{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "66d5faa2",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"from transformers import AutoModel, AutoTokenizer\n",
"# from transformers import MarianMTModel\n",
"from transformers import XLMRobertaForSequenceClassification\n",
"\n",
"from tqdm import tqdm\n",
"tqdm.pandas()\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ed76080b",
"metadata": {},
"outputs": [],
"source": [
"def detect_language(model, tokenizer, text):\n",
" \n",
" token_dict = tokenizer(text, return_tensors=\"pt\").to(\"cuda\")\n",
" outputs = model(token_dict.input_ids)\n",
" decoded = outputs.logits.argmax(-1).item()\n",
" lang = model.config.id2label[decoded]\n",
" return lang"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "d671ed05",
"metadata": {},
"outputs": [
{
"data": {
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"... ... ...\n",
"3993 quand il n'est plus possible d'établir des lie... 0\n",
"3999 quand vous prendrez vos sources ailleurs que s... 0\n",
"4002 je suis actuellement en amerique je suis en hi... 0\n",
"4013 pourquoi tant de migrants africains en europe ... 0\n",
"label 0 0\n",
"\n",
"[66224 rows x 2 columns]"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset_path = \"../data/dataset.csv\"\n",
"output_path = \"../data/clean_dataset.csv\"\n",
"\n",
"df = pd.read_csv(dataset_path, index_col=0)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "974b27b1",
"metadata": {},
"outputs": [
{
"data": {
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"model_id": "886bab4622fd4408ae02815fbea41a23",
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"model_id": "2f3a2c0e4f174d309da84fae2f54a90c",
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"metadata": {},
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{
"data": {
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Language detection model\n",
"tokenizer = AutoTokenizer.from_pretrained(\"papluca/xlm-roberta-base-language-detection\")\n",
"model = XLMRobertaForSequenceClassification.from_pretrained(\"papluca/xlm-roberta-base-language-detection\").to(\"cuda\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "10b222d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'en'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"detect_language(model, tokenizer, df.iloc[0].text)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "07d92587",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████| 66224/66224 [08:31<00:00, 129.51it/s]\n"
]
}
],
"source": [
"df[\"language\"] = df.text.progress_apply(lambda x: detect_language(model, tokenizer, x))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a402e116",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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aNm3apIiICDkcDgUFBal169Ye9aGhoXI4HJIkh8PhEXDcy93LLlXjdDp15swZffXVV6qurq6zxr2Ni1m0aJGCg4ONKTw8vL67DwAAGol6h5yePXuqoKBAH374oaZPn674+Hh9/PHH3hhbg0tNTVV5ebkxHT9+3NdDAgAAXhJY3xWCgoLUo0cPSVJUVJT27NmjFStWaOzYsaqqqlJZWZnH0Zzi4mKFhYVJksLCwmrdBeW+++rCmh/ekVVcXCyr1apmzZopICBAAQEBdda4t3ExFotFFoulvrsMAAAaoWt+Tk5NTY0qKysVFRWlJk2aKCcnx1h2+PBhFRUVyWazSZJsNpsKCws97oLKzs6W1WpVRESEUXPhNtw17m0EBQUpKirKo6ampkY5OTlGDQAAQL2O5KSmpmrkyJHq3Lmzvv32W61fv147duzQtm3bFBwcrMmTJyspKUlt2rSR1WrVzJkzZbPZNGTIEElSbGysIiIiNHHiRC1ZskQOh0Nz585VQkKCcYRl2rRpWrlypebMmaPHH39c27dv18aNG5WZ+c87nZKSkhQfH69BgwZp8ODBWr58uSoqKjRp0qQGfGsAAEBjVq+QU1JSokcffVQnT55UcHCw+vXrp23btum+++6TJC1btkz+/v4aM2aMKisrZbfb9fzzzxvrBwQEaMuWLZo+fbpsNptatGih+Ph4LViwwKjp2rWrMjMzlZiYqBUrVqhTp05au3at7Ha7UTN27FiVlpYqLS1NDodDkZGRysrKqnUxMgAAuHld83NyGjOekwMAQOPj9efkAAAA3MgIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQIOQAAwJTqFXIWLVqk22+/Xa1atVJISIhGjRqlw4cPe9QMGzZMfn5+HtO0adM8aoqKihQXF6fmzZsrJCREs2fP1vnz5z1qduzYoYEDB8pisahHjx7KyMioNZ5Vq1apS5cuatq0qaKjo7V79+767A4AADCxeoWc3NxcJSQk6IMPPlB2drbOnTun2NhYVVRUeNRNnTpVJ0+eNKYlS5YYy6qrqxUXF6eqqirt2rVL69atU0ZGhtLS0oyaY8eOKS4uTsOHD1dBQYFmzZqlKVOmaNu2bUbNhg0blJSUpHnz5mnv3r3q37+/7Ha7SkpKrva9AAAAJuLncrlcV7tyaWmpQkJClJubq6FDh0r6/khOZGSkli9fXuc6W7du1YMPPqgTJ04oNDRUkpSenq7k5GSVlpYqKChIycnJyszM1IEDB4z1xo0bp7KyMmVlZUmSoqOjdfvtt2vlypWSpJqaGoWHh2vmzJlKSUmps3dlZaUqKyuN106nU+Hh4SovL5fVaq1V3yUls/5vygU+Xxx3TesDAIDanE6ngoODL/r57XZN1+SUl5dLktq0aeMx/5VXXlG7du3Up08fpaam6rvvvjOW5eXlqW/fvkbAkSS73S6n06mDBw8aNTExMR7btNvtysvLkyRVVVUpPz/fo8bf318xMTFGTV0WLVqk4OBgYwoPD7/KPQcAADe6wKtdsaamRrNmzdKdd96pPn36GPPHjx+vW2+9VR07dtT+/fuVnJysw4cP6y9/+YskyeFweAQcScZrh8NxyRqn06kzZ87o1KlTqq6urrPm0KFDFx1zamqqkpKSjNfuIzkAAMB8rjrkJCQk6MCBA3r//fc95j/xxBPGn/v27asOHTpoxIgROnr0qLp37371I20AFotFFovFp2MAAADXx1WdrpoxY4a2bNmid999V506dbpkbXR0tCTpyJEjkqSwsDAVFxd71Lhfh4WFXbLGarWqWbNmateunQICAuqscW8DAADc3OoVclwul2bMmKFNmzZp+/bt6tq162XXKSgokCR16NBBkmSz2VRYWOhxF1R2drasVqsiIiKMmpycHI/tZGdny2azSZKCgoIUFRXlUVNTU6OcnByjBgAA3NzqdboqISFB69ev1xtvvKFWrVoZ19AEBwerWbNmOnr0qNavX68HHnhAbdu21f79+5WYmKihQ4eqX79+kqTY2FhFRERo4sSJWrJkiRwOh+bOnauEhATjVNK0adO0cuVKzZkzR48//ri2b9+ujRs3KjPzn3c7JSUlKT4+XoMGDdLgwYO1fPlyVVRUaNKkSQ313gAAgEasXiFn9erVkr6/TfxCL730kh577DEFBQXpnXfeMQJHeHi4xowZo7lz5xq1AQEB2rJli6ZPny6bzaYWLVooPj5eCxYsMGq6du2qzMxMJSYmasWKFerUqZPWrl0ru91u1IwdO1alpaVKS0uTw+FQZGSksrKyal2MDAAAbk7X9Jycxu5y99nznBwAAG481+U5OQAAADcqQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADAlQg4AADCleoWcRYsW6fbbb1erVq0UEhKiUaNG6fDhwx41Z8+eVUJCgtq2bauWLVtqzJgxKi4u9qgpKipSXFycmjdvrpCQEM2ePVvnz5/3qNmxY4cGDhwoi8WiHj16KCMjo9Z4Vq1apS5duqhp06aKjo7W7t2767M7AADAxOoVcnJzc5WQkKAPPvhA2dnZOnfunGJjY1VRUWHUJCYm6s0339Rrr72m3NxcnThxQqNHjzaWV1dXKy4uTlVVVdq1a5fWrVunjIwMpaWlGTXHjh1TXFychg8froKCAs2aNUtTpkzRtm3bjJoNGzYoKSlJ8+bN0969e9W/f3/Z7XaVlJRcy/sBAABMws/lcrmuduXS0lKFhIQoNzdXQ4cOVXl5udq3b6/169fr4YcfliQdOnRIvXv3Vl5enoYMGaKtW7fqwQcf1IkTJxQaGipJSk9PV3JyskpLSxUUFKTk5GRlZmbqwIEDRq9x48aprKxMWVlZkqTo6GjdfvvtWrlypSSppqZG4eHhmjlzplJSUq5o/E6nU8HBwSovL5fVaq21vEtK5tW+NZKkzxfHXdP6AACgtst9frtd0zU55eXlkqQ2bdpIkvLz83Xu3DnFxMQYNb169VLnzp2Vl5cnScrLy1Pfvn2NgCNJdrtdTqdTBw8eNGou3Ia7xr2Nqqoq5efne9T4+/srJibGqKlLZWWlnE6nxwQAAMzpqkNOTU2NZs2apTvvvFN9+vSRJDkcDgUFBal169YetaGhoXI4HEbNhQHHvdy97FI1TqdTZ86c0VdffaXq6uo6a9zbqMuiRYsUHBxsTOHh4fXfcQAA0ChcdchJSEjQgQMH9OqrrzbkeLwqNTVV5eXlxnT8+HFfDwkAAHhJ4NWsNGPGDG3ZskU7d+5Up06djPlhYWGqqqpSWVmZx9Gc4uJihYWFGTU/vAvKfffVhTU/vCOruLhYVqtVzZo1U0BAgAICAuqscW+jLhaLRRaLpf47DAAAGp16HclxuVyaMWOGNm3apO3bt6tr164ey6OiotSkSRPl5OQY8w4fPqyioiLZbDZJks1mU2FhocddUNnZ2bJarYqIiDBqLtyGu8a9jaCgIEVFRXnU1NTUKCcnx6gBAAA3t3odyUlISND69ev1xhtvqFWrVsb1L8HBwWrWrJmCg4M1efJkJSUlqU2bNrJarZo5c6ZsNpuGDBkiSYqNjVVERIQmTpyoJUuWyOFwaO7cuUpISDCOskybNk0rV67UnDlz9Pjjj2v79u3auHGjMjP/ebdTUlKS4uPjNWjQIA0ePFjLly9XRUWFJk2a1FDvDQAAaMTqFXJWr14tSRo2bJjH/JdeekmPPfaYJGnZsmXy9/fXmDFjVFlZKbvdrueff96oDQgI0JYtWzR9+nTZbDa1aNFC8fHxWrBggVHTtWtXZWZmKjExUStWrFCnTp20du1a2e12o2bs2LEqLS1VWlqaHA6HIiMjlZWVVetiZAAAcHO6pufkNHY8JwcAgMbnujwnBwAA4EZFyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZEyAEAAKZU75Czc+dO/fSnP1XHjh3l5+enzZs3eyx/7LHH5Ofn5zHdf//9HjXffPONJkyYIKvVqtatW2vy5Mk6ffq0R83+/ft19913q2nTpgoPD9eSJUtqjeW1115Tr1691LRpU/Xt21dvvfVWfXcHAACYVL1DTkVFhfr3769Vq1ZdtOb+++/XyZMnjelPf/qTx/IJEybo4MGDys7O1pYtW7Rz50498cQTxnKn06nY2Fjdeuutys/P19KlSzV//nytWbPGqNm1a5ceeeQRTZ48Wfv27dOoUaM0atQoHThwoL67BAAATMjP5XK5rnplPz9t2rRJo0aNMuY99thjKisrq3WEx+2TTz5RRESE9uzZo0GDBkmSsrKy9MADD+jLL79Ux44dtXr1av3617+Ww+FQUFCQJCklJUWbN2/WoUOHJEljx45VRUWFtmzZYmx7yJAhioyMVHp6+hWN3+l0Kjg4WOXl5bJarbWWd0nJvKLtXMzni+OuaX0AAFDb5T6/3bxyTc6OHTsUEhKinj17avr06fr666+NZXl5eWrdurURcCQpJiZG/v7++vDDD42aoUOHGgFHkux2uw4fPqxTp04ZNTExMR597Xa78vLyLjquyspKOZ1OjwkAAJhTg4ec+++/Xy+//LJycnL0+9//Xrm5uRo5cqSqq6slSQ6HQyEhIR7rBAYGqk2bNnI4HEZNaGioR4379eVq3MvrsmjRIgUHBxtTeHj4te0sAAC4YQU29AbHjRtn/Llv377q16+funfvrh07dmjEiBEN3a5eUlNTlZSUZLx2Op0EHQAATKrBQ84PdevWTe3atdORI0c0YsQIhYWFqaSkxKPm/Pnz+uabbxQWFiZJCgsLU3FxsUeN+/XlatzL62KxWGSxWK55n64HrgcCAODaeP05OV9++aW+/vprdejQQZJks9lUVlam/Px8o2b79u2qqalRdHS0UbNz506dO3fOqMnOzlbPnj11yy23GDU5OTkevbKzs2Wz2by9SwAAoBGod8g5ffq0CgoKVFBQIEk6duyYCgoKVFRUpNOnT2v27Nn64IMP9PnnnysnJ0cPPfSQevToIbvdLknq3bu37r//fk2dOlW7d+/W3/72N82YMUPjxo1Tx44dJUnjx49XUFCQJk+erIMHD2rDhg1asWKFx6mmJ598UllZWXrmmWd06NAhzZ8/Xx999JFmzJjRAG8LAABo7Oodcj766CMNGDBAAwYMkCQlJSVpwIABSktLU0BAgPbv36+f/exn+vGPf6zJkycrKipK7733nsdpoldeeUW9evXSiBEj9MADD+iuu+7yeAZOcHCw3n77bR07dkxRUVH65S9/qbS0NI9n6dxxxx1av3691qxZo/79++v111/X5s2b1adPn2t5PwAAgElc03NyGrsb+Tk5XJMDAEDdfPqcHAAAAF8j5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFOqd8jZuXOnfvrTn6pjx47y8/PT5s2bPZa7XC6lpaWpQ4cOatasmWJiYvTpp5961HzzzTeaMGGCrFarWrdurcmTJ+v06dMeNfv379fdd9+tpk2bKjw8XEuWLKk1ltdee029evVS06ZN1bdvX7311lv13R0AAGBS9Q45FRUV6t+/v1atWlXn8iVLlui5555Tenq6PvzwQ7Vo0UJ2u11nz541aiZMmKCDBw8qOztbW7Zs0c6dO/XEE08Yy51Op2JjY3XrrbcqPz9fS5cu1fz587VmzRqjZteuXXrkkUc0efJk7du3T6NGjdKoUaN04MCB+u4SAAAwIT+Xy+W66pX9/LRp0yaNGjVK0vdHcTp27Khf/vKXeuqppyRJ5eXlCg0NVUZGhsaNG6dPPvlEERER2rNnjwYNGiRJysrK0gMPPKAvv/xSHTt21OrVq/XrX/9aDodDQUFBkqSUlBRt3rxZhw4dkiSNHTtWFRUV2rJlizGeIUOGKDIyUunp6XWOt7KyUpWVlcZrp9Op8PBwlZeXy2q11qrvkpJ5tW+NJOnzxXFXva4vewMAcCNzOp0KDg6+6Oe3W4Nek3Ps2DE5HA7FxMQY84KDgxUdHa28vDxJUl5enlq3bm0EHEmKiYmRv7+/PvzwQ6Nm6NChRsCRJLvdrsOHD+vUqVNGzYV93DXuPnVZtGiRgoODjSk8PPzadxoAANyQGjTkOBwOSVJoaKjH/NDQUGOZw+FQSEiIx/LAwEC1adPGo6aubVzY42I17uV1SU1NVXl5uTEdP368vrsIAAAaiUBfD+B6slgsslgsvh4GAAC4Dhr0SE5YWJgkqbi42GN+cXGxsSwsLEwlJSUey8+fP69vvvnGo6aubVzY42I17uUAAODm1qAhp2vXrgoLC1NOTo4xz+l06sMPP5TNZpMk2Ww2lZWVKT8/36jZvn27ampqFB0dbdTs3LlT586dM2qys7PVs2dP3XLLLUbNhX3cNe4+AADg5lbvkHP69GkVFBSooKBA0vcXGxcUFKioqEh+fn6aNWuWFi5cqL/+9a8qLCzUo48+qo4dOxp3YPXu3Vv333+/pk6dqt27d+tvf/ubZsyYoXHjxqljx46SpPHjxysoKEiTJ0/WwYMHtWHDBq1YsUJJSUnGOJ588kllZWXpmWee0aFDhzR//nx99NFHmjFjxrW/KwAAoNGr9zU5H330kYYPH268dgeP+Ph4ZWRkaM6cOaqoqNATTzyhsrIy3XXXXcrKylLTpk2NdV555RXNmDFDI0aMkL+/v8aMGaPnnnvOWB4cHKy3335bCQkJioqKUrt27ZSWlubxLJ077rhD69ev19y5c/WrX/1Kt912mzZv3qw+ffpc1RsBAADM5Zqek9PYXe4+e56TAwDAjccnz8kBAAC4URByAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKRFyAACAKTV4yJk/f778/Pw8pl69ehnLz549q4SEBLVt21YtW7bUmDFjVFxc7LGNoqIixcXFqXnz5goJCdHs2bN1/vx5j5odO3Zo4MCBslgs6tGjhzIyMhp6VwAAQCPmlSM5P/nJT3Ty5Eljev/9941liYmJevPNN/Xaa68pNzdXJ06c0OjRo43l1dXViouLU1VVlXbt2qV169YpIyNDaWlpRs2xY8cUFxen4cOHq6CgQLNmzdKUKVO0bds2b+wOAABohAK9stHAQIWFhdWaX15erhdffFHr16/XvffeK0l66aWX1Lt3b33wwQcaMmSI3n77bX388cd65513FBoaqsjISP32t79VcnKy5s+fr6CgIKWnp6tr16565plnJEm9e/fW+++/r2XLlslut3tjlwAAQCPjlSM5n376qTp27Khu3bppwoQJKioqkiTl5+fr3LlziomJMWp79eqlzp07Ky8vT5KUl5envn37KjQ01Kix2+1yOp06ePCgUXPhNtw17m1cTGVlpZxOp8cEAADMqcFDTnR0tDIyMpSVlaXVq1fr2LFjuvvuu/Xtt9/K4XAoKChIrVu39lgnNDRUDodDkuRwODwCjnu5e9mlapxOp86cOXPRsS1atEjBwcHGFB4efq27CwAAblANfrpq5MiRxp/79eun6Oho3Xrrrdq4caOaNWvW0O3qJTU1VUlJScZrp9NJ0AEAwKS8fgt569at9eMf/1hHjhxRWFiYqqqqVFZW5lFTXFxsXMMTFhZW624r9+vL1Vit1ksGKYvFIqvV6jEBAABz8nrIOX36tI4ePaoOHTooKipKTZo0UU5OjrH88OHDKioqks1mkyTZbDYVFhaqpKTEqMnOzpbValVERIRRc+E23DXubQAAADR4yHnqqaeUm5urzz//XLt27dLPf/5zBQQE6JFHHlFwcLAmT56spKQkvfvuu8rPz9ekSZNks9k0ZMgQSVJsbKwiIiI0ceJE/f3vf9e2bds0d+5cJSQkyGKxSJKmTZumzz77THPmzNGhQ4f0/PPPa+PGjUpMTGzo3QEAAI1Ug1+T8+WXX+qRRx7R119/rfbt2+uuu+7SBx98oPbt20uSli1bJn9/f40ZM0aVlZWy2+16/vnnjfUDAgK0ZcsWTZ8+XTabTS1atFB8fLwWLFhg1HTt2lWZmZlKTEzUihUr1KlTJ61du5bbxwEAgMHP5XK5fD0IX3E6nQoODlZ5eXmd1+d0Scm8pu1/vjjuqtf1ZW8AAG5kl/v8duN3VwEAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMi5AAAAFMK9PUAcOPpkpJ5Tet/vjiugUYCAMDV40gOAAAwJUIOAAAwJU5X4YbCqTIAQEMh5AD/HwELAMyF01UAAMCUCDkAAMCUOF0F3AA4VQYADY+QA9zkCFgAzIrTVQAAwJQIOQAAwJQIOQAAwJQIOQAAwJQa/YXHq1at0tKlS+VwONS/f3/993//twYPHuzrYQG4Ar686Plm7Q3cTBr1kZwNGzYoKSlJ8+bN0969e9W/f3/Z7XaVlJT4emgAAMDHGvWRnGeffVZTp07VpEmTJEnp6enKzMzUH/7wB6WkpPh4dABw47lZj2Bx9Ozm1GhDTlVVlfLz85WammrM8/f3V0xMjPLy8upcp7KyUpWVlcbr8vJySZLT6ayzvqbyu2sa48W2eyXoTW9605ve5uiNhuf++3C5XJcudDVS//d//+eS5Nq1a5fH/NmzZ7sGDx5c5zrz5s1zSWJiYmJiYmIywXT8+PFLZoVGeyTnaqSmpiopKcl4XVNTo2+++UZt27aVn59fvbbldDoVHh6u48ePy2q1NvRQ6U1vetOb3vSm90W4XC59++236tix4yXrGm3IadeunQICAlRcXOwxv7i4WGFhYXWuY7FYZLFYPOa1bt36msZhtVqv+/8c9KY3velNb3rf7L2Dg4MvW9No764KCgpSVFSUcnJyjHk1NTXKycmRzWbz4cgAAMCNoNEeyZGkpKQkxcfHa9CgQRo8eLCWL1+uiooK424rAABw82rUIWfs2LEqLS1VWlqaHA6HIiMjlZWVpdDQUK/3tlgsmjdvXq3TX9cDvelNb3rTm970vjw/l+ty918BAAA0Po32mhwAAIBLIeQAAABTIuQAAABTIuQAAABTIuQAAABTIuRcgXPnzmnEiBH69NNPfT0UACZSXV2tnTt3qqyszNdDAUyJkHMFmjRpov379/t6GJK+/+3rX375pYqKijwmbxo6dKjS0tKUk5Ojs2fPerXXhc6fP68FCxboyy+/vG49cfNat26dMjMzjddz5sxR69atdccdd+iLL77wSs+AgADFxsbq1KlTXtk+Lu3o0aOaO3euHnnkEZWUlEiStm7dqoMHDzZ4L6fTecWTmV3vzzCek3OFEhMTZbFYtHjxYp/0//TTT/X4449r165dHvNdLpf8/PxUXV3ttd4LFy7Uzp07tWvXLp0/f16DBg3SsGHDdM899+jOO+9U8+bNvda7VatWKiwsVJcuXbzW42ICAgJ08uRJhYSEeMz/+uuvFRIS0uDv+V//+leNHDlSTZo00V//+tdL1v7sZz9r0N4X8sV+Xylv7nfPnj21evVq3XvvvcrLy1NMTIyWLVumLVu2KDAwUH/5y1+80nfQoEH6/e9/rxEjRnhl+5fz3HPP1Tnfz89PTZs2VY8ePTR06FAFBAR4tV9dfvGLXzRIz7rk5uZq5MiRuvPOO7Vz50598skn6tatmxYvXqyPPvpIr7/+eoP28/f3v+wvgr4eP8937tx5yeVDhw71Sl9ffYYRcq7QzJkz9fLLL+u2225TVFSUWrRo4bH82Wef9Wr/O++8U4GBgUpJSVGHDh1q/WPp37+/V/tL3x9Z2bNnj3Jzc7Vjxw5t375d/v7+Xj2689BDD2n06NGKj4/3Wo+L8ff3l8PhqPVhf+LECXXv3l1nzpzxWj9//4sfZPX2D0Ff7PeV8PZ+N2/eXIcOHVLnzp2VnJyskydP6uWXX9bBgwc1bNgwlZaWeqVvVlaWUlNT9dvf/rbOny3e/sWJXbt2VWlpqb777jvdcsstkqRTp06pefPmatmypUpKStStWze9++67Cg8Pb5B+V8LPz0+fffbZNfe7GJvNpn/5l39RUlKSWrVqpb///e/q1q2bdu/erdGjRzf4EeTc3Nwrrr3nnnsatPeF6vr3duHnibf+jfnqM6xR/1oHb9u/f7/69Okjf39/HThwQAMHDpQk/eMf//Cou1w6bwgFBQXKz89Xr169vN7rYj777DMVFhbq73//u/bv369