Language Detection using fine-tuned XLM-Roberta-Base Transformer Model.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Import the necessary libraries","metadata":{"papermill":{"duration":0.010323,"end_time":"2022-12-20T21:37:24.249939","exception":false,"start_time":"2022-12-20T21:37:24.239616","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":0.033894,"end_time":"2022-12-20T21:37:24.292844","exception":false,"start_time":"2022-12-20T21:37:24.258950","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:34.968504Z","iopub.execute_input":"2022-12-21T09:33:34.968885Z","iopub.status.idle":"2022-12-21T09:33:35.008157Z","shell.execute_reply.started":"2022-12-21T09:33:34.968805Z","shell.execute_reply":"2022-12-21T09:33:35.007143Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/language-detection/Language Detection.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install wget","metadata":{"papermill":{"duration":15.122787,"end_time":"2022-12-20T21:37:39.425129","exception":false,"start_time":"2022-12-20T21:37:24.302342","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:35.010109Z","iopub.execute_input":"2022-12-21T09:33:35.010478Z","iopub.status.idle":"2022-12-21T09:33:50.546467Z","shell.execute_reply.started":"2022-12-21T09:33:35.010443Z","shell.execute_reply":"2022-12-21T09:33:50.544915Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting wget\n  Downloading wget-3.2.zip (10 kB)\n  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hBuilding 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=9675 sha256=afe609640fbe0a6920b5ff111aa5194def2a4e4d0a479721a3e07091d7eba083\n  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\nSuccessfully built wget\nInstalling collected packages: wget\nSuccessfully 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\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings, wget\nwarnings.filterwarnings(\"ignore\")\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dense, Input, Dropout, Flatten\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom tensorflow.keras.losses import CategoricalCrossentropy\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.metrics import CategoricalAccuracy\nfrom tensorflow.keras.utils import to_categorical, plot_model\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report, plot_confusion_matrix\nfrom wordcloud import WordCloud\nwget.download(\"https://raw.githubusercontent.com/yogawicaksana/helper_prabowo/main/helper_prabowo_ml.py\",out=\"helper_prabowo_ml.py\")\n\n\nfrom helper_prabowo_ml import clean_html, remove_links, remove_special_characters, removeStopWords, remove_, remove_digits, lower, email_address, non_ascii, punct, hashtags\nfrom transformers import AutoTokenizer, TFAutoModelForSequenceClassification","metadata":{"papermill":{"duration":13.719018,"end_time":"2022-12-20T21:37:53.154128","exception":false,"start_time":"2022-12-20T21:37:39.435110","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:44:24.900058Z","iopub.execute_input":"2022-12-21T10:44:24.900445Z","iopub.status.idle":"2022-12-21T10:44:25.235959Z","shell.execute_reply.started":"2022-12-21T10:44:24.900415Z","shell.execute_reply":"2022-12-21T10:44:25.234965Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"## Updating the default configuration","metadata":{"papermill":{"duration":0.009321,"end_time":"2022-12-20T21:37:53.173221","exception":false,"start_time":"2022-12-20T21:37:53.163900","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plt.rcParams['figure.figsize'] = (12,8)","metadata":{"papermill":{"duration":0.017895,"end_time":"2022-12-20T21:37:53.200440","exception":false,"start_time":"2022-12-20T21:37:53.182545","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.012945Z","iopub.execute_input":"2022-12-21T09:33:59.013626Z","iopub.status.idle":"2022-12-21T09:33:59.026729Z","shell.execute_reply.started":"2022-12-21T09:33:59.013593Z","shell.execute_reply":"2022-12-21T09:33:59.022335Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"## Loading the dataset","metadata":{"papermill":{"duration":0.009489,"end_time":"2022-12-20T21:37:53.219198","exception":false,"start_time":"2022-12-20T21:37:53.209709","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/language-detection/Language Detection.csv\")\ndf = df.sample(frac=1)\ndf.head()","metadata":{"papermill":{"duration":0.107086,"end_time":"2022-12-20T21:37:53.336284","exception":false,"start_time":"2022-12-20T21:37:53.229198","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.031428Z","iopub.execute_input":"2022-12-21T09:33:59.031901Z","iopub.status.idle":"2022-12-21T09:33:59.142496Z","shell.execute_reply.started":"2022-12-21T09:33:59.031869Z","shell.execute_reply":"2022-12-21T09:33:59.141230Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                                                   Text    Language\n7642                                  va bene iniziamo.     Italian\n3104                     desculpe, eu não entendi isso.  Portugeese\n7898  Yayımlanmış Vikipedi maddeleri ayrıca PediaPre...     Turkish\n783   is replaced with the question \"Can machines do...     English\n1487  അങ്ങനെ അങ്ങനെ നിരന്തരമായ തിരുത്തലുകൾക്കൊടുവിൽ ...   Malayalam","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>Text</th>\n      <th>Language</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7642</th>\n      <td>va bene iniziamo.</td>\n      <td>Italian</td>\n    </tr>\n    <tr>\n      <th>3104</th>\n      <td>desculpe, eu não entendi isso.</td>\n      <td>Portugeese</td>\n    </tr>\n    <tr>\n      <th>7898</th>\n      <td>Yayımlanmış Vikipedi maddeleri ayrıca PediaPre...</td>\n      <td>Turkish</td>\n    </tr>\n    <tr>\n      <th>783</th>\n      <td>is replaced with the question \"Can machines do...</td>\n      <td>English</td>\n    </tr>\n    <tr>\n      <th>1487</th>\n      <td>അങ്ങനെ അങ്ങനെ നിരന്തരമായ തിരുത്തലുകൾക്കൊടുവിൽ ...</td>\n      <td>Malayalam</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Feature Engineering","metadata":{"papermill":{"duration":0.00981,"end_time":"2022-12-20T21:37:53.356451","exception":false,"start_time":"2022-12-20T21:37:53.346641","status":"completed"},"tags":[]}},{"cell_type":"code","source":"df.shape","metadata":{"papermill":{"duration":0.01911,"end_time":"2022-12-20T21:37:53.386194","exception":false,"start_time":"2022-12-20T21:37:53.367084","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.144209Z","iopub.execute_input":"2022-12-21T09:33:59.144646Z","iopub.status.idle":"2022-12-21T09:33:59.152930Z","shell.execute_reply.started":"2022-12-21T09:33:59.144605Z","shell.execute_reply":"2022-12-21T09:33:59.151641Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"(10337, 2)"},"metadata":{}}]},{"cell_type":"code","source":"df.Language.unique()","metadata":{"papermill":{"duration":0.028021,"end_time":"2022-12-20T21:37:53.424066","exception":false,"start_time":"2022-12-20T21:37:53.396045","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.155157Z","iopub.execute_input":"2022-12-21T09:33:59.155609Z","iopub.status.idle":"2022-12-21T09:33:59.169562Z","shell.execute_reply.started":"2022-12-21T09:33:59.155574Z","shell.execute_reply":"2022-12-21T09:33:59.168322Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array(['Italian', 'Portugeese', 'Turkish', 'English', 'Malayalam',\n       'Greek', 'German', 'Sweedish', 'Spanish', 'Dutch', 'French',\n       'Russian', 'Danish', 'Kannada', 'Tamil', 'Arabic', 'Hindi'],\n      dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"df.Language.replace(to_replace=['Portugeese','Sweedish'],value=['Portuguese','Swedish'],inplace=True)","metadata":{"papermill":{"duration":0.02076,"end_time":"2022-12-20T21:37:53.455115","exception":false,"start_time":"2022-12-20T21:37:53.434355","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.171443Z","iopub.execute_input":"2022-12-21T09:33:59.172219Z","iopub.status.idle":"2022-12-21T09:33:59.180691Z","shell.execute_reply.started":"2022-12-21T09:33:59.172166Z","shell.execute_reply":"2022-12-21T09:33:59.179685Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"sns.countplot(df.Language)\nplt.tight_layout();","metadata":{"papermill":{"duration":0.46259,"end_time":"2022-12-20T21:37:53.927646","exception":false,"start_time":"2022-12-20T21:37:53.465056","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.182328Z","iopub.execute_input":"2022-12-21T09:33:59.182788Z","iopub.status.idle":"2022-12-21T09:33:59.617177Z","shell.execute_reply.started":"2022-12-21T09:33:59.182750Z","shell.execute_reply":"2022-12-21T09:33:59.616075Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df = df[df.Language.isin([\"English\",\"French\",\"Dutch\",\"Spanish\",\"Danish\",\"Italian\",\"Swedish\",\"German\",\"Portuguese\",\"Turkish\"])]\ndf.shape","metadata":{"papermill":{"duration":0.024846,"end_time":"2022-12-20T21:37:53.963166","exception":false,"start_time":"2022-12-20T21:37:53.938320","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.618818Z","iopub.execute_input":"2022-12-21T09:33:59.619242Z","iopub.status.idle":"2022-12-21T09:33:59.630461Z","shell.execute_reply.started":"2022-12-21T09:33:59.619203Z","shell.execute_reply":"2022-12-21T09:33:59.629041Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"(7249, 