Stance_detection_RoBERTa_baseline.ipynb

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   "cell_type": "markdown",
   "id": "L0lN3vS45Jv1",
   "metadata": {
    "id": "L0lN3vS45Jv1"
   },
   "source": [
    "## Stance detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "xWh5Ype45Jv7",
   "metadata": {
    "id": "xWh5Ype45Jv7"
   },
   "source": [
    "Here is a simple baseline for stance detection using RoBERTa model from [Simple Transformers](https://simpletransformers.ai/) library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nJUFJb-S5nEC",
   "metadata": {
    "id": "nJUFJb-S5nEC"
   },
   "outputs": [],
   "source": [
    "!pip install simpletransformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dgVuIvT75Jv8",
   "metadata": {
    "id": "dgVuIvT75Jv8"
   },
   "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": 2,
   "id": "WHKZ7_DU5Jv_",
   "metadata": {
    "id": "WHKZ7_DU5Jv_"
   },
   "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": "s7rUhb1f5JwC",
   "metadata": {
    "id": "s7rUhb1f5JwC"
   },
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "YfQVKaea5JwD",
   "metadata": {
    "id": "YfQVKaea5JwD"
   },
   "outputs": [],
   "source": [
    "#read train dataset\n",
    "train = pd.read_csv('data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "gnWVdxSf5JwF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "gnWVdxSf5JwF",
    "outputId": "50a51c5c-d0b9-4904-cb94-980df9308e94"
   },
   "outputs": [
    {
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       "      <th>id</th>\n",
       "      <th>text</th>\n",
       "      <th>Claim</th>\n",
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       "  </thead>\n",
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       "      <td>@BBCNews 1. You are reporting 78% uptake on ma...</td>\n",
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       "                 id                                               text  \\\n",
       "0           3054935  @realDonaldTrump It's too dangerous to hold th...   \n",
       "1            453444  If masks are so important why is the public no...   \n",
       "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",
       "2755         825397  @FOX10Phoenix the boys &amp; girls clubs are O...   \n",
       "\n",
       "                    Claim labels  Premise  \n",
       "0         school closures      1        1  \n",
       "1              face masks      0        1  \n",
       "2         school closures      1        1  \n",
       "3     stay at home orders      1        1  \n",
       "4              face masks      1        1  \n",
       "...                   ...    ...      ...  \n",
       "2751      school closures      1        1  \n",
       "2752           face masks      2        1  \n",
       "2753      school closures      1        1  \n",
       "2754  stay at home orders      2        0  \n",
       "2755      school closures      0        1  \n",
       "\n",
       "[2756 rows x 5 columns]"
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     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "#rename columns to match SimpleTransformers format\n",
    "column_names = {'Tweet': 'text', 'Stance':'labels'}\n",
    "train = train.rename(columns = column_names)\n",
    "\n",
    "#change labels to match SimpleTransformers format\n",
    "train.loc[train[\"labels\"] == \"AGAINST\", \"labels\"] = 0\n",
    "train.loc[train[\"labels\"] == \"FAVOR\", \"labels\"] = 1\n",
    "train.loc[train[\"labels\"] == \"NONE\", \"labels\"] = 2\n",
    "\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4725BiMb5JwG",
   "metadata": {
    "id": "4725BiMb5JwG"
   },
   "source": [
    "Let's train our model!"
   ]
  },
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:filelock:Lock 139624710369632 acquired on /home/gokulakrishnan/.cache/huggingface/hub/models--roberta-base/blobs/8db5e7ac5bfc9ec8b613b776009300fe3685d957.lock\n"
     ]
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     "data": {
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       "model_id": "fe3c82d557294122afc85d29292609f3",
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       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
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     "name": "stderr",
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     "text": [
      "INFO:filelock:Lock 139624710369632 released on /home/gokulakrishnan/.cache/huggingface/hub/models--roberta-base/blobs/8db5e7ac5bfc9ec8b613b776009300fe3685d957.lock\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "'use_cuda' set to True when cuda is unavailable. Make sure CUDA is available or set use_cuda=False.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/simpletransformers/classification/classification_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_type, model_name, tokenizer_type, tokenizer_name, num_labels, weight, args, use_cuda, cuda_device, onnx_execution_provider, **kwargs)\u001b[0m\n\u001b[1;32m    386\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"cuda:{cuda_device}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m    389\u001b[0m                     \u001b[0;34m\"'use_cuda' set to True when cuda is unavailable.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m                     \u001b[0;34m\" Make sure CUDA is available or set use_cuda=False.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'use_cuda' set to True when cuda is unavailable. Make sure CUDA is available or set use_cuda=False."