WrVp5LfW7jRw5UikpKSosLKzzA8Ab3+zd3zT9/Py0du1atWzZ0ljmvobCG38PNTU1df75erkR9tuXWrZsqa+//lqdO3fW22+/raSkJElS06ZNGzzYXeiBBx6Q9P3/yxf+LLke3+ol6Xe/+53WrFmjtWvXqnv37pKkI0eO6N///d/1xBNP6M4779S4ceOUmJjYIEc3jh07Vud89/ft6/HzVJIKCwu1fv36WvNDQkL01VdfNXi/HwaX9957Ty+88IKOHj2q119/XT/60Y/0xz/+8YpD4NX64anRc+fOad++fXr66af1n//5n17r66vPMELOJQwYMMA4bP/FF19oz549atu2rU/GEhER4ZV/eFdi/Pjxys3NVWVlpYYOHap77rlHKSkp6tevn9d/IP3Hf/yHpLqPlHnrA2DZsmWSvv+hm56e7nGYPigoSF26dFF6enqD9/2hnJwc5eTkqKSkxCMI+Pn56cUXX2zwfjfrfrvdd999mjJligYMGKB//OMfRvg4ePCgbr31Vq/1femllxQeHl7rdFBNTY3Xr7eTpLlz5+rPf/6zEXAkqUePHvqv//ovjRkzRp999pmWLFmiMWPGeKX/iy++qGXLlhk3dtx2222aNWuWpkyZ4pV+bq1bt9bJkydrhYp9+/bpRz/6kVd7//nPf9bEiRM1YcIE7du3T5WVlZKk8vJy/e53v9Nbb73ltd7BwcG15t13330KCgpSUlKS8vPzvdLXZ59hLlxUmzZtXB988IHL5XK5/Pz8XCUlJT4bS05Ojstms7neffdd11dffeUqLy/3mLzJz8/P1b59e1dycrJr27ZtroqKCq/2u1EMGzbMderUKZ/0nj9/vsvf3981ePBg10MPPeQaNWqUx+RNF+53TU2Nq6amxqv9LuTL/T516pRrxowZroceesiVlZVlzE9LS3MtXLjQa339/f1dxcXFteZ/9dVXLn9/f6/1dWvWrJlrz549tebv3r3b1axZM5fL5XIdO3bM1aJFiwbv/fTTT7tatGjhSklJcb3xxhuuN954w5WSkuJq2bKl6+mnn27wfhf65S9/6brrrrtcJ0+edLVq1cr16aefut5//31Xt27dXPPnz/dq78jISNe6detcLpfL1bJlS9fRo0ddLpfLtXfvXldoaKhXe1/MJ5980uB/xxd+RvnqM4xrci7hiSee0Msvv6wOHTqoqKhInTp1uujFd948dyx5nke93oe0T506pffee087duxQbm6uPvnkE0VGRmrYsGEaNmyYYmNjvdZ7wYIFF13m5+enp59+ukH7JSUl6be//a1atGihxMTESx6p8uZ1WB06dNCSJUs0ceJEr/W4FF99u/b1fu/cuVMvvPCCPvvsM+MUwssvv6xu3brprrvu8kpPf39/FRcXq3379h7zv/jiC0VERKiiosIrfd3i4uLkcDi0du1aDRgwQNL3RzOmTp2qsLAwbdmyRW+++aZ+9atfqbCwsEF7t2/fXs8995weeeQRj/l/+tOfNHPmTK9+86+qqlJCQoIyMjJUXV2twMBAnT9/XhMmTFBGRkaDXWhdl+bNm+vjjz9Wly5dPK4H+uyzzxQREeHV6xx/eKewy+XSyZMntXjxYp0/f17vv/9+g/X64cXW7s+rH/b35mcYp6suYc2aNRo9erSOHDmiX/ziF5o6dapatWrlk7G8++67PukrSbfccot+9rOfGde/HDlyRAsXLtTSpUv1+9//3qsBa9OmTR6vz507p2PHjikwMFDdu3dv8JCzb98+nTt3TtL355Avxtun6aqqqnTHHXd4tcfFzJs3T88884xmzpwpm80mScrLy1NiYqKKioouGTyvlS/3+2KnEJxOp1dOIbiv+XGH9QvvUqyurtaHH36oyMjIBu1ZlxdffFETJ05UVFSUmjRpIun7f2cxMTHG6cGWLVvqmWeeafDe586d06BBg2rNj4qK0vnz5xu834WCgoL0P//zP0pLS1NhYaFOnz6tAQMG6LbbbvNqX0kKCwvTkSNHat01+v7776tbt25e7R0ZGSk/Pz/98PjGkCFD9Ic//KFBe134ufX555/75LQsR3Ku0KRJk/Tcc8/5LORIl75QzVvfMqXvbx1231G1Y8cOffzxx2rdurVxfc6TTz7ptd51cTqdeuyxx/Tzn//cZ9/4vS05OVktW7Zs8BB3JXz57dqX+z1gwAAlJibq0Ucf9fh2vW/fPo0cOVIOh6NB+w0fPlzS93fd2Gw2BQUFGcvc10A99dRT1+VDV5IOHz6sw4cPS/r+dvqePXt6vefMmTPVpEmTWkdFn3rqKZ05c0arVq1q0H7uYHklvHmkdtGiRfrf//1f/eEPf9B9992nt956S1988YUSExP19NNPa+bMmV7pe+7cOdntdqWnp8tisUj6/mhL+/bt1bRpU6/0dLvej6Zw40jOFXrppZd82t+XF6qFhISoXbt2uvvuuzV16lQNGzZMffv29Vq/y7FarfrNb36jn/70p6YKORf+AK6pqdGaNWv0zjvvqF+/fsY3bDdv/gD25bfrs2fP+my/Dx8+XOfdgsHBwV55IrH7W+6kSZO0YsUKr98qfim+Oj3p7v32229ryJAhkqQPP/xQRUVFevTRRz3+TTTE3/2+ffs8Xu/du1fnz583At0//vEPBQQEKCoq6pp7XUpKSopqamo0YsQIfffddxo6dKgsFoueeuoprwUc6fsH2xYWFsrf39+rF9PXpa5TVZJ0+vRprwYsQk4jsXDhQqWnp+vRRx/Vq6++asy/8847tXDhQq/23rdvn7p162bcUvzFF19o+fLlioiI8Or1OJdSXl6u8vJyn/T2lh/+AHafqjhw4IDHfG+fKps4caJWr15d60NlzZo1mjBhgld779+/32f77atTCL7+ApWWlqZnn33WJ6cnL3w0x9GjRyVJ7dq1U7t27Tz+/hvq7/7C0yfPPvusWrVqpXXr1nk8H2jSpEm6++67G6Tfxfj5+enXv/61Zs+erSNHjuj06dOKiIjweGyDt/zbv/2b1q5de90ebOvr07KcrmokfHmhWmxsrEaPHq1p06aprKxMvXr1UpMmTfTVV1/p2Wef1fTp073W+4dPR3VfJPfHP/5R99xzT53PucC1cT/4Mjw8vM5v1xceXfH2QzCvJ1+dQvA1X56e9KUf/ehHevvtt/WTn/zEY/6BAwcUGxurEydO+Ghk3nW9H2zr69OyHMlpJHx5odrevXuNZ6i8/vrrCg0N1b59+/TnP/9ZaWlpXg057r5u7vPH8fHxSk1N9Vrfm9n1/nZ9o/DVKQRf8+XpSV9yOp11PsW6tLRU3377rQ9GdH1c7wfb+vq0LEdyGglffsu88HH3//qv/6qf/OQnmjdvno4fP66ePXvqu+++81pv4Hqrqqq67qcQfOl6X/x7o3j00Uf13nvv6ZlnntHgwYMlfX/Ecvbs2br77ru1bt06H48QDYEjOY2EL79l9ujRQ5s3b9bPf/5zbdu2TYmJiZKkkpISn14sCXhDUFCQIiIifD0Mr7rwgl73r/G42MW/ZpWenq6nnnpK48ePNx4bERgYqMmTJ2vp0qU+Hh0aCkdyGhlffMt8/fXXNX78eFVXV2vEiBF6++23JX1/dGnnzp3aunWr18cAoOG4r5O4HD8/P23fvt3Lo/GtiooK47Rs9+7da12jgsaNkIMr4nA4dPLkSfXv3994+vLu3btltVp9+ktDAQC4GEIOAAAwJf/LlwAAADQ+hBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBKhBwAAGBK/w+yc0I/BK+1SgAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.language.value_counts().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "04d8369e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(24002, 3)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"errors_df = df.loc[df.language != \"fr\"]\n",
"errors_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f1ed5db9",
"metadata": {},
"outputs": [
{
"data": {
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" 0 | \n",
" it | \n",
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\n",
" \n",
" [2019-06-13 14:29:04] | \n",
" Adrien | \n",
" 0 | \n",
" nl | \n",
"
\n",
" \n",
" [2019-06-13 14:31:44] | \n",
" ... | \n",
" 0 | \n",
" hi | \n",
"
\n",
" \n",
" 2260 | \n",
" @TeikOoFN @Sweafts @wahxbb Tu penses j'en reço... | \n",
" 1 | \n",
" nl | \n",
"
\n",
" \n",
" 1844 | \n",
" Trichirappalli | \n",
" 0 | \n",
" hi | \n",
"
\n",
" \n",
" 6357 | \n",
" lax | \n",
" 0 | \n",
" it | \n",
"
\n",
" \n",
" [2019-06-13 14:22:19] | \n",
" pute | \n",
" 0 | \n",
" ur | \n",
"
\n",
" \n",
" 8913 | \n",
" Yep | \n",
" 0 | \n",
" sw | \n",
"
\n",
" \n",
" [2019-06-13 14:28:14] | \n",
" sylvia ouuouuu? | \n",
" 0 | \n",
" pt | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" text \\\n",
"3218 oshi que mongol \n",
"[2019-06-13 14:16:49] toi ta mere c satan \n",
"235 Fascist republican profiteers nazi pigs https:... \n",
"[2019-06-13 14:33:42] boucje \n",
"9203 yes i am \n",
"2783 buenos aires \n",
"[2019-06-13 14:32:11] salope \n",
"3228 gay men aren't real men \n",
"2814 @Btlabeutax93 @AntiChanell Mdrrrr wesh jtarran... \n",
"3005 no \n",
"8460 i wan't \n",
"1031 \"Comme si Hitler n'existait pas ! \n",
"[2019-06-13 14:29:04] Adrien \n",
"[2019-06-13 14:31:44] ... \n",
"2260 @TeikOoFN @Sweafts @wahxbb Tu penses j'en reço... \n",
"1844 Trichirappalli \n",
"6357 lax \n",
"[2019-06-13 14:22:19] pute \n",
"8913 Yep \n",
"[2019-06-13 14:28:14] sylvia ouuouuu? \n",
"\n",
" label language \n",
"3218 0 sw \n",
"[2019-06-13 14:16:49] 1 hi \n",
"235 1 it \n",
"[2019-06-13 14:33:42] 0 pl \n",
"9203 0 sw \n",
"2783 0 es \n",
"[2019-06-13 14:32:11] 1 bg \n",
"3228 1 nl \n",
"2814 0 tr \n",
"3005 0 sw \n",
"8460 0 sw \n",
"1031 0 it \n",
"[2019-06-13 14:29:04] 0 nl \n",
"[2019-06-13 14:31:44] 0 hi \n",
"2260 1 nl \n",
"1844 0 hi \n",
"6357 0 it \n",
"[2019-06-13 14:22:19] 0 ur \n",
"8913 0 sw \n",
"[2019-06-13 14:28:14] 0 pt "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"errors_df.loc[errors_df.language != \"en\"].sample(20)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "87c43105",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pred errors : 441\n"
]
},
{
"data": {
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"\n",