2)"},"metadata":{}}]},{"cell_type":"code","source":"sns.countplot(df.Language);","metadata":{"papermill":{"duration":0.302327,"end_time":"2022-12-20T21:37:54.277217","exception":false,"start_time":"2022-12-20T21:37:53.974890","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.636805Z","iopub.execute_input":"2022-12-21T09:33:59.637240Z","iopub.status.idle":"2022-12-21T09:33:59.911044Z","shell.execute_reply.started":"2022-12-21T09:33:59.637202Z","shell.execute_reply":"2022-12-21T09:33:59.909960Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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7fAk7+3zSfLwJIf32wlJXrX61QUAgF2z6kG7qm6f5IFJXpMkrbVvt9a+luSYJKf31U5P8pg+fUyS17XJuUn2r6o7rWqlAQBgF82jR/uwJFuT/EVVfaqqXl1Vt01yUGvt6r7ONUkO6tMHJ7liZvsre9kPqKoTqmpzVW3eunXrwOoDAMDOzSNo753kXkle1Vr72STfyOIwkSRJa60labuy09baqa21Ta21TRs2bFixygIAwO6YR9C+MsmVrbWP9/m3ZgreX1oYEtL/XtuXX5Xk0JntD+llAACwZq160G6tXZPkiqq6Wy96SJJLk5yV5PhednySM/v0WUmO61cfuV+S62eGmAAAwJq095zu9zeTvKGq9klyWZJfyxT631xVT03yhSSP7+ueneQRSbYk+WZfFwAA1rS5BO3W2gVJNi2x6CFLrNuSPG10nQAAYCX5ZUgAABhA0AYAgAEEbQAAGEDQBgCAAQRtAAAYQNAGAIABBG0AABhA0AYAgAEEbQAAGEDQBgCAAQRtAAAYYO95VwAAWJ9OPvnkeVdhxe2Jj4n50aMNAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADDA3vOuAKwFR55y5LyrMMRHf/Oj864CAPzQErQBtuMVz3rnvKswxNP/9FHzrgLADwVDRwAAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAbYe94VAABgz3GPt75n3lVYcZ8+9qG7tZ0ebQAAGEDQBgCAAQRtAAAYYG5Bu6r2qqpPVdW7+vxhVfXxqtpSVW+qqn16+a36/Ja+fOO86gwAAMs1zx7t307ymZn5FyV5aWvtrkmuS/LUXv7UJNf18pf29QAAYE2bS9CuqkOS/Pskr+7zleTBSd7aVzk9yWP69DF9Pn35Q/r6AACwZs2rR/t/JHl2ku/1+Tsm+Vpr7cY+f2WSg/v0wUmuSJK+/Pq+PgAArFmrHrSr6pFJrm2tnbfC+z2hqjZX1eatW7eu5K4BAGCXzaNH+8gkj66qy5O8MdOQkZcl2b+qFn5A55AkV/Xpq5IcmiR9+e2TfGXbnbbWTm2tbWqtbdqwYcPYRwAAADux6kG7tfac1tohrbWNSZ6Q5H2ttScleX+SY/tqxyc5s0+f1efTl7+vtdZWscoAALDL1tJ1tH8/yTOrakumMdiv6eWvSXLHXv7MJCfOqX4AALBse+98lXFaax9I8oE+fVmS+yyxzreSPG5VKwYAADfTWurRBgCAPYagDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwgKANAAADCNoAADCAoA0AAAMI2gAAMICgDQAAAwjaAAAwwKoH7ao6tKreX1WXVtUlVfXbvfwOVXVOVX2u/z2gl1dVvbyqtlTVhVV1r9WuMwAA7Kp59GjfmORZrbUjktwvydOq6ogkJyZ5b2vt8CTv7fNJ8vAkh/fbCUletfpVBgCAXbPqQbu1dnVr7fw+/c9JPpPk4CTHJDm9r3Z6ksf06WOSvK5Nzk2yf1XdaXVrDQAAu2auY7SramOSn03y8SQHtdau7ouuSXJQnz44yRUzm13ZywAAYM2aW9Cuqv2SvC3J77TW/ml2WWutJWm7uL8TqmpzVW3eunXrCtYUAAB23VyCdlXdMlPIfkNr7e29+EsLQ0L632t7+VVJDp3Z/JBe9gNaa6e21ja11jZt2LBhXOUBAGAZ5nHVkUrymiSfaa29ZGbRWUmO79PHJzlzpvy4fvWR+yW5fmaICQAArEl7z+E+j0zy5CQXVdUFvewPkrwwyZur6qlJvpDk8X3Z2UkekWRLkm8m+bVVrS0AAOyGVQ/arbWPJKntLH7IEuu3JE8bWikAAFhhfhkSAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAYQtAEAYABBGwAABhC0AQBgAEEbAAAGELQBAGAAQRsAAAZYN0G7qh5WVZ+tqi1VdeK86wMAADuyLoJ2Ve2V5M+SPDzJEUmeWFVHzLdWAACwfesiaCe5T5ItrbXLWmvfTvLGJMfMuU4AALBd6yVoH5zkipn5K3sZAACsSdVam3cddqqqjk3ysNbaf+jzT05y39ba02fWOSHJCX32bkk+u+oVvakDk3x53pVYI7TFIm2xSFss0haLtMUibbFIWyzSFovWQlv8eGttw1IL9l7tmuymq5IcOjN/SC/7vtbaqUlOXc1K7UxVbW6tbZp3PdYCbbFIWyzSFou0xSJtsUhbLNIWi7TForXeFutl6MgnkxxeVYdV1T5JnpDkrDnXCQAAtmtd9Gi31m6sqqcneU+SvZKc1lq7ZM7VAgCA7VoXQTtJWmtnJzl73vXYRWtqKMucaYtF2mKRtlikLRZpi0XaYpG2WKQtFq3ptlgXX4YEAID1Zr2M0QYAgHVF0N6Jqvp6/7uxqn5lGetvrKqL+/Smqnr56Drurqr6blVdUFUXV9Vbquo2u7DtPavqESPrt9ZU1R17e11QVddU1VUz8/vsZNvvHxfblO/wGKmqo6rqXStR/5U2c/ws3E68GftaeJ79WFW9dQfrLdmO81JVB1XVX1bVZVV1XlV9rKp+cd71moeqem5VXVJVF/bj4b4ruO91d3yMbI++/7/fyfKvr+T9raSZc8clVfXpqnpWVe00j1TVHyxjndf2SwKvaUucPzcOup81+xqS7P6xsIP9vXpHvxxeVR+oqlW9Qsm6GaO9BmxM8itJ/nK5G7TWNifZPKpCK+BfWmv3TJKqekOS/5TkJTvbqKr2TnLPJJuy/sbN77bW2lcyPe5U1clJvt5ae/HOtuvttb19rvVjZEe+f/yslNba/02y5l8kk6SqKsk7kpzeWvuVXvbjSR69zO33bq3dOK6Gq6eq7p/kkUnu1Vq7oaoOTLLDN5+7Y70cH6vRHq21B6zk/lbZ7GvPv8r0unq7JCftZLs/SPL/ja3aqtnu+bOfW6q19r3VrdJc7O6xsKSF31tZS/RoL98Lk/xCf+f1jN5z8uGqOr/fbnLSm30nWVX36b1dn6qqv6+qu/Xyp1TV26vq3VX1uar641V+XAs+nOSuVXWHqnpH74U5t6ru3ut5clW9vqo+muT1SZ6f5Jd7e/xyX/67CzvrveQb+/R/qarPVtVHquqMhfVm31lW1YFVdXmf3quq/qSqPtnr8R97+Z2q6kO12Av/C7386N6259fUM7/fajXatr0nMz1vR/Xj46wkl26zzU/04+DntjlGHjTTu/GpqvqRvsl+VfXWqvqHqnpDPwmvWVV1eVX9Yf9/XFRVP9XLN1TVOb3n4tVV9YUeQGa3nf1E6Ker6hO9PS6sqsP7antV1Z/3/fxtVd16lR/iggcn+XZr7X8uFLTWvtBaO2UHx/APHBd9/oNVdWZNveIvrKon9cd9UVXdpW/3qKr6eD8u/q6qDurlJ1fVaf25dFlV/dY8GiLJnZJ8ubV2Q2+HLyc5uKre3ut5TFX9S1XtU1X7VtVlvfwu/dx3Xm+XhWPlsP6cvqiq/tvCnayj4+Mm7dFa+7/9ufHH/XF9oqru2h/LLv9/Z841S54X+7IX1NRLeO7CPtea1tq1mX5s7uk1eUpVvWJheVW9qz9PXpjk1v1xvqEvO67/7z9dVa+f2e0Da3qdvazWQe928v1j+7NV9bokFyc5tKp+b+Yc8ocz631mqWO8qu7aj59P13T+vUvf/bp4DVniWFgyZ/Xj4QNLPaZevqmmc/Br+3Pioqp6xsxdPa4///5x9vky8oG57eCWqdcySY5K8q6Z8tsk2bdPH55kc5/emOTibbfJ9A5t7z79b5O8rU8/JcllSW6fZN8kX0hy6Co/tr2TnJnkN5KckuSkXv7gJBf06ZOTnJfk1jP1fsXMvk5O8rsz8xf3tvi5JBf0x/YjST63sF6SDyTZ1KcPTHJ5nz4hyfP69K0y9fgeluRZSZ7by/fq+zswyYeS3LaX/36S/7oKbXdykt9N8tokx27nePlGksNmj4tMv1r6qST3WOIYeWeSI/v0fv3/clSS6zP9SNMtknwsyc/P+3nR6/jd/r9duP1yL788yW/26f+c5NV9+hVJntOnH5akJTlwm3bbmMXnzylJntSn90ly6778xiT37OVvTvKrc3r8v5XkpdtZtr1jeNvj4qgkX8sUzG6V6Ye4/rAv++0k/6NPH5DFL6//hyR/OnMc/n3f9sAkX0lyyzm0xX79GPjHJK9M8qB+/F7Wl7840+8hHNmXndHL35vk8D593yTv69NnJTmuTz9tvR0fS7XHzHNj4Rx2XBaf+7v8/51pk5ucF/t0S/KoPv3HC8fjWrgt1H2bsq8lOSg3fW15V5Kjtt0uyU/39l04h9yh/31tkrdkOl8ekWTLvB/vdtpg9vz5V/3Y/V6S+/XlR2e6mkb1x/KuJA/c0TGe5ONJfrFP75sppxyVNfoasoxjYXs5a7uPKT1XJLl3knNm9rn/zPKF59cjkvzd6Mdo6Mjuu2WSV1TVPTM9YX5yJ+vfPsnpvdel9e0XvLe1dn2SVNWlSX48yRUrXuObunVVXdCnP5zkNZmeqL+UJK2199U0Lvl2fZ2zWmv/sov3cWSSM1tr30ryrap65zK2OTrJ3Wd6Im6f6Un2ySSnVdUtk7yjtXZBVT0o08n0o/0N7T6ZnnRrwSdaa5+fmd+Q6Q3NY1trly6x/keTvKT31ry9tXZlf0yfaK1dmST9/7UxyUdGVnyZdjR05O3973lJHtunfz7JLyZJa+3dVXXdTvb/sSTPrapDMrXH53p7fL61dsHM/jfuVu1XWFX9WabH+O1Mb5iXOoa/nZseF59srV3d9/F/kvxtL78oyb/p04ckeVNV3SnTMT67/V+3qef0hqq6NtML1JUr/fh2pLX29aq6d5Jf6HV+U5ITk/yfqvrXSe6TaVjaAzOFwQ/X9MnTA5K8ZaaD7Vb975Hp56FMn6C9aIm7XbPHx1LtUYvfYThj5u9L+/TN+f/e5LzYy7+dKZwlUzv8u5V6fGvEg5O8pU2fnqS19tWZZe9o07CLS9dqT362OX/W9AnwF1pr5/aio/vtU31+v0znkC9miWO8pk9AD26t/VWS9NfcrPHXkJ3ZUc7a2WO6LMlPVNUpSf46i+fV5AdfnzYOqPcPELR33zOSfCnJPTK9o/rWTtb/oyTvb639Yn9CfWBm2Q0z09/N6v1fbhKUdvKJ0jd2sOzG/OBQpH2Xcf+z28yuX5l6RN+z7QZV9cAk/z7Ja6vqJUmuy/Su9YnLuL8Rvv8YavoCx+w4zG3b6/pMJ8mfzzbDSZKktfbCqvrrTO+yP1pVD+2L5nV83BwLdd7t+rbW/rKqPp7p/312TcMvLstN22NeQ0cuyWIYTGvtaTUNhdmc6f98k2O4qo7KTY+L2cfzvZn572Wx7U5J8pLW2ll9HydvZ/u5HR+tte9mOq99oKouSnJ8pk+bHp7kO0n+LlNv415Jfi/T8+ZrO3iztsNrz67142M77ZH84ONamN7t/29r7UPbnhdba69L8p3Wu+2W2m4tqaqfyFTHa7N7ryXbmm2zNTlMYjtmzw2V5L+31v7X7Ao9P+zqMb4mzhHLsc2xcFK2n7N29ry4rqrukeShmb5/9vgkv77NtqvSFsZoL98/ZxqqsOD2Sa7u75qfnOnFY0dun+lj4WT6aGyt+nCSJyXfDwVfbq390xLrbdselye5V9/uXpk+Jk+mXtpH1TQuc79MXxCa3ebefXp2HN17kvxG76FJVf1kVd22pi+afam19udJXt3v79wkR9biWMfbVtXOPl1YSZdn8TE8Oj/4ScW2vp2pR/e4WuIKNlV1l9baRa21F2XqpfqpFa7rvH0008kuVXV0po/Lt6ufcC9rrb080ycBdx9ew13zviT7VtVvzJQtXLlnyWP4ZtzX7Pnj+B2tOA9VdbdaHCOdTF8a/kKm88nvJPlYa21rkjtmGj51cT+vfL6qHtf3Uf2FMZmOlSf06Sdt5z7X7PGxg/ZIkl+e+bvw6dtu/3+3c15cN6pqQ5L/mWm4SMt0Tr1nVd2iqg7N9GnIgu8sPKcyPf8eV1V37Pu5wypWezW8J8mv99fNVNXBNX1ZcEmttX9OcmVVPaavf6vahSuJrQVLHAu7mrNm93Vgklu01t6W5HmZ4/Nizb6rWYMuTPLdqvp0pl6ZVyZ5W1Udl+Td2XFvbzKNkTu9qp6X6WOMterkTB9DXpjkm9n+Sf/9SU7sH9n89yRvyxQgL8k0/OQfk6S19smavvh1YaZ3phdl6tlNpnGbb66qE/KDbfLqTB/nnF9TF/vWJI/JNC7r96rqO0m+nmkM59aqekqSM6pq4WPn5y3c/yr48yRn9uNip8dBa+0bVfXIJOfU9GWm2Tcxv1NV/yZTT+YlSf4myf3HVHtFzA49SpJ3t9Z2dIm/P8z0f3pypoBxTaY3bNvz+CRP7v/vazJdbeB2O1h/VbXWWn9Re2lVPTvTcfqNTN8TeEuWPoZ318mZhlhclylgHLbj1VfdfklOqar9M/VIbsk0Tv0bmYY6fKivd2GSH53paX1Sklf18+Itk7wxyaczjU//y6r6/Uwheilr+fjYXns8MskB/fx6Q5KFT+JOzu7/f4/KNufFFaj/aAvnjltmap/XZ/GKVx/NNHTm0iSfSXL+zHanJrmwqs5vrT2pql6Q5INV9d1MQyyesjrVH6+19rd92NXH+ifNX0/yq5l6YbfnyUn+V1U9P9OnSI8bXtGbb0fHwq7mrFkHJ/mLWrxU4HNWprq7zi9DMlxV7dfHLN4m0wvuCa2183e2HXuW/kbou621G2u6/NmrdjBsAPY4NV1ZadPCuGJgz6dHm9Vwak0XkN830zWHhewfTnfO9AnGLTINo/l/51wfABhKjzYAAAzgy5AAADCAoA0AAAMI2gAAMICgDbCO9MtCArAOCNoAADCAoA2wzlXVo6rq41X1qar6u6o6qJefXFWnVdUHquqyqvqtmW3+S1V9tqo+UlVnVNXv9vIPVNWmPn1gv/ZzqmpjVX24qs7vtwf08ltU1Sur6h+q6pyqOruqju3L7l1VH6yq86rqPVV1p1VuGoC5ErQB1r+PJLlfa+1nM/264rNnlv1Ukodm+inrk6rqllX1c0l+Kck9kjw8yaZl3Me1Sf5da+1emX4+/OW9/LGZfgXziEy/THf/JOk/lX1KkmNba/dOclqSF9yMxwiw7vjBGoD175Akb+o9xvtk+gnrBX/dWrshyQ1VdW2mn0Q/MsmZrbVvJflWVb1zGfdxyySvqKp7ZvoZ6J/s5T+f5C2tte8luaaq3t/L75bkZ5Kc039Ceq8kV9+Mxwiw7gjaAOvfKUle0lo7q6qOSnLyzLIbZqa/m52f92/M4qed+86UPyPJlzL1gt8iybd2sp9Kcklr7f47WQ9gj2XoCMD6d/skV/Xp45ex/keTPKqq9q2q/ZI8cmbZ5Unu3aeP3eY+ru4910/O1EO9sK9f6mO1D0pyVC//bJINVfX9oSRV9dO79KgA1jlBG2B9uU1VXTlze2amHuy3VNV5Sb68sx201j6Z5KwkFyb5myQXJbm+L35xkt+oqk8lOXBms1cmOb6qPp1p3Pc3evnbklyZ5NIk/zvJ+Umub619O1NQf1Hf5oIkD9jtRw2wDlVrbd51AGCVVdV+rbWvV9VtknwoyQmttfNv5r7umOQTSY5srV2zkvUFWI+M0Qb44XRqVR2RaRz26bsbsrt3VdX+mb6I+UdCNsBEjzYAAAxgjDYAAAwgaAMAwACCNgAADCBoAwDAAII2AAAMIGgDAMAA/z+HQq9WHHH1HgAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"The target language classes are considerably imbalanced so they need to be accounted for later.","metadata":{"papermill":{"duration":0.011188,"end_time":"2022-12-20T21:37:54.299319","exception":false,"start_time":"2022-12-20T21:37:54.288131","status":"completed"},"tags":[]}},{"cell_type":"code","source":"wc = WordCloud(width=600,height=300,random_state=42).generate(' '.join(df.Text))\nplt.imshow(wc);","metadata":{"papermill":{"duration":1.96613,"end_time":"2022-12-20T21:37:56.276269","exception":false,"start_time":"2022-12-20T21:37:54.310139","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:33:59.912660Z","iopub.execute_input":"2022-12-21T09:33:59.913028Z","iopub.status.idle":"2022-12-21T09:34:01.851695Z","shell.execute_reply.started":"2022-12-21T09:33:59.912982Z","shell.execute_reply":"2022-12-21T09:34:01.850722Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df['num_words'] = df.Text.apply(len)\nsns.distplot(df.num_words);","metadata":{"papermill":{"duration":0.410981,"end_time":"2022-12-20T21:37:56.702212","exception":false,"start_time":"2022-12-20T21:37:56.291231","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:01.852989Z","iopub.execute_input":"2022-12-21T09:34:01.853458Z","iopub.status.idle":"2022-12-21T09:34:02.330977Z","shell.execute_reply.started":"2022-12-21T09:34:01.853404Z","shell.execute_reply":"2022-12-21T09:34:02.329771Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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AC4YoTvG3F2lObSXVEe6p1p15OjQuI6dyevKVbWF7pZMSss6WhjpBgAAWACE7hgrVYJJjLW2lyzvysls6q3gH3vhpCTpurVLav7+K7pb9fzgSM3nAwAAYGqE7hgrh6E7na7tbcqmU+rrzOnoFCPdjx84pZZMSldd1FPz9+/vyumF4yNsBw8AAHCBCN0xVg27texIWTXdWt2PvXBSV1/UrZZM7W95f2dOxbLrwInRmq8BAADAyxG6Y6w60j2X0L2iu/VlEykLpYqePHRa169dOqfv398VLC1IiwkAAMCFIXTHWKkchu4a20skaWVP7mUj3bsODylfqui6OYbuvs5q6D4zp+sAAABwPkJ3jBUr82svOTVa1HixfPbY4y8Gkyivv2TJnL5/W0ta/V05PT9A6AYAALgQhO4YO9teUuOSgZK0MtzUZuIGOY8fOKWV3a1nN7yZiw39HYx0AwAAXCBCd4wVq+0lNW6OIwUj3dL5ywY+9uLJOY9yV23o79TzgyNy93ldDwAAAEJ3rJXC9pLsHEa61ywNRrN/9PxxSdLgcF4HTozpuovn1s9dtaG/U6fHijo+UpjX9QAAACB0x1o5HOlOz6Gne11fh976ilX61Hf2aPeR4Xn3c1dtWN4pSfR1AwAAXABCd4wVK3NfvUSSPvK2q9TVmtX//eWfaOcLJ5VN25w2xZloQ3+HJJYNBAAAuBCE7hgrzWNzHEnq7czpP229Wk8eOq3P/WC/Nl/Uo9Zsel41XNTTptZsismUAAAAF4DQHWPz2Ryn6q2vWKW3XrNKhXJF1128ZN41pFKmS/s6Cd0AAAAXIBN1AZjefNtLqn5/61UaHM7rra9YdUF1bFjeqScOnLyg5wAAAGhmjHTH2HzbS6r6OnO6//94rV69btkF1bGhv0MHT46dt+EOAAAAakfojrH5bI5TDxv6O+Uu7TvGZEoAAID5qGvoNrNbzWy3me0xs7uneDxnZl8KH99hZusmPPbB8PhuM7tl0nVpM3vczP6hnvVHrVh2maS0RR+6JdHXDQAAME91C91mlpb0SUm3Sdos6Z1mtnnSae+VdNLdL5P0cUkfDa/dLGmbpKsk3SrpU+HzVb1f0q561R4X5UpF6ZTJIg7d6/s6ZCY9P8BINwAAwHzUc6T7Bkl73H2vuxck3Sdp66Rztkr6fHj7K5JutiBhbpV0n7vn3X2fpD3h88nM1kh6q6Q/r2PtsVCseOStJZLU1pLW6iVtjHQDAADMUz1D92pJBybcPxgem/Icdy9JOi2pd5Zr/1jS/yOpsuAVx0yp7Mqm4tF2v6GfZQMBAADmKx6JrkZm9q8lDbj7ozWce6eZ7TSznYODg4tQ3cIrVypKx2CkWwpC997BEVXCyZ0AAACoXT1D9yFJF0+4vyY8NuU5ZpaR1CPp+AzX3iTpbWa2X0G7ypvM7K+m+ubu/ml33+LuW/r7+y/81USgWHZl4jLSvbxDY8WyDg+NR10KAABA4tQz0T0iaaOZrTezFgUTI7dPOme7pDvC27dL+pa7e3h8W7i6yXpJGyU97O4fdPc17r4ufL5vufu76/gaIlWq+LzX6F5ol/aFK5gM0GICAAAwV3UL3WGP9vskPaRgpZH73f1pM/uImb0tPO0zknrNbI+kD0i6O7z2aUn3S3pG0oOS7nL3ptuZpVypxGIipRSMdEssGwgAADAfdd0G3t0fkPTApGMfmnB7XNLbp7n2Hkn3zPDc35H0nYWoM67i1F7S35lTV2tGewdZNhAAAGCu4pHoMKVSOT4j3WbGCiYAAADzROiOsXKMerollg0EAACYL0J3jAWb48TnLdqwvENHh/IaHi9GXQoAAECixCfR4WXiONItib5uAACAOSJ0x1ipXIll6KbFBAAAYG4I3TFWLHtsJlJK0iW97cqkjNANAAAwR4TuGAvaS+LzFmXTKa3tbdfzA7SXAAAAzEV8Eh1ephSjzXGqWMEEAABg7gjdMVWuuCquWPV0S0Ho3n98RKVyJepSAAAAEoPQHVPliktSrNpLJGlDf4eKZdeBk2NRlwIAAJAY8Up0OKs6khy79pLl4QomA7SYAAAA1IrQHVPFuI5097FsIAAAwFxloi4AUzvbXlLDSPe9O16sdzln9bRn1deZI3QDAADMQbyGUXFWsdpeErOJlFLQ1/08u1ICAADUjJHumCrFpL1kqlF0d+mZl4b0xR+/ILPzfyl4141rF6s0AACAxGCkO6bKMZ1IKUn9XTmNFcsaKZSjLgUAACARCN0xdW4iZTxDtyQNDucjrgQAACAZCN0xdW4iZfzeov7OIHQfI3QDAADUJH6JDpImrNMdw5HunvasMinT4BlCNwAAQC0I3TEV5/aSlJn6u3K0lwAAANSI0B1T5XJ820skqa8zx0g3AABAjeKZ6KBiJb6rl0jBZMqTI4Wz64kDAABgeoTumCqV49teIgWh2yUdP1OIuhQAAIDYI3THVDkmm+NMp7qCCS0mAAAAs4tnokPs20v6qqF7eDziSgAAAOKP0B1TpbIrZcFKIXHUkklpSXuWFUwAAABqQOiOqXLFY7tySVU/K5gAAADUJN6prokVy5XYTqKsqq7VXXGPuhQAAIBYI3THVLniiQjdxbJraKwYdSkAAACxRuiOqVJC2kskVjABAACYTbxTXRNLSnuJJCZTAgAAzILQHVPBRMp4h+7OXEat2RShGwAAYBaE7pgqlT22G+NUmZn6O3M6RnsJAADAjOKd6ppYsRL/9hIpaDEZYKQbAABgRoTumEpCe4kkLe9q1fB4SaOFUtSlAAAAxBahO6aS0F4iSSt7WiVJR4bYDh4AAGA68U91TapYriRipHtFdxC6j54mdAMAAEyH0B1TweY48X97ulszasumdXSIvm4AAIDpxD/VNaliAnaklIIVTFZ052gvAQAAmAGhO6ZKCWkvkYIWk6ND43L3qEsBAACIJUJ3TCWlvUQKJlPmSxWdHitGXQoAAEAsJSPVNRl3VykhSwZK0spuVjABAACYCaE7hgrliiQloqdbCtbqlljBBAAAYDqE7hjKl5IVutta0uppy+ooO1MCAABMqabQbWZ/Y2ZvNTNC+iLIF8PQnU7Of+6V3a06wkg3AADAlGpNdZ+S9C5Jz5nZH5rZpjrW1PSS1l4iBSuYDA7nVQxrBwAAwDk1hW53/4a7/ztJ10vaL+kbZvZDM/s1M8vWs8BmlC+WJSkxEyklaUV3TmV37Ts2EnUpAAAAsVNz/4KZ9Ur6VUn/m6THJX1CQQj/el0qa2LneroT1F7SE0ym3H1kOOJKAAAA4qfWnu6vSfqepHZJ/4u7v83dv+Tuvymps54FNqNCNXQnaKS7vzOnlBG6AQAAppKp8bw/c/cHJh4ws5y75919Sx3qampJHOnOpFPq7czpWUI3AADAy9Sa6v7zFMd+tJCF4Jx8KezpTtBESilYwWT30aGoywAAAIidGUe6zWylpNWS2szsOknVFNitoNUEdZDE9hJJWtXTqicPndbpsaJ62phfCwAAUDVbe8ktCiZPrpH0sQnHhyX9Xp1qanpJbC+RpNVL2iRJTx86rddd1hdxNQAAAPExY+h2989L+ryZ/ZK7f3WRamp6SR3probunxK6AQAAzjNbe8m73f2vJK0zsw9MftzdPzbFZbhASe3pbs9ltGZpm548dDrqUgAAAGJltvaSjvArywIuorPtJQnaBr7qFWt69ORBQjcAAMBEs7WX/H/h199fnHIgTWgvSdhItyRds3qJHnjyiE6PFtXTzmRKAAAAqfbNcf6rmXWbWdbMvmlmg2b27noX16zyiQ7dPZJEiwkAAMAEtfYvvNndhyT9a0n7JV0m6XfqVVSzq4budIJD908PnYq2EAAAgBipNXRX21DeKunL7s4wZh0VShWlUyaz5IXunvas1i5r11OMdAMAAJxV6zbw/2Bmz0oak/QbZtYvabx+ZTW3fKmcyNaSqmvW9OgnB05FXQYAAEBs1DTS7e53S3qdpC3uXpQ0ImlrPQtrZoVSJdmhe3WPDp4c08mRQtSlAAAAxMJc1qS7QtI7zOw9km6X9ObZLjCzW81st5ntMbO7p3g8Z2ZfCh/fYWbrJjz2wfD4bjO7JTzWamYPm9lPzOxpM2vIVVUKpUoilwusegWTKQEAAM5T6+olX5D03yS9XtKrwz9bZrkmLemTkm6TtFnSO81s86TT3ivppLtfJunjkj4aXrtZ0jZJV0m6VdKnwufLS3qTu79S0rWSbjWz19TyGpIkn/CR7qsI3QAAAOeptad7i6TN7u5zeO4bJO1x972SZGb3KWhJeWbCOVslfTi8/RVJ/8OC2YNbJd3n7nlJ+8xsj6Qb3P1Hks6E52fDP3OpKRGqEymTqqctq3W97WySAwAAEKq1h+EpSSvn+NyrJR2YcP9geGzKc9y9JOm0pN6ZrjWztJk9IWlA0tfdfccc64q9QrmiTDq5oVuSrlmzhJFuAACAUK2hu0/SM2b2kJltr/6pZ2HTcfeyu18raY2kG8zs6qnOM7M7zWynme0cHBxc1BovVLB6SXJ7uiXpmtXdOnRqTMfP5KMuBQAAIHK1tpd8eB7PfUjSxRPurwmPTXXOQTPLSOqRdLyWa939lJl9W0HP91OTv7m7f1rSpyVpy5YtiWpBSfrqJVKwHbwU9HW/cdPyaIsBAACIWK1LBv6Lgp0os+HtRyQ9Nstlj0jaaGbrzaxFwcTIyaPj2yXdEd6+XdK3wr7x7ZK2haubrJe0UdLDZtZvZkskyczaJP2ipGdreQ1JEqxekuzQfdXqbkmirxsAAEA1jnSb2f8u6U5JyyRtUNBf/T8l3TzdNe5eMrP3SXpIUlrSZ939aTP7iKSd7r5d0mckfSGcKHlCQTBXeN79CiZdliTd5e5lM1sl6fPhSiYpSfe7+z/M54XHWb5UUTrh7SXdrVld2tdBXzcAAIBqby+5S8FqJDskyd2fM7NZewbc/QFJD0w69qEJt8clvX2aa++RdM+kYz+VdF2NNSdWoVRRR67Wtya+rlnTo4f3nYi6DAAAgMjVOpyad/ez2wuG/deJ6pNOkqSv0111zeoeHT49rsFhJlMCAIDmVmvo/hcz+z1JbWb2i5K+LOnv61dWc2uEJQOlIHRL0lO0mAAAgCZXa+i+W9KgpCcl/bqClpH/UK+iml2+mPwlA6VgZ0oz6adMpgQAAE2upsZhd6+Y2d9K+lt3T9ai1wlUKCe3veTeHS+ed7+vI6cHnzqs/q7clOe/68a1i1EWAABApGYcTrXAh83smKTdknab2aCZfWim6zB/7h5sA98A7SWStHppmw6dGou6DAAAgEjN1sPw25JukvRqd1/m7ssk3SjpJjP77bpX14RKFVfF1RDtJZK0ekmbhsZLGhovRl0KAABAZGZLdr8i6Z3uvq96wN33Snq3pPfUs7BmVShVJCmx7SWTrV7SJkl66SSj3QAAoHnNFrqz7n5s8sGwrztbn5Ka29nQ3SDtJauWtMokHaTFBAAANLHZQndhno9hngrlIHSnG2SkO5dJq78rp5cI3QAAoInNtnrJK81saIrjJqm1DvU0vXwxCN3ZBunploIWkz0DZ6IuAwAAIDIzhm53Ty9WIQgUymVJapjVSyTpoiVtevzAKQ2NF9XdSlcSAABoPo0znNog8g02kVKSVnQH/ygyMMR28AAAoDkRumPmXOhunLdmRXewMc7RofGIKwEAAIhG4yS7BtFoq5dIUmcuo/aWNKEbAAA0LUJ3zDTaOt2SZGZa0d2qgWHaSwAAQHMidMdMI7aXSNLyrpyODo3L3aMuBQAAYNE1VrJrANWR7kZavUQKJlPmSxWdHmM7eAAA0HwI3TFTXTKwkdpLpHMrmBxlBRMAANCECN0x04g93ZK0oitYwWRgmMmUAACg+RC6Y+ZsT3e6sd6a9lxGXbkMK5gAAICm1FjJrgE06ki3FLSY0F4CAACaEaE7Zqoj3ekGDN3Lu3MaGB5XhRVMAABAkyF0x0wjbgNftaK7VcWy69QoK5gAAIDmQuiOmUKpopZ0SmYNGLq72A4eAAA0J0J3zBRKFbVkGvNtWX522UBCNwAAaC6Nme4SLF8qK9egobs1m1ZPW5bQDQAAmk5jprsEa+SRbkla0Z3TwDArmAAAgObSuOkuoQrlBg/dXa0aGM6rXGEFEwAA0DwaN90lVHUiZaNa3t2qcsV1cqQQdSkAAACLpnHTXULlSxXlso37tvR1tkiSjo3QYgIAAJpH46a7hGr0ke6+zmDZwGNnGOkGAADNo3HTXUI1+kTK9pa02rJpHTvDSDcAAGgejZvuEipYMjAddRl1Y2bq7WzRcUI3AABoIoTumMk3+Ei3FLSY0F4CAACaSWOnuwRq9CUDpWAy5emxogqlStSlAAAALIrGTncJlC9WGnZHyqrqZMrjrGACAACaRGOnuwQqlBs/dPdWQzctJgAAoEk0drpLoEZfMlCS+jrCtbqZTAkAAJpEY6e7BGr0JQMlKZdNq6s1w2RKAADQNBo73SVQoy8ZWBWsYMJINwAAaA6E7hgplSuquBp+pFsKVjBhrW4AANAsGj/dJUihHCyh1wyhu7cjp5FCWadHi1GXAgAAUHeNn+4SJF8MQnejr14inVs2cN/xkYgrAQAAqL/GT3cJ0kwj3X2dwQom+46dibgSAACA+mv8dJcg1R0aG33JQEla1tEik7Tv2GjUpQAAANRd46e7BMmXypKCJfUaXSad0pL2rPYdo70EAAA0PkJ3jOSbaKRbCvq6aS8BAADNoDnSXUJU20uaYSKlFIbuwRG5e9SlAAAA1FVzpLuEONvT3TShu0UjhbIGWa8bAAA0uOZIdwmRb7KR7t7qsoGD9HUDAIDG1hzpLiGab6Q7DN1MpgQAAA2uOdJdQjTTOt2StKQ9q5Z0itANAAAaXnOku4Q4u2RgpvGXDJSklJku6W0ndAMAgIZH6I6RZmsvkaR1fR2EbgAA0PCaJ90lQDPtSFl1aV+HXjg+qnKFZQMBAEDjap50lwBnVy/JNs/bsr6vQ4VyRS+dGou6FAAAgLppnnSXAM22I6UUhG6JFUwAAEBja550lwDN2F5C6AYAAM2gedJdAhTKFWXTplTKoi5l0fR35dTRkiZ0AwCAhkbojpF8sdI0ywVWmZnW93doL6EbAAA0MEJ3jBTK5aZaLrBqfV+n9hO6AQBAA2u+hBdjhVKlqfq5q9b3tuvgydGzmwMBAAA0mromPDO71cx2m9keM7t7isdzZval8PEdZrZuwmMfDI/vNrNbwmMXm9m3zewZM3vazN5fz/oXW75UaarlAqvW93eo4tKBE6NRlwIAAFAXdUt4ZpaW9ElJt0naLOmdZrZ50mnvlXTS3S+T9HFJHw2v3Sxpm6SrJN0q6VPh85Uk/Xt33yzpNZLumuI5E6tpR7r7OiVJewdpMQEAAI2pngnvBkl73H2vuxck3Sdp66Rztkr6fHj7K5JuNjMLj9/n7nl33ydpj6Qb3P2wuz8mSe4+LGmXpNV1fA2LqlCqNGdPd2+wbOD+44RuAADQmOqZ8FZLOjDh/kG9PCCfPcfdS5JOS+qt5dqwFeU6STum+uZmdqeZ7TSznYODg/N/FYsoX6oo14Shu6c9q2UdLSwbCAAAGlYiE56ZdUr6qqTfcvehqc5x90+7+xZ339Lf37+4Bc5Ts450S8EmObSXAACARlXPhHdI0sUT7q8Jj015jpllJPVIOj7TtWaWVRC4v+juf1OXyiOSL1fU0mTrdFet7+tgpBsAADSseobuRyRtNLP1ZtaiYGLk9knnbJd0R3j7dknfcncPj28LVzdZL2mjpIfDfu/PSNrl7h+rY+2RaNaJlFIQugeG8xrJl6IuBQAAYMHVLeGFPdrvk/SQggmP97v702b2ETN7W3jaZyT1mtkeSR+QdHd47dOS7pf0jKQHJd3l7mVJN0n6FUlvMrMnwj9vqddrWGz5UrkplwyUpEv7gsmUjHYDAIBGlKnnk7v7A5IemHTsQxNuj0t6+zTX3iPpnknHvi/JFr7SeCiUKso16Uj3xhXBsoG7jwzr6tU9EVcDAACwsJoz4cVUM0+kXNfboZZMSrsOTzkvFgAAINGaM+HFVLMuGShJmXRKm1Z06dkjw1GXAgAAsOCaM+HFVDOPdEvSlau6tOvwkIK5tAAAAI2jeRNeDBXKzR66u3V8pKDB4XzUpQAAACyo5k14MVMqV1SuuHJNuk63JF2xsluStIsWEwAA0GAI3TFRKFckqalHujevCkM3kykBAECDad6EFzOFUhi6m3TJQEnqac/qop5WQjcAAGg4zZvwYuZs6G7ikW5JumJVN6EbAAA0nOZOeDGSD0N3sy4ZWHXlqi49PziifKkcdSkAAAALprkTXozkGemWFKxgUq64njt6JupSAAAAFkxzJ7wYKTDSLWnCCia0mAAAgAbS3AkvRqrtFM28ZKAkre/rUGs2xc6UAACgoRC6Y4KJlIF0yrRpRRcj3QAAoKE0d8KLEdbpPueKld1sBw8AABoKCS8m8kV6uquuXNWlk6NFHR1iO3gAANAYMlEXgECzjnTfu+PFlx07dGpckvSpb+/RFeEulZL0rhvXLlpdAAAAC6m5El6MsSPlOauXtCltpv3HR6IuBQAAYEGQ8GKCiZTntGRSWrOsTXuPEboBAEBjIOHFBEsGnm99X4deOjWmfJGdKQEAQPIRumOCHSnPd2lfpyouvXBiNOpSAAAALhgJLyaqEylZvSSwdlm7Uibto8UEAAA0ABJeTFSXDGQiZaAlk9Kape3aO3gm6lIAAAAuGAkvJsaLZbVkUkqlLOpSYmN9X4cOnRo72+8OAACQVITumBgtlNXewiTKiS7t61DFpReP09cNAACSjdAdE6OFstqzhO6J1vbS1w0AABoDoTsmxooltTHSfZ5cJq3VS1ivGwAAJB+hOyaC9pJM1GXEzqX9nTp0cuzs5kEAAABJROiOidFCmZHuKazv61DZXS+cYLQbAAAkF6E7JsaYSDmlS3rblUmZfnZkOOpSAAAA5o3QHROjhRKhewq5TFqX9nfo2SPDcveoywEAAJgXQndMjBXKasvS0z2VK1Z26/hIQc8P0mICAACSidAdE6NF2kumc8XKLknSN3cdjbgSAACA+SF0xwSb40xvSXuLVvW06huEbgAAkFCE7hgoV1yFUoUlA2dwxcpuPfrCSZ0cKURdCgAAwJwRumNgtFCSJEa6Z3Dlqi5VXPr27oGoSwEAAJgzQncMjBXKksQ63TO4aEmb+rty+uYuQjcAAEgeQncMjIahm5Hu6aXMdPMVy/UvPxtkd0oAAJA4hO4YIHTX5uYrV+hMvqSH952IuhQAAIA5IXTHwFgx6OluYyLljF5/WZ9ymRSrmAAAgMQhdMcAI921aWtJ66bL+vSNXUfZnRIAACQKoTsGqqG7LUvons0vXLlCB0+O6WdHz0RdCgAAQM0I3TEwxkh3zW6+crkk0WICAAAShdAdA+faS+jpns2K7lZds7qHLeEBAECiELpjoLo5Dut01+bmK5fr8QOndOxMPupSAAAAakLojgHaS+bmF65cIXfp28+yUQ4AAEgGQncMjBbLyqZN2TRvRy2uuqhbK7tb6esGAACJQcqLgbFCmZVL5sDM9KYrl+t7zx3TeLEcdTkAAACzInTHwGihxCTKOfqFK5drtFDWj/cej7oUAACAWRG6Y2C0UKafe45et6FPrdmUvkVfNwAASABCdwyMFcqsXDJHrdm0XrehT9/ZPcjulAAAIPYI3THASPf8/Pymfr14YlT7jo1EXQoAAMCMCN0xMFosq42e7jl746Zgd8pv7x6MuBIAAICZEbpjYKxQUjurl8zZxcvataG/Q9/ZTV83AACIN4ZXY4D2ktrcu+PFlx1b1dOmHz5/XJ/7wX61ZM7/HfJdN65drNIAAABmxEh3DDCRcv4uX9GlcsX1/OCZqEsBAACYFqE7Bhjpnr91ve1qSaf0s6PDUZcCAAAwLUJ3xCoV1xgTKectk05pw/JO7T46zNKBAAAgtgjdERsLtzFnpHv+Ll/RqVOjRQ0M56MuBQAAYEqE7oiNFgjdF2rTii5JosUEAADEFqE7YmNh6G5jycB5W9LeouVdOe0mdAMAgJgidEdstFiSJLXT031BNq3s0gvHRpUP23UAAADipK6h28xuNbPdZrbHzO6e4vGcmX0pfHyHma2b8NgHw+O7zeyWCcc/a2YDZvZUPWtfLLSXLIxNK7pUdpYOBAAA8VS30G1maUmflHSbpM2S3mlmmyed9l5JJ939Mkkfl/TR8NrNkrZJukrSrZI+FT6fJH0uPNYQzraXELovyCW9HcplUrSYAACAWKrnSPcNkva4+153L0i6T9LWSedslfT58PZXJN1sZhYev8/d8+6+T9Ke8Pnk7t+VdKKOdS8qRroXRjplumx5p3529AxLBwIAgNipZ+heLenAhPsHw2NTnuPuJUmnJfXWeG1DGC1Ue7oJ3Rdq04ounR4r6ugQSwcCAIB4adiJlGZ2p5ntNLOdg4ODUZczrXPtJUykvFAbWToQAADEVD1D9yFJF0+4vyY8NuU5ZpaR1CPpeI3XzsjdP+3uW9x9S39//xxLXzxn20tYMvCC9bRltaqnlb5uAAAQO/UM3Y9I2mhm682sRcHEyO2Tztku6Y7w9u2SvuVBQ+52SdvC1U3WS9oo6eE61hqZ6o6UTKRcGJev6NILx0c0ztKBAAAgRuoWusMe7fdJekjSLkn3u/vTZvYRM3tbeNpnJPWa2R5JH5B0d3jt05Lul/SMpAcl3eXuZUkys7+W9CNJm8zsoJm9t16vYTGMFkpKp0y5TMN2+iyqy1d0qeLSngGWDgQAAPFR10Zid39A0gOTjn1owu1xSW+f5tp7JN0zxfF3LnCZkRotlNWeTStYtAUXau2ydrVmU3r2yFDUpQAAAJzF8GrExgplWksWUDplunJlt3YdHlaxXIm6HAAAAEmE7siNFsosF7jArrqoR2PFsn70/PGoSwEAAJBE6I7caKHMcoELbOOKTrWkU/qnp45EXQoAAIAkQnfkxoolRroXWDad0qaVXfrnp4+oXGF3SgAAED1Cd8RoL6mPq1f36PhIQQ/vOxF1KQAAAITuqI0VympjY5wFd/mKTuUyKT341OGoSwEAACB0R42R7vrIZdJ646Z+Pfj0EVVoMQEAABEjdEeMiZT1c9vVq3R0KK/HD5yMuhQAANDkCN0RGyswkbJe3nTlcmXTpgeeZBUTAAAQLUJ3hNxdo0XaS+qluzWrN25arr974iWV2CgHAABEiNAdoXypInexI2Ud3f6qNTp2Jq/vPXcs6lIAAEATI3RHaLRQliS1s3pJ3fz8puVa2p7VVx49GHUpAACgiRG6IzRaKEmS2plIWTctmZS2XrtaX3/mqE6PFqMuBwAANClCd4TGwpFu2kvq6/ZXrVGhXNH2n74UdSkAAKBJEbojdLa9hNBdV1dd1K0rVnbpq7SYAACAiBC6IzTKSPeiMDPd/qo1euLAKe0ZOBN1OQAAoAkRuiM0VqSne7FsvXa10iljQiUAAIgEoTtCtJcsnv6unN50xXLdv/OAxovlqMsBAABNhtAdobPtJSwZuCh+7aZ1OjFS0PYnmFAJAAAWF30NERpjpLuu7t3x4nn33V0ru1v1sa//TMVyRWZ29rF33bh2scsDAABNhJHuCJ1rL+F3n8VgZrrpsl4dGRrX3mMjUZcDAACaCKE7QmOFksyk1ixvw2J5xZol6mhJ6wd72BYeAAAsHtJehEYLZbVl0+e1OaC+sumUbry0V7uPDOvYmXzU5QAAgCZB6I7Q8ZGClra3RF1G07lx/TKlUqYfPs9oNwAAWByE7ggNDI9reXcu6jKaTldrVtddvEQ795/UqdFC1OUAAIAmQOiO0MBQXsu7CN1ReNMVyyVJ33x2IOJKAABAMyB0R2hgOK/lXa1Rl9GUlrS36DWX9uqxF07q6NB41OUAAIAGR+iOyHixrNNjRUa6I/SvLu9XSyalrz9zNOpSAABAgyN0R2RwOFg5g57u6HTkMnrDxn49c3hIj714MupyAABAAyN0R2SgGrppL4nUTZf1qjOX0X/5x12qVDzqcgAAQIMidEdkcDjoI+6nvSRSuUxat1y1QjtfOKkv7ngh6nIAAECDInRHZID2kti4fu1S/dzl/fqDf3pWB06MRl0OAABoQITuiAwM5ZUyqbeD0B01M9Mf/NtrlDLT7371p3KnzQQAACwsQndEBobH1deZUzrFFvBxsHpJmz74liv0w+eP696HX4y6HAAA0GAI3REZHM7TWhIz77phrV63oVf3/OMu7RkYjrocAADQQAjdEWFjnPgxM33sl69Vazat3/irxzRaKEVdEgAAaBCE7ogEoZuR7rhZ2dOqT2y7VnsGz+g/fO0p+rsBAMCCyERdQDMqV1zHzxC64+TeHef3cf/8puX6m8cPySW9et2yl53/rhvXLlJlAACgETDSHYHjZ/KquNTfTXtJXL3piuW6rL9Tf/+Tl/TSqbGoywEAAAlH6I7Aud0oGemOq5SZfvnVF6u9Ja2/fvhFjRfLUZcEAAASjNAdgYFwN0pCd7x15jLa9uq1Ojla0FcfO0h/NwAAmDdCdwQGhqq7UdJeEnfr+jr05s0r9fRLQ/rR3uNRlwMAABKK0B2BantJfycj3Unw+o19umJll/7pySP0dwMAgHkhdEdgYHhcS9uzasnwnz8JUma6/fo16sil9aWdB1QoVaIuCQAAJAypLwIDQ3n108+dKO25jH7pVWs0OJzXg08fibocAACQMITuCLAbZTJtXN6lmzb06sd7j+vbuweiLgcAACQIoTsCg+xGmVhvvmqllnfl9Dtf/qmODo1HXQ4AAEgIQvcic3cNDufV303oTqJsOqVtN6zVaKGkO/9yJ+t3AwCAmhC6F9mp0aIK5QrtJQm2srtVH3/HtfrJwdP63a/+lPW7AQDArAjdi4zdKBvDLVet1O/cskl/98RL+tR3no+6HAAAEHOZqAtoNuxG2Tj+zzdu0O4jw/qjh3br+JmCfve2Tcpl0lGXBQAAYojQvcjYjbJxmJn+6O2v0NL2rD77g3360d7j+u/vvFaXLe+KujQAABAztJcsMtpLGksuk9bvb71an7lji44Ojestf/J93fXFx/T1Z46yiQ4AADiLke5FNjA8ro6WtDpy/KdvJDdfuUIPvv8N+tR3ntff/+Ql/eOTh7WkPau3XrNK/+a61XrV2qVKpSzqMgEAQERIfovsyYOnta6vI+oycIHu3fHilMcvX9Gl3/qFy7VnYFhPHDil+3ce0Bd3vKg1S9v0K6+5RNtuWKuetuwiVwsAAKJG6F5EA0PjevTFk/qtmy+PuhTUUTpl2rSyW5tWditfLOuZw0M6cHJUf/BPz+oT33xOv7zlYr37NZfosuWdUZcKAAAWCaF7ET30zFG5S7ddszLqUrBIctm0rlu7VH/09lfq6ZdO6zPf36cv7nhBn/vhft2wfpneecPFetOmFeppZ/QbAIBGRuheRA8+dViX9nVoIyOcTafajrLlkmXatKJLj714So/sP6Hf/tJPZJJW9rTqkt4O9Xa06M1XrVB/Z05tLWll0ym1ZFLqas1oaXuLWrMsSQgAQBIRuhfJyZGCfrz3hH795y6VGRPqmllXa1b/6vJ+vWFjn148Pqq9x85o/7FRPfrCCRXLrn988vC017Zl01q7rF1XrurSFau69co1S3Td2iWEcQAAYo7QvUi+/sxRlSuu265eFXUpiImUmdb1dZydWFtx13ihrOF8SSP5korlisoVV6niypcqGs2XdCZf0rEzBX1796D+9omXJEktmZRetXapXnNpr167oVfXXrxELRlWAwUAIE4I3YvkwaePaM3SNl29ujvqUhBTKTO15zJqr3E5ydF8SS+cGFVLJqUfPX9cf/zNn+nj35BasyltuWSZXnPpMr12Q69esWaJsmlCOAAAUSJ0L4Lh8aK+/9wxvee1l9BaggXTnsvoylXBL3Eb+js1Wihp/7ERPX9sRHsGzuj7e45JktJmWtbZov7OnN6wsU8rulu1sqdVq3padWl/J0sYAgCwCOoaus3sVkmfkJSW9Ofu/oeTHs9J+ktJr5J0XNI73H1/+NgHJb1XUlnS/+XuD9XynHH0rWcHVChXWLUEddXektHmi3q0+aIeSdJIvqR9x0Z08OSYjp3Ja3A4r7/4wX4VyufvlNnfldPlKzp17cVLdP3apbpu7VIt62iJ4iUAANCw6ha6zSwt6ZOSflHSQUmPmNl2d39mwmnvlXTS3S8zs22SPirpHWa2WdI2SVdJukjSN8ysurj1bM8ZKz/cc0x/9NBurejO6bqLl0ZdDppIRy6jq1f36OrVPWePubtGC2UNjRd1arSoweEgjO8/NqofPX9cFQ/O627NaGVPq964abn6Olu0tD3405HLqCOXVntLWu0tGXW0ZNSeS9O+AgDALOo50n2DpD3uvleSzOw+SVslTQzIWyV9OLz9FUn/w4L+i62S7nP3vKR9ZrYnfD7V8JyxcGq0oHv+cZe+/OhBXdLbrj9+x/VsA47ImVkYnDNa1dOmKyfM6y2UKjp0akwHT47qyOlxHT49rr/4wT4Vyz7r83a0pLW0IwznHS1a2p7V0vYWtbek1ZpNqzWbUi7z8q+5Sfdb0imZSamUKWVBn7uFX4M/wWtITThWfbzirqHxoobGShoeL2p4vKSh8OtweHykUAr+O6j6XNXvYcplUmdrrX5tywb1t2WDXzLaWtJqa0mrPZtWxYMJrsGfsvLF4HaxXFGhVFGhXFHaTG3hf4P2luB5qvdzmZRymVQiWs7cXcWyq1SpqFh2lSsud1cmlVI6bcqkTOmUKW0Wu59z5YoH70m5omIp+FeeTCqlTDqoOZtOnf1cAYhO9edMsRz8HC1VXGkzZTMpZcL/V9Mx+/kyV/UM3aslHZhw/6CkG6c7x91LZnZaUm94/MeTrl0d3p7tOSPn7nrPZx/W0y8N6TfeuEHvv3kjS7oh9loyKa3v69D6cDUVKfgs50sVjRbKGi2UgjBZqihfDZZh4BwrlDUSnrN38Mx551dmz+yLwhS8RjPJXXIFr0+SKh6Esyi0hOG7NZtWNmUzhr+JD00+zWTTnhu8Xg++evC6ffJxnXus7K5S+JdfqeJz+m9jprMhPJNKhV/DUD7hz+SagjqDG9PVpgn3q+dPrDs8RS6d9wtCLTKp4BeGTPjLQzodfp1Qc/WXPGAmPuEj5/Ipj091/9zxCdfM97mn/T7nn3j+801dw0zPN9N5mua5J19XcZ39WTMbMykb/tJcDeITB2kmDsS89/Xrdcfr1s36nIupYSdSmtmdku4M754xs91R1HH3H0h3z/2yPknHFrwYNCM+S1hIfJ6wkPg8YaG87LP0PUm/GkkpumS6B+oZug9JunjC/TXhsanOOWhmGUk9CiZUznTtbM8pSXL3T0v69HyLj5KZ7XT3LVHXgeTjs4SFxOcJC4nPExZKUj5L9Zz99IikjWa23sxaFEyM3D7pnO2S7ghv3y7pWx78m8N2SdvMLGdm6yVtlPRwjc8JAAAAxErdRrrDHu33SXpIwfJ+n3X3p83sI5J2uvt2SZ+R9IVwouQJBSFa4Xn3K5ggWZJ0l7uXJWmq56zXawAAAAAWgk1ugkf0zOzOsD0GuCB8lrCQ+DxhIfF5wkJJymeJ0A0AAADUGTtaAAAAAHVG6I4RM7vVzHab2R4zm8dKg2hGZrbfzJ40syfMbGd4bJmZfd3Mngu/Lg2Pm5n9SfgZ+6mZXR9t9YiamX3WzAbM7KkJx+b8+TGzO8LznzOzO6b6Xmhs03yWPmxmh8KfT0+Y2VsmPPbB8LO028xumXCcvwshM7vYzL5tZs+Y2dNm9v7weGJ/PhG6Y8LM0gq2uL9N0mZJ7zSzzdFWhQT5eXe/dsKSSXdL+qa7b5T0TZ1bLv42BasBbVSwjv2fLnqliJvPSbp10rE5fX7MbJmk/6hgs7IbJP3H6l+EaCqf08s/S5L08fDn07Xu/oAkhX+/bZN0VXjNp8wszd+FmKAk6d+7+2ZJr5F0V/hZSOzPJ0J3fNygcIt7dy9Iqm5xD8zHVkmfD29/XtK/mXD8Lz3wY0lLzGzVFNejSbj7dxWsHjXRXD8/t0j6urufcPeTkr6uqcMXGtg0n6XpbJV0n7vn3X2fpD0K/h7k70JIktz9sLs/Ft4elrRLwe7kif35ROiOj9V6+Rb3qyOqBcnikv7ZzB4Nd2KVpBXufji8fUTSivA2nzPUYq6fHz5XmMn7wn/u/+yEEUY+S6iZma2TdJ2kHUrwzydCN5B8r3f36xX809pdZvZzEx8MN5ximSLMC58fXKA/lbRB0rWSDkv6fyOtBoljZp2Svirpt9x9aOJjSfv5ROiOj0OqcYt7YCJ3PxR+HZD0NQX/PHu02jYSfh0IT+dzhlrM9fPD5wpTcvej7l5294qkP1Pw80nis4QamFlWQeD+orv/TXg4sT+fCN3xwRb3mDMz6zCzruptSW+W9JSCz051hvYdkv4uvL1d0nvCWd6vkXR6wj/TAVVz/fw8JOnNZrY0bB94c3gMTW7SnJH/VcHPJyn4LG0zs5yZrVcw+e1h8XchQmZmCnYu3+XuH5vwUGJ/PtVtG3jMjbuXjC3uMXcrJH0t+NmkjKR73f1BM3tE0v1m9l5JL0j65fD8ByS9RcGkpVFJv7b4JSNOzOyvJb1RUp+ZHVQwy/8PNYfPj7ufMLP/pCAwSdJH3L3WCXVoENN8lt5oZtcqaAHYL+nXJcndnzaz+yU9o2CVirvcvRw+D38XQpJukvQrkp40syfCY7+nBP98YkdKAAAAoM5oLwEAAADqjNANAAAA1BmhGwAAAKgzQjcAAABQZ4RuAAAAoM4I3QAAAECdEboBAIvGzM5EXQMARIHQDQCoCzNjAzYACBG6ASAhzGydme0ysz8zs6fN7J/NrM3MvmNmW8Jz+sxsf3j7V83sb83s62a238zeZ2YfMLPHzezHZrZsmu+z3MweDW+/0szczNaG9583s/awlm+Z2U/N7JsTHv+cmf1PM9sh6b+G23n/yMyeNLP/POF7rDKz75rZE2b2lJm9ob7/9QAgWoRuAEiWjZI+6e5XSTol6ZdmOf9qSf9W0qsl3SNp1N2vk/QjSe+Z6gJ3H5DUambdkt4gaaekN5jZJZIG3H1U0n+X9Hl3f4WkL0r6kwlPsUbS69z9A5I+IelP3f0aSYcnnPMuSQ+5+7WSXinpiZpePQAkFKEbAJJln7s/Ed5+VNK6Wc7/trsPu/ugpNOS/j48/uQs1/5Q0k2Sfk7Sfwm/vkHS98LHXyvp3vD2FyS9fsK1X3b3cnj7Jkl/PeG8qkck/ZqZfVjSNe4+PMvrAIBEI3QDQLLkJ9wuS8pIKuncz/PWGc6vTLhfCa+dzncVhOxLJP2dgtHo1+tc6J7JyKT7PvkEd/+ugiB/SNLnzGzKUXcAaBSEbgBIvv2SXhXevn2BnvN7kt4t6Tl3r0g6Iektkr4fPv5DSdvC2/9O04fxH0w6T5IUtqocdfc/k/Tnkq5foLoBIJYI3QCQfP9N0m+Y2eOS+hbiCd19vyRTMOItBWH7lLufDO//poL2kJ9K+hVJ75/mqd4v6S4ze1LS6gnH3yjpJ2HN71DQ+w0ADcvcX/avfgAAAAAWECPdAAAAQJ2xcQEANDEz+6SCFUYm+oS7/0UU9QBAo6K9BAAAAKgz2ksAAACAOiN0AwAAAHVG6AYAAADqjNANAAAA1BmhGwAAAKiz/x8fPn+CjlIUsQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"df.num_words.describe()","metadata":{"papermill":{"duration":0.029556,"end_time":"2022-12-20T21:37:56.746317","exception":false,"start_time":"2022-12-20T21:37:56.716761","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:02.332563Z","iopub.execute_input":"2022-12-21T09:34:02.337007Z","iopub.status.idle":"2022-12-21T09:34:02.361739Z","shell.execute_reply.started":"2022-12-21T09:34:02.336954Z","shell.execute_reply":"2022-12-21T09:34:02.358185Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"count    7249.000000\nmean      123.570010\nstd        98.117023\nmin         3.000000\n25%        48.000000\n50%       106.000000\n75%       173.000000\nmax      1965.000000\nName: num_words, dtype: float64"},"metadata":{}}]},{"cell_type":"code","source":"max_len = 125","metadata":{"papermill":{"duration":0.021967,"end_time":"2022-12-20T21:37:56.783255","exception":false,"start_time":"2022-12-20T21:37:56.761288","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:02.364991Z","iopub.execute_input":"2022-12-21T09:34:02.365937Z","iopub.status.idle":"2022-12-21T09:34:02.376318Z","shell.execute_reply.started":"2022-12-21T09:34:02.365857Z","shell.execute_reply":"2022-12-21T09:34:02.374958Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"## Text