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = ClassificationModel(\n",
    "    \"roberta\", \"roberta-base\", num_labels=3\n",
    ")\n",
    "model.train_model(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "N1mamPoq5JwK",
   "metadata": {
    "id": "N1mamPoq5JwK"
   },
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54vFfAsr5JwM",
   "metadata": {
    "id": "54vFfAsr5JwM"
   },
   "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)\n",
    "val.loc[val[\"labels\"] == \"AGAINST\", \"labels\"] = 0\n",
    "val.loc[val[\"labels\"] == \"FAVOR\", \"labels\"] = 1\n",
    "val.loc[val[\"labels\"] == \"NONE\", \"labels\"] = 2"
   ]
  },
  {
   "cell_type": "code",
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      "13fba4fccb1349849ee5c8902e7d2f85"
     ]
    },
    "id": "E9uw5vJc5JwO",
    "outputId": "5b40fb06-8b9e-4be7-9804-81ce79680a3f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e997e08c349488b861f1881001fbc0b",
       "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": "bb3d460897aa48718882e39d8f1fc7f9",
       "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': 0.4252971756214733, 'eval_loss': 0.8226409022013347}"
      ]
     },
     "execution_count": 10,
     "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": 12,
   "id": "frX89usd5JwP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "a05b92abe83d48018e0750fd902597bc",
      "8d79b9f8f71a4e8fb2fa16d79a576ac8",
      "cb748534065945f9b39e4753ab4c9a2d",
      "5c4137b92f064cc1be22a2a35a23b614",
      "89e70387c4f242059bd9036bb802d0d4",
      "35da8e0a9b424614ab4af7195d9ade17",
      "3d6f1dc88c444c9ab8d7d68e43e07a60",
      "93a19ab52342485f87415fa5fa39b6a5",
      "14d4e3b20acb42bc8bc045fb8c69765d",
      "c730efba223a4baab9f9b4f32ac1d54a",
      "1121ff31daea4b17add3e3a390de385a",
      "ae022b863a2d429bb83f38753baa198e",
      "2d9d21b1e13043b0b610978dd15d1c1d",
      "feaee6ab900143168381ac8bb741433c",
      "4c18b0bac8c141a8a8e0300d27e64f07",
      "07b390aeb121432db92a8645cdcf61f3",
      "104aec12f1654487b429f5ceedd10525",
      "3c96483b959c466e8d2b8bfa42affe67",
      "b4c428e3dadc405da628c588e11a4fc0",
      "d99fdbbc346d416da485a8a726b58cc6",
      "7a1f285504ed4e16af36354d4d1f5cc4",
      "9f682cf876624e08a2a645059f89187a"
     ]
    },
    "id": "frX89usd5JwP",
    "outputId": "feb187c1-d252-4446-84ee-e56f90e6dee2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a05b92abe83d48018e0750fd902597bc",
       "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": "ae022b863a2d429bb83f38753baa198e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/75 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": null,
   "id": "yCn6wRbN5JwQ",
   "metadata": {
    "id": "yCn6wRbN5JwQ",
    "outputId": "da001e58-8b47-49b4-fe7e-2a7f3a8933ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
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    "#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()"
   ]
  },
  {
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   "id": "j19nek7G5JwR",
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     "execution_count": 13,
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    "#Mapping back integer labels to words\n",
    "val.loc[val[\"predictions\"] == 0, \"predictions\"] = \"AGAINST\"\n",
    "val.loc[val[\"predictions\"] == 1, \"predictions\"] = \"FAVOR\"\n",
    "val.loc[val[\"predictions\"] == 2, \"predictions\"] = \"NONE\"\n",
    "#Final submission is need to be in the following format\n",
    "val = val.drop(columns=['labels', 'Premise']).rename(columns={'predictions':\n",
    "                                                      'Stance'})\n",
    "val"
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