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\n",
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"
42663 rows × 3 columns
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"
"
],
"text/plain": [
" text \\\n",
"5823 dumb bot \n",
"6783 dumb bot \n",
"8898 dumb bot \n",
"[2019-06-13 14:12:47] t as vu ce que t as fait a la soiré sale pute \n",
"[2019-06-13 14:14:56] je suis espagnol pardon de ne pas tout savoir... \n",
"... ... \n",
"[2019-06-13 14:32:11] salope \n",
"[2019-06-13 14:16:49] toi ta mere c satan \n",
"[2019-06-13 14:22:15] t conne \n",
"[2019-06-13 14:22:22] salope \n",
"[2019-06-13 14:32:11] salope \n",
"\n",
" label language \n",
"5823 1 fr \n",
"6783 1 fr \n",
"8898 1 fr \n",
"[2019-06-13 14:12:47] 1 fr \n",
"[2019-06-13 14:14:56] 1 fr \n",
"... ... ... \n",
"[2019-06-13 14:32:11] 0 fr \n",
"[2019-06-13 14:16:49] 0 fr \n",
"[2019-06-13 14:22:15] 0 fr \n",
"[2019-06-13 14:22:22] 0 fr \n",
"[2019-06-13 14:32:11] 0 fr \n",
"\n",
"[42663 rows x 3 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clean_df = df.loc[df.language == \"fr\"]\n",
"\n",
"# Check language predictions using specific stopword list\n",
"stopwords = [\"conne\", \"salope\", \"mere\"]\n",
"\n",
"pred_errors = errors_df.loc[errors_df.text.str.contains(\"|\".join(stopwords))]\n",
"print(\"Pred errors :\", pred_errors.shape[0])\n",
"clean_df = pd.concat([clean_df, pred_errors])\n",
"clean_df[\"language\"] = \"fr\"\n",
"clean_df"
]
},
{
"cell_type": "markdown",
"id": "8a33a9c3",
"metadata": {},
"source": [
"### Manual postprocessing\n",
"\n",
"Remove manually detected wrong language rows"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "9d9f26e1",
"metadata": {},
"outputs": [
{
"data": {
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42660 rows × 3 columns
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],
"text/plain": [
" text \\\n",
"[2019-06-13 14:12:47] t as vu ce que t as fait a la soiré sale pute \n",
"[2019-06-13 14:14:56] je suis espagnol pardon de ne pas tout savoir... \n",
"[2019-06-13 14:15:18] tu as embrasser Lucas alors que sophia est ta... \n",
"[2019-06-13 14:15:27] tu as foutu la merde dans leurs couple \n",
"[2019-06-13 14:15:31] c'est dégeulasse \n",
"... ... \n",
"[2019-06-13 14:32:11] salope \n",
"[2019-06-13 14:16:49] toi ta mere c satan \n",
"[2019-06-13 14:22:15] t conne \n",
"[2019-06-13 14:22:22] salope \n",
"[2019-06-13 14:32:11] salope \n",
"\n",
" label language \n",
"[2019-06-13 14:12:47] 1 fr \n",
"[2019-06-13 14:14:56] 1 fr \n",
"[2019-06-13 14:15:18] 1 fr \n",
"[2019-06-13 14:15:27] 1 fr \n",
"[2019-06-13 14:15:31] 1 fr \n",
"... ... ... \n",
"[2019-06-13 14:32:11] 0 fr \n",
"[2019-06-13 14:16:49] 0 fr \n",
"[2019-06-13 14:22:15] 0 fr \n",
"[2019-06-13 14:22:22] 0 fr \n",
"[2019-06-13 14:32:11] 0 fr \n",
"\n",
"[42660 rows x 3 columns]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ids = [\"5823\", \"6783\", \"8898\", \"1966\"]\n",
"\n",
"manual_clean_df = clean_df.loc[clean_df.index.isin(ids) == False]\n",
"manual_clean_df"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "fb79ecfb",
"metadata": {},
"outputs": [
{
"data": {
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" 284 | \n",
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" 664 | \n",