Preprocessing","metadata":{"papermill":{"duration":0.014402,"end_time":"2022-12-20T21:37:56.811494","exception":false,"start_time":"2022-12-20T21:37:56.797092","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def text_preprocess(data,col):\n    data[col] = data[col].apply(func=clean_html)\n    data[col] = data[col].apply(func=remove_)\n    data[col] = data[col].apply(func=removeStopWords)\n    data[col] = data[col].apply(func=remove_digits)\n    data[col] = data[col].apply(func=remove_links)\n    data[col] = data[col].apply(func=remove_special_characters)\n    data[col] = data[col].apply(func=punct)\n    data[col] = data[col].apply(func=non_ascii)\n    data[col] = data[col].apply(func=email_address)\n    data[col] = data[col].apply(func=lower)\n    return data","metadata":{"papermill":{"duration":0.025587,"end_time":"2022-12-20T21:37:56.851909","exception":false,"start_time":"2022-12-20T21:37:56.826322","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:02.378583Z","iopub.execute_input":"2022-12-21T09:34:02.379331Z","iopub.status.idle":"2022-12-21T09:34:02.390604Z","shell.execute_reply.started":"2022-12-21T09:34:02.379279Z","shell.execute_reply":"2022-12-21T09:34:02.388766Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"preprocessed_df = text_preprocess(df,'Text')\npreprocessed_df.head()","metadata":{"papermill":{"duration":1.459044,"end_time":"2022-12-20T21:37:58.325301","exception":false,"start_time":"2022-12-20T21:37:56.866257","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:02.392982Z","iopub.execute_input":"2022-12-21T09:34:02.393418Z","iopub.status.idle":"2022-12-21T09:34:03.782966Z","shell.execute_reply.started":"2022-12-21T09:34:02.393374Z","shell.execute_reply":"2022-12-21T09:34:03.782040Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"                                                   Text    Language  num_words\n7642                                   va bene iniziamo     Italian         17\n3104                        desculpe eu no entendi isso  Portuguese         30\n7898  yaymlanm vikipedi maddeleri ayrca pediapress p...     Turkish        107\n783   replaced question can machines as thinking ent...     English         87\n9642  ich habe nicht zugehrt weil du vielleicht besc...      German         62","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>Text</th>\n      <th>Language</th>\n      <th>num_words</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7642</th>\n      <td>va bene iniziamo</td>\n      <td>Italian</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>3104</th>\n      <td>desculpe eu no entendi isso</td>\n      <td>Portuguese</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>7898</th>\n      <td>yaymlanm vikipedi maddeleri ayrca pediapress p...</td>\n      <td>Turkish</td>\n      <td>107</td>\n    </tr>\n    <tr>\n      <th>783</th>\n      <td>replaced question can machines as thinking ent...</td>\n      <td>English</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>9642</th>\n      <td>ich habe nicht zugehrt weil du vielleicht besc...</td>\n      <td>German</td>\n      <td>62</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Encoding the target label classes","metadata":{"papermill":{"duration":0.013818,"end_time":"2022-12-20T21:37:58.353393","exception":false,"start_time":"2022-12-20T21:37:58.339575","status":"completed"},"tags":[]}},{"cell_type":"code","source":"labels_dict = {}\nfor idx, lang in enumerate(preprocessed_df.Language.unique()):\n    labels_dict[lang] = idx\nlabels_dict","metadata":{"papermill":{"duration":0.026325,"end_time":"2022-12-20T21:37:58.394152","exception":false,"start_time":"2022-12-20T21:37:58.367827","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:03.784474Z","iopub.execute_input":"2022-12-21T09:34:03.784846Z","iopub.status.idle":"2022-12-21T09:34:03.793787Z","shell.execute_reply.started":"2022-12-21T09:34:03.784809Z","shell.execute_reply":"2022-12-21T09:34:03.792659Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"{'Italian': 0,\n 'Portuguese': 1,\n 'Turkish': 2,\n 'English': 3,\n 'German': 4,\n 'Swedish': 5,\n 'Spanish': 6,\n 'Dutch': 7,\n 'French': 8,\n 'Danish': 9}"},"metadata":{}}]},{"cell_type":"code","source":"preprocessed_df['Label'] = preprocessed_df.Language.map(labels_dict)\npreprocessed_df.head()","metadata":{"papermill":{"duration":0.029217,"end_time":"2022-12-20T21:37:58.437374","exception":false,"start_time":"2022-12-20T21:37:58.408157","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:03.796820Z","iopub.execute_input":"2022-12-21T09:34:03.797325Z","iopub.status.idle":"2022-12-21T09:34:03.813723Z","shell.execute_reply.started":"2022-12-21T09:34:03.797296Z","shell.execute_reply":"2022-12-21T09:34:03.812784Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"                                                   Text    Language  \\\n7642                                   va bene iniziamo     Italian   \n3104                        desculpe eu no entendi isso  Portuguese   \n7898  yaymlanm vikipedi maddeleri ayrca pediapress p...     Turkish   \n783   replaced question can machines as thinking ent...     English   \n9642  ich habe nicht zugehrt weil du vielleicht besc...      German   \n\n      num_words  Label  \n7642         17      0  \n3104         30      1  \n7898        107      2  \n783          87      3  \n9642         62      4  ","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>Text</th>\n      <th>Language</th>\n      <th>num_words</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7642</th>\n      <td>va bene iniziamo</td>\n      <td>Italian</td>\n      <td>17</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3104</th>\n      <td>desculpe eu no entendi isso</td>\n      <td>Portuguese</td>\n      <td>30</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7898</th>\n      <td>yaymlanm vikipedi maddeleri ayrca pediapress p...</td>\n      <td>Turkish</td>\n      <td>107</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>783</th>\n      <td>replaced question can machines as thinking ent...</td>\n      <td>English</td>\n      <td>87</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>9642</th>\n      <td>ich habe nicht zugehrt weil du vielleicht besc...</td>\n      <td>German</td>\n      <td>62</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"preprocessed_df.sample(frac=1)","metadata":{"papermill":{"duration":0.029329,"end_time":"2022-12-20T21:37:58.480685","exception":false,"start_time":"2022-12-20T21:37:58.451356","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:03.815129Z","iopub.execute_input":"2022-12-21T09:34:03.815678Z","iopub.status.idle":"2022-12-21T09:34:03.833962Z","shell.execute_reply.started":"2022-12-21T09:34:03.815636Z","shell.execute_reply":"2022-12-21T09:34:03.833079Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"                                                   Text    Language  \\\n4669                            je hebt helemaal gelijk       Dutch   \n3417  globalement peut apprhender cette question en ...      French   \n3020  voc est meio de algo e algum est lhe pedindo p...  Portuguese   \n919   for example rule n n p e b u r g e r displayst...     English   \n343   obvious vandalism generally easy remove wikipe...     English   \n...                                                 ...         ...   \n9581  unten im beschreibungsfeld dafr und jetzt ist ...      German   \n756   the latter started sanger attempt create relia...     English   \n8840                   jag r rdd att jag inte hller med     Swedish   \n7433  nell ambito dell apprendimento automatico la t...     Italian   \n7622                                        mi dispiace     Italian   \n\n      num_words  Label  \n4669         24      7  \n3417        129      8  \n3020         91      1  \n919         324      3  \n343         137      3  \n...         ...    ...  \n9581        187      4  \n756          93      3  \n8840         36      5  \n7433        176      0  \n7622         12      0  \n\n[7249 rows x 4 columns]","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>Text</th>\n      <th>Language</th>\n      <th>num_words</th>\n      <th>Label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4669</th>\n      <td>je hebt helemaal gelijk</td>\n      <td>Dutch</td>\n      <td>24</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>3417</th>\n      <td>globalement peut apprhender cette question en ...</td>\n      <td>French</td>\n      <td>129</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>3020</th>\n      <td>voc est meio de algo e algum est lhe pedindo p...</td>\n      <td>Portuguese</td>\n      <td>91</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>919</th>\n      <td>for example rule n n p e b u r g e r displayst...</td>\n      <td>English</td>\n      <td>324</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>343</th>\n      <td>obvious vandalism generally easy remove wikipe...</td>\n      <td>English</td>\n      <td>137</td>\n      <td>3</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>9581</th>\n      <td>unten im beschreibungsfeld dafr und jetzt ist ...</td>\n      <td>German</td>\n      <td>187</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>756</th>\n      <td>the latter started sanger attempt create relia...</td>\n      <td>English</td>\n      <td>93</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8840</th>\n      <td>jag r rdd att jag inte hller med</td>\n      <td>Swedish</td>\n      <td>36</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>7433</th>\n      <td>nell ambito dell apprendimento automatico la t...