780602,\n",
       " 'eval_loss': 0.40849300384521486}"
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     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "result, model_outputs, wrong_predictions = model.eval_model(val)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "inFTZprD0fy5",
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    "id": "inFTZprD0fy5",
    "outputId": "95858130-aebe-48f3-8dda-45de2d20ebc6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6ab71424e514d32a52e2edad29f3243",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/599 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dcb32116f0c4fd09c9b061d112e5c80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/75 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#Make predictions on validation tweets\n",
    "val = val.dropna()\n",
    "tweets_val = list(val['text'])\n",
    "stance_predictions = model.predict(tweets_val)[0]\n",
    "val['predictions'] = stance_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "wySx-U0Q4iUL",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "wySx-U0Q4iUL",
    "outputId": "ac940952-fb0a-465c-d417-18a930fd36ee"
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   "outputs": [
    {
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       "      <td>@Caissie Obviously I was simply practicing my ...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>FAVOR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>599 rows × 6 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8be81b3-c989-49e2-a4dd-1d84f36da31d')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b8be81b3-c989-49e2-a4dd-1d84f36da31d button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b8be81b3-c989-49e2-a4dd-1d84f36da31d');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                id                                               text  \\\n",
       "0    7212131785749  Ordered a mask that had a cute chain attached ...   \n",
       "1       2367433035  Who is ready for some #baseball?  #BaseballisB...   \n",
       "2           496011  @Mystere07623203 @va_shiva @Liberty13046 130,0...   \n",
       "3          1490686  When they ask me what I did with my life I wil...   \n",
       "4          1209366  Taylor not putting her album in physical store...   \n",
       "..             ...                                                ...   \n",
       "595        2949472  @BrianKempGA is one of the few #Governors that...   \n",
       "596         208269  I tested positive for Covid. Got it from a fri...   \n",
       "597        2316008  If you are under retirement age, you have a *m...   \n",
       "598         659269  @TheLalasventure @doqholliday @realDonaldTrump...   \n",
       "599        1940282  @Caissie Obviously I was simply practicing my ...   \n",
       "\n",
       "                   Claim   Stance  labels  predictions  \n",
       "0             face masks    FAVOR       0            0  \n",
       "1    stay at home orders     NONE       0            0  \n",
       "2             face masks    FAVOR       1            1  \n",
       "3             face masks    FAVOR       0            0  \n",
       "4    stay at home orders    FAVOR       0            0  \n",
       "..                   ...      ...     ...          ...  \n",
       "595           face masks  AGAINST       1            0  \n",
       "596           face masks    FAVOR       1            1  \n",
       "597  stay at home orders  AGAINST       1            1  \n",
       "598           face masks    FAVOR       0            0  \n",
       "599           face masks    FAVOR       0            0  \n",
       "\n",
       "[599 rows x 6 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "-_zX_INR0fy6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "-_zX_INR0fy6",
    "outputId": "2c7cb7a9-a176-45a2-ff00-157891094975"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's look at model's predictions distribution\n",
    "confusion_matrix = pd.crosstab(val['labels'], val['predictions'], rownames=['Actual'], colnames=['Predicted'])\n",
    "\n",
    "sn.heatmap(confusion_matrix, annot=True, fmt='d')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "576p7-bR0fy8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "576p7-bR0fy8",
    "outputId": "a12a86c0-1725-425d-d9ae-cf67579b755a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c737c73f-c0c3-44a9-b069-7606d2f0d3aa\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>id</th>\n",
       "      <th>text</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Premise</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7212131785749</td>\n",
       "      <td>Ordered a mask that had a cute chain attached ...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2367433035</td>\n",
       "      <td>Who is ready for some #baseball?  #BaseballisB...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>496011</td>\n",
       "      <td>@Mystere07623203 @va_shiva @Liberty13046 130,0...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1490686</td>\n",
       "      <td>When they ask me what I did with my life I wil...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1209366</td>\n",
       "      <td>Taylor not putting her album in physical store...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>2949472</td>\n",
       "      <td>@BrianKempGA is one of the few #Governors that...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>208269</td>\n",
       "      <td>I tested positive for Covid. Got it from a fri...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2316008</td>\n",
       "      <td>If you are under retirement age, you have a *m...</td>\n",
       "      <td>stay at home orders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>659269</td>\n",
       "      <td>@TheLalasventure @doqholliday @realDonaldTrump...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>1940282</td>\n",
       "      <td>@Caissie Obviously I was simply practicing my ...</td>\n",
       "      <td>face masks</td>\n",
       "      <td>0</td>\n",
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       "  "
      ],
      "text/plain": [
       "                id                                               text  \\\n",
       "0    7212131785749  Ordered a mask that had a cute chain attached ...   \n",
       "1       2367433035  Who is ready for some #baseball?  #BaseballisB...   \n",
       "2           496011  @Mystere07623203 @va_shiva @Liberty13046 130,0...   \n",
       "3          1490686  When they ask me what I did with my life I wil...   \n",
       "4          1209366  Taylor not putting her album in physical store...   \n",
       "..             ...                                                ...   \n",
       "595        2949472  @BrianKempGA is one of the few #Governors that...   \n",
       "596         208269  I tested positive for Covid. Got it from a fri...   \n",
       "597        2316008  If you are under retirement age, you have a *m...   \n",
       "598         659269  @TheLalasventure @doqholliday @realDonaldTrump...   \n",
       "599        1940282  @Caissie Obviously I was simply practicing my ...   \n",
       "\n",
       "                   Claim  Premise  \n",
       "0             face masks        0  \n",
       "1    stay at home orders        0  \n",
       "2             face masks        1  \n",
       "3             face masks        0  \n",
       "4    stay at home orders        0  \n",
       "..                   ...      ...  \n",
       "595           face masks        0  \n",
       "596           face masks        1  \n",
       "597  stay at home orders        1  \n",
       "598           face masks        0  \n",
       "599           face masks        0  \n",
       "\n",
       "[599 rows x 4 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val = val.drop(columns=['labels', 'Stance']).rename(columns={'predictions':\n",
    "                                                      'Premise'})\n",
    "val"
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