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" 0 | \n",
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" 137 | \n",
" @djoulye @LiorahDouge @barriere_dr @MahmoudZur... | \n",
" 0 | \n",
" fr | \n",
"
\n",
" \n",
" 1293 | \n",
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" au moins mon pere est pas mort | \n",
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\n",
" \n",
" 1602 | \n",
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\n",
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" [2019-06-13 14:16:49] | \n",
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" 1 | \n",
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\n",
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" t'es venu au nom de dieu Herbert? | \n",
" 0 | \n",
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\n",
" \n",
" [2019-06-13 14:11:46] | \n",
" Salut Adri, ça va? | \n",
" 0 | \n",
" fr | \n",
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\n",
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" text \\\n",
"[2019-06-13 14:21:43] apprend a écrire connasse \n",
"[2019-06-13 14:21:42] il leur paie des apparts privés \n",
"3333 la pauvreté n'est le collateral de la violence... \n",
"827 \"Les décès massifs qui ont lieu en Afghanistan... \n",
"2674 @Arashi_Storm @Jubaieuh par contre dire que je... \n",
"1023 @TeTr4_Live Non mais totalement... Bon Zemmour... \n",
"284 homme si une dame a essayé de se mettre entre ... \n",
"664 rt l'histoire du quebec vaut la peine qu'on s... \n",
"[2019-06-13 14:28:44] déso j'avais plus de connexion \n",
"609 @CommanderTorenn On va faire court,je ne m,int... \n",
"[2019-06-13 14:35:20] au moins j'arrive à me faire embrasse Diana \n",
"[2019-06-13 14:32:35] Mais oui... \n",
"137 @djoulye @LiorahDouge @barriere_dr @MahmoudZur... \n",
"1293 ptdrrr tout les «anti satanistes/theorie du co... \n",
"[2019-06-13 14:35:04] au moins mon pere est pas mort \n",
"1602 @LamoloQc @tvanouvelles On ne sera plus là pou... \n",
"[2019-06-13 14:16:49] toi ta mere c satan \n",
"[2019-06-13 14:24:51] t'es venu au nom de dieu Herbert? \n",
"[2019-06-13 14:11:46] Salut Adri, ça va? \n",
"[2019-06-13 14:32:11] salope \n",
"\n",
" label language \n",
"[2019-06-13 14:21:43] 0 fr \n",
"[2019-06-13 14:21:42] 0 fr \n",
"3333 0 fr \n",
"827 0 fr \n",
"2674 0 fr \n",
"1023 0 fr \n",
"284 0 fr \n",
"664 0 fr \n",
"[2019-06-13 14:28:44] 0 fr \n",
"609 0 fr \n",
"[2019-06-13 14:35:20] 0 fr \n",
"[2019-06-13 14:32:35] 0 fr \n",
"137 0 fr \n",
"1293 0 fr \n",
"[2019-06-13 14:35:04] 0 fr \n",
"1602 0 fr \n",
"[2019-06-13 14:16:49] 1 fr \n",
"[2019-06-13 14:24:51] 0 fr \n",
"[2019-06-13 14:11:46] 0 fr \n",
"[2019-06-13 14:32:11] 0 fr "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"manual_clean_df.sample(20)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "922e8862",
"metadata": {},
"outputs": [],
"source": [
"manual_clean_df.to_csv(output_path)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sexism_detection",
"language": "python",
"name": "sexism_detection"
},
"language_info": {
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"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
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