</td>\n      <td>Italian</td>\n      <td>176</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7622</th>\n      <td>mi dispiace</td>\n      <td>Italian</td>\n      <td>12</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>7249 rows × 4 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Splitting the preprocessed dataset into train and test sets","metadata":{"papermill":{"duration":0.013876,"end_time":"2022-12-20T21:37:58.508653","exception":false,"start_time":"2022-12-20T21:37:58.494777","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train_df, test_df = train_test_split(preprocessed_df,test_size=0.3,random_state=42,shuffle=True,stratify=preprocessed_df.Language)","metadata":{"papermill":{"duration":0.028703,"end_time":"2022-12-20T21:37:58.551551","exception":false,"start_time":"2022-12-20T21:37:58.522848","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:03.835260Z","iopub.execute_input":"2022-12-21T09:34:03.835692Z","iopub.status.idle":"2022-12-21T09:34:03.849743Z","shell.execute_reply.started":"2022-12-21T09:34:03.835656Z","shell.execute_reply":"2022-12-21T09:34:03.848514Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"## Downloading the pretrained tokenizer and xlm-roberta-base language detection model","metadata":{"papermill":{"duration":0.014024,"end_time":"2022-12-20T21:37:58.580511","exception":false,"start_time":"2022-12-20T21:37:58.566487","status":"completed"},"tags":[]}},{"cell_type":"code","source":"tokenizer = AutoTokenizer.from_pretrained(\"papluca/xlm-roberta-base-language-detection\")\nxlm = TFAutoModelForSequenceClassification.from_pretrained(\"papluca/xlm-roberta-base-language-detection\",from_pt=True)","metadata":{"papermill":{"duration":62.211684,"end_time":"2022-12-20T21:39:00.806995","exception":false,"start_time":"2022-12-20T21:37:58.595311","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:03.851243Z","iopub.execute_input":"2022-12-21T09:34:03.851764Z","iopub.status.idle":"2022-12-21T09:34:41.893290Z","shell.execute_reply.started":"2022-12-21T09:34:03.851724Z","shell.execute_reply":"2022-12-21T09:34:41.892374Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/502 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2465590ab8ce417e8c9121c6256db55d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/4.83M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bed4a49876974f0a94ff04362a7f1f0a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/8.66M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c99496836db4e778705c3c0d7e8c7a1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/239 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e7be401a16184e3c98823af64bf90840"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/1.38k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f56cdcf01671482a8d2036444298d140"}},"metadata":{}},{"name":"stderr","text":"2022-12-21 09:34:09.657171: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.658144: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.659159: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.659950: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.660718: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.661435: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.664745: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2022-12-21 09:34:09.925378: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.926281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.927058: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.927789: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.928556: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:09.929336: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.977500: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.978508: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.979327: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.980065: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.980858: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.981600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 13351 MB memory:  -> device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5\n2022-12-21 09:34:16.985262: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n2022-12-21 09:34:16.986058: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 13351 MB memory:  -> device: 1, name: Tesla T4, pci bus id: 0000:00:05.0, compute capability: 7.5\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Downloading:   0%|          | 0.00/1.04G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97ec359f8f154b46b78aa3d98ff4f010"}},"metadata":{}},{"name":"stderr","text":"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFXLMRobertaForSequenceClassification: ['roberta.embeddings.position_ids']\n- This IS expected if you are initializing TFXLMRobertaForSequenceClassification from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing TFXLMRobertaForSequenceClassification from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\nAll the weights of TFXLMRobertaForSequenceClassification were initialized from the PyTorch model.\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use TFXLMRobertaForSequenceClassification for predictions without further training.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Performing text tokenization","metadata":{"papermill":{"duration":0.017114,"end_time":"2022-12-20T21:39:00.841049","exception":false,"start_time":"2022-12-20T21:39:00.823935","status":"completed"},"tags":[]}},{"cell_type":"code","source":"X_train = tokenizer(text=train_df.Text.tolist(),\n                   max_length=max_len,\n                   padding=True,\n                   truncation=True,\n                   add_special_tokens=True,\n                   return_tensors=\"tf\",\n                   return_attention_mask=True,\n                   return_token_type_ids=False,\n                   verbose=True)\n\nX_test = tokenizer(text=test_df.Text.tolist(),\n                  max_length=max_len,\n                  padding=True,\n                  truncation=True,\n                  add_special_tokens=True,\n                  return_tensors=\"tf\",\n                  return_attention_mask=True,\n                  return_token_type_ids=False,\n                  verbose=True)","metadata":{"papermill":{"duration":0.747367,"end_time":"2022-12-20T21:39:01.605286","exception":false,"start_time":"2022-12-20T21:39:00.857919","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:41.894874Z","iopub.execute_input":"2022-12-21T09:34:41.895833Z","iopub.status.idle":"2022-12-21T09:34:42.690564Z","shell.execute_reply.started":"2022-12-21T09:34:41.895790Z","shell.execute_reply":"2022-12-21T09:34:42.689482Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"## Fine-tuning the pretrained xlm-roberta-base language detection model","metadata":{"papermill":{"duration":0.016425,"end_time":"2022-12-20T21:39:01.638765","exception":false,"start_time":"2022-12-20T21:39:01.622340","status":"completed"},"tags":[]}},{"cell_type":"code","source":"input_ids = Input(shape=(max_len,),dtype=tf.int32,name='input_ids')\nattention_mask = Input(shape=(max_len,),dtype=tf.int32,name='attention_mask')","metadata":{"papermill":{"duration":0.028993,"end_time":"2022-12-20T21:39:01.683007","exception":false,"start_time":"2022-12-20T21:39:01.654014","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:42.693023Z","iopub.execute_input":"2022-12-21T09:34:42.694019Z","iopub.status.idle":"2022-12-21T09:34:42.703734Z","shell.execute_reply.started":"2022-12-21T09:34:42.693977Z","shell.execute_reply":"2022-12-21T09:34:42.702550Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"embeddings = xlm(input_ids,attention_mask=attention_mask)[0] # 0 --> final hidden state, 1 --> pooling output\noutput = Flatten()(embeddings)\noutput = Dense(units=1024,activation='relu')(output)\noutput = Dropout(0.3)(output)\noutput = Dense(units=512,activation='relu')(output)\noutput = Dropout(0.2)(output)\noutput = Dense(units=512,activation='relu')(output)\noutput = Dropout(0.2)(output)\noutput = Dense(units=128,activation='relu')(output)\noutput = Dense(units=10,activation='softmax')(output)\n\nmodel = Model(inputs=[input_ids,attention_mask],outputs=output)\nmodel.layers[2].trainable = True","metadata":{"papermill":{"duration":8.781098,"end_time":"2022-12-20T21:39:10.479195","exception":false,"start_time":"2022-12-20T21:39:01.698097","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:42.707041Z","iopub.execute_input":"2022-12-21T09:34:42.707959Z","iopub.status.idle":"2022-12-21T09:34:51.197557Z","shell.execute_reply.started":"2022-12-21T09:34:42.707918Z","shell.execute_reply":"2022-12-21T09:34:51.196566Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"## Model Architecture","metadata":{"papermill":{"duration":0.014975,"end_time":"2022-12-20T21:39:10.510051","exception":false,"start_time":"2022-12-20T21:39:10.495076","status":"completed"},"tags":[]}},{"cell_type":"code","source":"model.summary()","metadata":{"papermill":{"duration":0.038862,"end_time":"2022-12-20T21:39:10.564069","exception":false,"start_time":"2022-12-20T21:39:10.525207","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:51.199166Z","iopub.execute_input":"2022-12-21T09:34:51.199831Z","iopub.status.idle":"2022-12-21T09:34:51.218783Z","shell.execute_reply.started":"2022-12-21T09:34:51.199793Z","shell.execute_reply":"2022-12-21T09:34:51.217511Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_ids (InputLayer)          [(None, 125)]        0                                            \n__________________________________________________________________________________________________\nattention_mask (InputLayer)     [(None, 125)]        0                                            \n__________________________________________________________________________________________________\ntfxlm_roberta_for_sequence_clas TFSequenceClassifier 278059028   input_ids[0][0]                  \n                                                                 attention_mask[0][0]             \n__________________________________________________________________________________________________\nflatten (Flatten)               (None, 20)           0           tfxlm_roberta_for_sequence_classi\n__________________________________________________________________________________________________\ndense (Dense)                   (None, 1024)         21504       flatten[0][0]                    \n__________________________________________________________________________________________________\ndropout_38 (Dropout)            (None, 1024)         0           dense[0][0]                      \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 512)          524800      dropout_38[0][0]                 \n__________________________________________________________________________________________________\ndropout_39 (Dropout)            (None, 512)          0           dense_1[0][0]                    \n__________________________________________________________________________________________________\ndense_2 (Dense)                 (None, 512)          262656      dropout_39[0][0]                 \n__________________________________________________________________________________________________\ndropout_40 (Dropout)            (None, 512)          0           dense_2[0][0]                    \n__________________________________________________________________________________________________\ndense_3 (Dense)                 (None, 128)          65664       dropout_40[0][0]                 \n__________________________________________________________________________________________________\ndense_4 (Dense)                 (None, 10)           1290        dense_3[0][0]                    \n==================================================================================================\nTotal params: 278,934,942\nTrainable params: 278,934,942\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"plot_model(model,'model.png',show_shapes=True,dpi=100,rankdir='TB')","metadata":{"papermill":{"duration":2.366584,"end_time":"2022-12-20T21:39:12.947486","exception":false,"start_time":"2022-12-20T21:39:10.580902","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:51.220261Z","iopub.execute_input":"2022-12-21T09:34:51.220772Z","iopub.status.idle":"2022-12-21T09:34:52.492432Z","shell.execute_reply.started":"2022-12-21T09:34:51.220729Z","shell.execute_reply":"2022-12-21T09:34:52.491123Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\nhuggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\nTo disable this warning, you can either:\n\t- Avoid using `tokenizers` before the fork if possible\n\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n","output_type":"stream"},{"execution_count":27,"output_type":"execute_result","data":{"image/png":"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object>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Compiling the model","metadata":{"papermill":{"duration":0.017515,"end_time":"2022-12-20T21:39:12.983202","exception":false,"start_time":"2022-12-20T21:39:12.965687","status":"completed"},"tags":[]}},{"cell_type":"code","source":"optimizer = Adam(learning_rate=5e-5,epsilon=2e-8,decay=0.01,clipnorm=1.0)\nloss = CategoricalCrossentropy()\nmetrics = CategoricalAccuracy('balanced_accuracy')\nmodel.compile(loss=loss,optimizer=optimizer,metrics=metrics)","metadata":{"papermill":{"duration":0.048559,"end_time":"2022-12-20T21:39:13.049994","exception":false,"start_time":"2022-12-20T21:39:13.001435","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:52.494237Z","iopub.execute_input":"2022-12-21T09:34:52.495059Z","iopub.status.idle":"2022-12-21T09:34:52.523272Z","shell.execute_reply.started":"2022-12-21T09:34:52.495012Z","shell.execute_reply":"2022-12-21T09:34:52.522386Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"markdown","source":"## Training the fine-tuned XLM-Roberta-Base transformer model","metadata":{"papermill":{"duration":0.01706,"end_time":"2022-12-20T21:39:13.084741","exception":false,"start_time":"2022-12-20T21:39:13.067681","status":"completed"},"tags":[]}},{"cell_type":"code","source":"es = EarlyStopping(monitor='val_balanced_accuracy',patience=20,verbose=1,mode='max',restore_best_weights=True)\nmc = ModelCheckpoint(filepath='checkpoint',monitor='val_balanced_accuracy',mode='max',save_best_only=True,verbose=1)\n\nr = model.fit(x={'input_ids': X_train['input_ids'], 'attention_mask': X_train['attention_mask']},\n              y=to_categorical(train_df.Label),\n              epochs=50,\n              batch_size=64,\n              callbacks=[es,mc],\n              validation_data=({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']},to_categorical(test_df.Label))\n             )","metadata":{"papermill":{"duration":9070.276096,"end_time":"2022-12-21T00:10:23.377936","exception":false,"start_time":"2022-12-20T21:39:13.101840","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T09:34:52.528792Z","iopub.execute_input":"2022-12-21T09:34:52.529070Z","iopub.status.idle":"2022-12-21T10:43:11.811108Z","shell.execute_reply.started":"2022-12-21T09:34:52.529043Z","shell.execute_reply":"2022-12-21T10:43:11.810111Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stderr","text":"2022-12-21 09:34:52.611420: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/50\n80/80 [==============================] - 163s 2s/step - loss: 1.3802 - balanced_accuracy: 0.6730 - val_loss: 0.5346 - val_balanced_accuracy: 0.8759\n","output_type":"stream"},{"name":"stderr","text":"2022-12-21 09:37:36.178344: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 768006144 exceeds 10% of free system memory.\n2022-12-21 09:37:37.490590: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 768006144 exceeds 10% of free system memory.\n","output_type":"stream"},{"name":"stdout","text":"\nEpoch 00001: val_balanced_accuracy improved from -inf to 0.87586, saving model to checkpoint\n","output_type":"stream"},{"name":"stderr","text":"2022-12-21 09:37:52.311671: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n2022-12-21 09:38:28.250443: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 768006144 exceeds 10% of free system memory.\n2022-12-21 09:38:29.315618: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 768006144 exceeds 10% of free system memory.\n2022-12-21 09:38:30.410041: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 768006144 exceeds 10% of free system memory.\n","output_type":"stream"},{"name":"stdout","text":"Epoch 2/50\n80/80 [==============================] - 143s 2s/step - loss: 0.4218 - balanced_accuracy: 0.9013 - val_loss: 0.1863 - val_balanced_accuracy: 0.9752\n\nEpoch 00002: val_balanced_accuracy improved from 0.87586 to 0.97517, saving model to checkpoint\nEpoch 3/50\n80/80 [==============================] - 143s 2s/step - loss: 0.1536 - balanced_accuracy: 0.9765 - val_loss: 0.1177 - val_balanced_accuracy: 0.9770\n\nEpoch 00003: val_balanced_accuracy improved from 0.97517 to 0.97701, saving model to checkpoint\nEpoch 4/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0825 - balanced_accuracy: 0.9858 - val_loss: 0.1207 - val_balanced_accuracy: 0.9761\n\nEpoch 00004: val_balanced_accuracy did not improve from 0.97701\nEpoch 5/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0728 - balanced_accuracy: 0.9858 - val_loss: 0.1210 - val_balanced_accuracy: 0.9775\n\nEpoch 00005: val_balanced_accuracy improved from 0.97701 to 0.97747, saving model to checkpoint\nEpoch 6/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0532 - balanced_accuracy: 0.9901 - val_loss: 0.1206 - val_balanced_accuracy: 0.9789\n\nEpoch 00006: val_balanced_accuracy improved from 0.97747 to 0.97885, saving model to checkpoint\nEpoch 7/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0507 - balanced_accuracy: 0.9898 - val_loss: 0.1317 - val_balanced_accuracy: 0.9766\n\nEpoch 00007: val_balanced_accuracy did not improve from 0.97885\nEpoch 8/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0368 - balanced_accuracy: 0.9929 - val_loss: 0.1314 - val_balanced_accuracy: 0.9766\n\nEpoch 00008: val_balanced_accuracy did not improve from 0.97885\nEpoch 9/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0405 - balanced_accuracy: 0.9909 - val_loss: 0.1410 - val_balanced_accuracy: 0.9761\n\nEpoch 00009: val_balanced_accuracy did not improve from 0.97885\nEpoch 10/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0298 - balanced_accuracy: 0.9939 - val_loss: 0.1297 - val_balanced_accuracy: 0.9775\n\nEpoch 00010: val_balanced_accuracy did not improve from 0.97885\nEpoch 11/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0248 - balanced_accuracy: 0.9953 - val_loss: 0.1477 - val_balanced_accuracy: 0.9756\n\nEpoch 00011: val_balanced_accuracy did not improve from 0.97885\nEpoch 12/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0335 - balanced_accuracy: 0.9915 - val_loss: 0.1487 - val_balanced_accuracy: 0.9784\n\nEpoch 00012: val_balanced_accuracy did not improve from 0.97885\nEpoch 13/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0229 - balanced_accuracy: 0.9953 - val_loss: 0.1378 - val_balanced_accuracy: 0.9770\n\nEpoch 00013: val_balanced_accuracy did not improve from 0.97885\nEpoch 14/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0295 - balanced_accuracy: 0.9935 - val_loss: 0.1468 - val_balanced_accuracy: 0.9761\n\nEpoch 00014: val_balanced_accuracy did not improve from 0.97885\nEpoch 15/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0222 - balanced_accuracy: 0.9945 - val_loss: 0.1497 - val_balanced_accuracy: 0.9761\n\nEpoch 00015: val_balanced_accuracy did not improve from 0.97885\nEpoch 16/50\n80/80 [==============================] - 142s 2s/step - loss: 0.0192 - balanced_accuracy: 0.9961 - val_loss: 0.1494 - val_balanced_accuracy: 0.9761\n\nEpoch 00016: val_balanced_accuracy did not improve from 0.97885\nEpoch 17/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0217 - balanced_accuracy: 0.9949 - val_loss: 0.1457 - val_balanced_accuracy: 0.9775\n\nEpoch 00017: val_balanced_accuracy did not improve from 0.97885\nEpoch 18/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0205 - balanced_accuracy: 0.9955 - val_loss: 0.1465 - val_balanced_accuracy: 0.9766\n\nEpoch 00018: val_balanced_accuracy did not improve from 0.97885\nEpoch 19/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0184 - balanced_accuracy: 0.9961 - val_loss: 0.1523 - val_balanced_accuracy: 0.9766\n\nEpoch 00019: val_balanced_accuracy did not improve from 0.97885\nEpoch 20/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0148 - balanced_accuracy: 0.9966 - val_loss: 0.1561 - val_balanced_accuracy: 0.9766\n\nEpoch 00020: val_balanced_accuracy did not improve from 0.97885\nEpoch 21/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0188 - balanced_accuracy: 0.9963 - val_loss: 0.1535 - val_balanced_accuracy: 0.9770\n\nEpoch 00021: val_balanced_accuracy did not improve from 0.97885\nEpoch 22/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0187 - balanced_accuracy: 0.9959 - val_loss: 0.1563 - val_balanced_accuracy: 0.9766\n\nEpoch 00022: val_balanced_accuracy did not improve from 0.97885\nEpoch 23/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0158 - balanced_accuracy: 0.9965 - val_loss: 0.1568 - val_balanced_accuracy: 0.9761\n\nEpoch 00023: val_balanced_accuracy did not improve from 0.97885\nEpoch 24/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0116 - balanced_accuracy: 0.9974 - val_loss: 0.1595 - val_balanced_accuracy: 0.9761\n\nEpoch 00024: val_balanced_accuracy did not improve from 0.97885\nEpoch 25/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0151 - balanced_accuracy: 0.9961 - val_loss: 0.1677 - val_balanced_accuracy: 0.9761\n\nEpoch 00025: val_balanced_accuracy did not improve from 0.97885\nEpoch 26/50\n80/80 [==============================] - 143s 2s/step - loss: 0.0140 - balanced_accuracy: 0.9957 - val_loss: 0.1667 - val_balanced_accuracy: 0.9752\nRestoring model weights from the end of the best epoch.\n\nEpoch 00026: val_balanced_accuracy did not improve from 0.97885\nEpoch 00026: early stopping\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Analyzing the overall performance of trained model","metadata":{"papermill":{"duration":0.244867,"end_time":"2022-12-21T00:10:23.870767","exception":false,"start_time":"2022-12-21T00:10:23.625900","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plt.plot(r.history['loss'],'r',label='train loss')\nplt.plot(r.history['val_loss'],'b',label='test loss')\nplt.xlabel('No. of Epochs')\nplt.ylabel('Categorical Crossentropy Loss')\nplt.title('Loss Graph')\nplt.legend();","metadata":{"papermill":{"duration":2.17212,"end_time":"2022-12-21T00:10:26.345518","exception":false,"start_time":"2022-12-21T00:10:24.173398","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:43:11.812771Z","iopub.execute_input":"2022-12-21T10:43:11.813156Z","iopub.status.idle":"2022-12-21T10:43:12.081139Z","shell.execute_reply.started":"2022-12-21T10:43:11.813118Z","shell.execute_reply":"2022-12-21T10:43:12.080062Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.plot(r.history['balanced_accuracy'],'r',label='train balanced accuracy')\nplt.plot(r.history['val_balanced_accuracy'],'b',label='test balanced accuracy')\nplt.xlabel('No. of Epochs')\nplt.ylabel('Categorical Balanced Accuracy')\nplt.title('Accuracy Graph')\nplt.legend();","metadata":{"papermill":{"duration":0.55307,"end_time":"2022-12-21T00:10:27.169212","exception":false,"start_time":"2022-12-21T00:10:26.616142","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:43:12.082752Z","iopub.execute_input":"2022-12-21T10:43:12.083427Z","iopub.status.idle":"2022-12-21T10:43:12.343719Z","shell.execute_reply.started":"2022-12-21T10:43:12.083388Z","shell.execute_reply":"2022-12-21T10:43:12.342673Z"},"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"## Saving the model","metadata":{"papermill":{"duration":0.257086,"end_time":"2022-12-21T00:10:27.694784","exception":false,"start_time":"2022-12-21T00:10:27.437698","status":"completed"},"tags":[]}},{"cell_type":"code","source":"model.save('language_detector.h5')","metadata":{"papermill":{"duration":13.209816,"end_time":"2022-12-21T00:10:41.155551","exception":false,"start_time":"2022-12-21T00:10:27.945735","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:43:12.345702Z","iopub.execute_input":"2022-12-21T10:43:12.346445Z","iopub.status.idle":"2022-12-21T10:43:26.907000Z","shell.execute_reply.started":"2022-12-21T10:43:12.346400Z","shell.execute_reply":"2022-12-21T10:43:26.905883Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"markdown","source":"## Model Evaluation","metadata":{"papermill":{"duration":0.407526,"end_time":"2022-12-21T00:10:44.284801","exception":false,"start_time":"2022-12-21T00:10:43.877275","status":"completed"},"tags":[]}},{"cell_type":"code","source":"loss, acc = model.evaluate({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']},to_categorical(test_df.Label))\nprint(\"Categorical Cross-entropy Loss:\", loss)\nprint(\"Categorical Accuracy:\", acc)","metadata":{"papermill":{"duration":20.437115,"end_time":"2022-12-21T00:11:04.969979","exception":false,"start_time":"2022-12-21T00:10:44.532864","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:43:26.909717Z","iopub.execute_input":"2022-12-21T10:43:26.910492Z","iopub.status.idle":"2022-12-21T10:43:44.789912Z","shell.execute_reply.started":"2022-12-21T10:43:26.910451Z","shell.execute_reply":"2022-12-21T10:43:44.788763Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"68/68 [==============================] - 18s 261ms/step - loss: 0.1206 - balanced_accuracy: 0.9789\nCategorical Cross-entropy Loss: 0.12063389271497726\nCategorical Accuracy: 0.9788506031036377\n","output_type":"stream"}]},{"cell_type":"code","source":"test_predictions = model.predict({'input_ids': X_test['input_ids'], 'attention_mask': X_test['attention_mask']})\ntest_predictions = np.argmax(test_predictions,axis=1)\nprint(\"Confusion Matrix:\")\nprint(confusion_matrix(test_df.Label,test_predictions))\nprint('-----------------------------')\nprint(\"Encoded Labels:\")\nprint(labels_dict)\nprint('-----------------------------')\nprint(\"Classification Report:\")\nprint(classification_report(test_df.Label,test_predictions))","metadata":{"papermill":{"duration":23.29653,"end_time":"2022-12-21T00:11:28.530471","exception":false,"start_time":"2022-12-21T00:11:05.233941","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2022-12-21T10:46:40.203859Z","iopub.execute_input":"2022-12-21T10:46:40.204259Z","iopub.status.idle":"2022-12-21T10:47:00.733339Z","shell.execute_reply.started":"2022-12-21T10:46:40.204224Z","shell.execute_reply":"2022-12-21T10:47:00.732166Z"},"trusted":true},"execution_count":37,"outputs":[{"name":"stdout","text":"Confusion Matrix:\n[[205   2   0   0   0   0   2   0   0   0]\n [  0 220   0   2   0   0   0   0   0   0]\n [  0   0 140   0   1   0   0   1   0   0]\n [  2   0   0 412   1   0   1   0   0   0]\n [  0   0   1   0 137   0   0   0   1   2]\n [  0   0   0   1   0 201   0   0   0   1]\n [  1   6   0   0   0   0 237   0   2   0]\n [  0   0   1   2   1   0   1 158   0   1]\n [  2   1   1   0   0   0   2   0 297   1]\n [  0   0   0   0   0   5   0   0   1 122]]\n-----------------------------\nEncoded Labels:\n{'Italian': 0, 'Portuguese': 1, 'Turkish': 2, 'English': 3, 'German': 4, 'Swedish': 5, 'Spanish': 6, 'Dutch': 7, 'French': 8, 'Danish': 9}\n-----------------------------\nClassification Report:\n              precision    recall  f1-score   support\n\n           0       0.98      0.98      0.98       209\n           1       0.96      0.99      0.98       222\n           2       0.98      0.99      0.98       142\n           3       0.99      0.99      0.99       416\n           4       0.98      0.97      0.98       141\n           5       0.98      0.99      0.98       203\n           6       0.98      0.96      0.97       246\n           7       0.99      0.96      0.98       164\n           8       0.99      0.98      0.98       304\n           9       0.96      0.95      0.96       128\n\n    accuracy                           0.98      2175\n   macro avg       0.98      0.98      0.98      2175\nweighted avg       0.98      0.98      0.98      2175\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The results are quite outstanding as the fine-tuned xlm-roberta-base transformer model has produced an amazing f1-score of more than 95% for all the languages.\n\nIf you adore my work, please don't forget to upvote it! ","metadata":{"papermill":{"duration":0.26089,"end_time":"2022-12-21T00:11:29.056296","exception":false,"start_time":"2022-12-21T00:11:28.795406","status":"completed"},"tags":[]}}]}