Practical_Project_10.ipynb

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OuP33ANp47b29Kd0X42m7MtHAnVpysuQpjg/LN/Ui6qWzUkJf+TPg+jxxYO0ccViwzXOn6UH0byZ7Sij4jG6TJNrVqSJp/g8utPwresU6+zGcfjXrJ1pzca+lEN+lqStdUhrvLs6l1o3GUY+T6TvsRQ3lH9LWF3dVHsE32Cx5B8vGrRSalvQlzdlC+ajrKl/0JW1N8n/7GpDWmV8g8nq1cr1xxvbulKd9uvFdTnvq9W0C1UulETMoxinfZ+juwz1pFih1Wnp+RlUXLon0oBH9tdpL5Udarez5Nx0KpEs/AE3fS1nnLbf0Lkt02mHf6h4Y1irQUUpibyZH9JXIC9DvmT5eEqCyG9hW2o9eB+9kI9ZxHIgYl1R+Zhqc+1ma7lnnJcsODOfyqSQPwgn/E2WLHxeE77MYxxX+69YRu1LyhloBI4sm+xhW7oPH2cWH/qRZnrp03Viylo4IyWMnZHaTxhBxQ3XbtK1QS/dtYEUf2w773p2Hc/5h6hXxcjnKXwZz3W7ZtSmdj45T2Zv6b/jvrTbKI9qNfMcDakWvi1FTVkp5VHNdHnUHXHZ1jzK/vPXm3Y86UZZFctI29LBgJXLqV0J5fiq/TWBNWzLe2Zt8aZyaZUyRW3bjcKEb9tg5uvc03R17r2q6txq2VJWD9uylprnNyQWHZX5xMSRVDxCPmzLddacGSPJM63y747KurPe9U1ddetJ9Qi1v8netgfGv71/i78NdXLbfjuYxZ2hCK4dgp5sEhqnTSEkT55cSJtoinAlRHk968Jn4ci0auI2OaTL3UiYf+ypwnqC8CXAV5jc3iNs3VwdhEMPI6/H4eMFb8N6bm5u4t8yraYI+y6+UVzfmmB8HMpP8FNcRx+M94NDimSFhG7TjwnPP0Ve92vgTWF+N3fDulVGHNMdncjr8TZTpJDW6bs+8rG6s7WNYRteuv1T2oY+PL+8SRjcYargH6j8ubrwTFjWMa38nW66451C8Kg5RNh5WflYP7+2SehZLaVhH7NXmiLcUDgeHDdOzpTiRrZK/YQdJs/dW+HUAi8hl3xMMuQcLZz9oLSeIBzxTi9/r7SffD66TNwuXHuhvD4H4+O56HSo4jrWBmvi0J2dnYWcut+mTxumzuf13QOF8qml/UyZtJGw9T/lfTWOk0rxh8Ob896GbaXNXVtY6PdJcT0pXBIm1kglb9NN6DDlmPDii9J6HMcvCZNb5jWsO27na8X11IaQoEvC6OrSdzfov8HkeRS/t0X4tKWLcgrr6uJyhzSG9ThwXDYV967tGCiUSCWtlyppS2FvpPzovjCveWozn0cOV9d5GdL3iG2251fWxFst45iW+Q+n1xS67fCx2/NA6TeELz88ak6xKy8LDvIV/s4opUv3BiuEx7o3I67D4curnUKHPzkPk/IRXr+amXT86cpcQzxp4n0x0udfXx0UuueXtpMiWRVh9IbrwjvdBxHX4xD0cKch/+R1l18IUVxPbXBEGSWmyxpSvLc2XUbcHp+TQQWl35ut1CzhpsV6x2dhz7j8Yrzh8zPzyMdI6zw5MFCM7+I2dWWLqbKKg3Gcz1FRzfebDne2tjWkbfvLZivzXcNxNp/vOuLYWBfHLgmTmklxh4OpdMVlVHV3Kc007L9feKF7M+I66sNZYUQWKY4VaLleeG7hHDviGAV/OGsoyzhv3XIrYrp+JqztHlZ2Dlx/N8LnYcGa+oXacGdnFyFXSul3mItvIZ+fCftmtxby6dbV1705mC6DtK8/GP9+/brNBinnQyFf7wnrBlQwrFug5QaTcSlSPr3+mup8esW/lutjaq9V1JfXgyyW1/qgrxtbqqeoiVtiXJbzfw5Fm0wWTj+OvB6H+yfmCE2LhJ2r0h22mzz+YfV3KfxZqKWwxMTvD7g4R6idRr9dN2HhKfvKSXVBl49klr7TXDzSh1MLasjlFB+jKYLfE+X1pGucpoY8p3zv/Wbi3SahZT5pHzLkGiKcjVB+GNeZOV4uOB25fNQHh1z7GO0fB3P1g7uHxgo18krrNh3tF+E3h9XVTbVHBD3ZKX+XlK8olTfiekZplUO3FXcF3eYirHdWGFlYOm7c9rDnrunjwXng/tmdhGEblPJo3XYKSd+lfjt3FD9Xcy2n5jhZs961rWHlAMefUev/FQK/Rl4v5OszYfvEemKc5fVmHQ+fn9lz7WYqv+O8qUk66dhaKvfU5HfXtkr5LK8n5vcm6uVcL145tG64dReeUU5XjiqbtAzq073x9bJ0LcTtBYPmmU5r4a8N1tt93q0/nhUNea6put6RafkM2zJXz+JrDS6/uvTaoHitqKas5DyqVX6Os7o8avJRIcCKPEppPQ62nL+0iSYLl4OV1pHSvK3pYNFZ5bYjq68JmuvjmflrbTXB9rznqJPajaRgfJ0hxg8uwzSpc6sLtpfVp4VA3Zthn1ufT3Cw7jprkFAhjbRujoozhRsm4rLW7akc1NadOTw5MMhQfjqz7YGD/hpO/O1Hn2jy2xHCB3SGfgfBuGDngqta/TpCHStC3XpDhOMRLva4QUtfKFi6MJDCfWGZoUHI9EVh+ILWTei3xHTjsrVBTQVHH4z3gysGC06Yzvg4fP5vsVBVztDd6ig3xvM2zXWG6jPm1ImHCOc+hv/MMUFX0Bk6Q7kxY4PwyERjlj58DQxr+ONgumJ3SZgzZKVw+53SZ8bhs7BntL4BPLkwy1e54cO4MYXj22YzBZE+OKRBwIo4xA0h28eNEc48V/osfLiyqqlhX/usVj6mli6gresI5Qad6obtWWrg58AXJPM7ShUj7ri2N45+urJYGLv0usXvDf7gK/QpIF1MpEk82kTFP3yl1nJcfius7R22vlL8eLynt+FzpY6w8OGSMKGKdKFo77GxLt5qF8e0zH94W6Y7Q7XtCJUC5yPpxO2Zq9g+3vO3mAf/mXOysHfXAHH9P3OabkA6taCiuE6KZGUVGsXfClsHquvk0Ievgb7CPxWluOzeYLnwyI5KsiPKKA62pkulY3hyjnT8+PjMOmo+PwrrqNZdbJSKfFFk3KGhrs4hCE9PjDPUU8w1QlgKR7wziPFZi7RhU77bISxvUbphyFHHxto4ZnzzgLkGwJCvn4U3bz8rfmZdUN8Z6sj48/neKkMHTvZKk4WLb8M+M26QsnS+ratfWA7BQWeFUZX19TVdHrVOuXHeODz1GWeoR3AwVQbZW39QisfGv5/3d4CJupA+cJ7RPoO0vumOgMj5tG41hfXCQvh82nR9XtqmtF111yra1gk5qG3QsRy3uFyuZKiLl+6w3mL5FL7RzU0Yt0s5fzCuv/NNlP6vI69jHMLKDV1dd90Ti+fL/qC+M5TzOH3jHad1NXWXq+v0dTrz5eArn+GGvILrsGHxzrjOJKVjc8dE+2sf4zQkpXdLcZ2vR3pPUcoXjBvyTde9nhwYK8w8ZPk6zzjvzd9MV++PsD2O9/qOto5LbK8DGKcfe7bDwdK1nBTUHSc164lln1xX43J8/nFLN7N+Fo7O6GTyGlLLOiIH43NkqdyzlN8Znyf+rZaug6W0Vd2Q75lqkDfeLqdBbcombYNtnaGcj42O1ManFLQ877YcT/31gXIHYNhvMnd9oyYY75u5+GhrHmVq37TsDDVOU+rTQdh1ualOHFuuCUrK9UktbgqwKg4WlOq+zmw3EtuCjOvcKtoFnvlNU1XnVhdsL6sjdxhbn0/Yep2ljyPdlpuq20Rd3dm4/HR224MUtP/tCOGD4mQa4Lp43nO/o77ka0U4ceQBvdfPmCQKpL0rJ4hTmvBQ8fZjR5mYutJYKmowYho1SSlFucvLFtEho7m2lZToPI/+aZrJKc/dM6fayInUvJD5KU7ipM9HBZNJv+3N4ef0xvxPM0uIEUiBH+QFJ4kd4kUjx9SkZPr5fkyIFc+d2vZqIz4nix3e7EvyTLgRZKfW/zQwOW1WmLiUv2hV0s8rd+9x2NRbSng6gz6zJ1LVbI6ZckZb8alS7z7kLk9HYk56j4qUTz72Fx4+l15Y4fkZb9VT47LQL8dp+9wz4uvfcw+igd0tT6cQI3Zqavz3GPKME5M+vfSmnQfsmxA/blYv6t0kk8XvjflTXipVQzo4oTFv0UPDtB/K1MXl+Lp4V15+TXRHId4lLlGdWqaS0vTxuTvogplHT7w5tZOWXtAdfB2PNpXJzXimEYfSPo45Nv/h6eW0mRo3vLiUq0Aj8RU/DP/gybDpQsIEks+BtTwnHmX38qSSnqWp7k8x6OPLxXTqrFKcukzHNl4SX/2atSYVyBG+IPpyX3fOlz0VX+dsNIH61LQ8F0qseB7UdUBb+kMXrZ4dXkKH/OW5fOykZRlle7oUX4bjVqEpefyPj1sALVt/wOxzLP47sJq2fJKOR7kWFSlDhGmfbh1YTGse8QbU1jl0axbqQv06JBdf712wi/6z83DbmzZsznf7jDXku7sORH64ijOOjZo4FjdrXiqjS1Ps1ekH9NrE+Y4ROy7Fj2/fROvWcuQxipO6Po3wrimm69cXxtOYmdJzrXja527ytEo8RVqvNs6dtijQbyfNuCBV3pMW70Xta1qemjuxZ28aO6qYvKRMi/qDUjw2ltFrAg2qZz5P5emsPMrHFl+HxLpBzxSqj0r5tKUzr8+nuZ6r5rnh6q9VnFcntFbw64O0ZtpFcdrXH/7womFDdHUoqbgw6X8pqtP4Ke3k64EAWr10h4nrAUlMwYOGTu5C2SPPcBhOroKV5FdE1x+avyZwLum58P5f+VrEnfqN0v0WFXWXzLUH0lAxnpovBxPo0p7x80OnrpWmDH52cAR1GivVmbJWHySmY8u5l3aCrq6hSUukAl6f3i3F9fhunWhsV9vzuySl/jY5VaOxOKk9qVwxqbLwfMcDs9fegYGB8iv7aLUdZzm3aTytfSgVXhUGjaTmhS0kQF19ukgHb5PXkFrWEbV2futccXpTVmHwKLPXwZK4VLTzNBpSQdrPV//Opf2+5p8zqFXZFB2IU5fP7EOFIkwJrcRR513t8SysO56c7YTEuqk7nqYT+peXgU55Zp4j8iitnN86jw4GyWledTrwlsspTgdzdOnA/PND1V4TlP5JqkiYuyZQy6o4WP1/YnxxZrsRtwUtMdS5e1MHFe0CifJ1tljnVsuestrcFZnafMLW66y+7VOI52rvQt11Vrj+Br2oqzvfOrCE1j7itBRVbQ/R97rBVVi41AGILOTdWTq+SWr4T5ClLdUoZ7kywGL+5EE1WkrPzwmNeYiOnzdfO0ydMZXNF1Jaih9fze9LRKnd7XuwTcZMtcS/IbFWUwev/rT5zH36Ih1mh4sZmoTi/ywvWJDAswZ5uUm/9eUxX7qu1O9ghR9+sXRhFiamkJrSp7ZUqfv2xIjzA8WPYbmAVcIdoU1rSc85UNMRygL9DtKCB1LpXKVlDdXPtuRKfVm5Ur/n6BmnXHBwJf0H/QOcVLAmLusppbOYcTyocgspvzLf+fuANsydQS90h5Of7VOnUg75/ejFUhxzfP7DHaFNHdARKvkjfymxc5MdVYibwa996fDGYBJ0FdpqRfNSrHi6i6X6XGsMoA17femLtJrBl/v+dPSSVCvPWClfpDRy+/gW8RljXEGuX6eU6pt24uv2s4Lu4lCIcZn2Hb8kNj7by1llVHiW02Wc1JWobl3p4vr+2lVmboC6TDsWHxKfp/1jws7UpEbExt4HdGzrYfHVTwmbURWVdQ7ex/xFq4mv3l7dSKcv2Xa0xbSh2yF704Ym+e6xM/Q+3IWVc46NujgWJvgpkfRk0OjA8cco01/jybtHbjHeHvZuQ0OXbqIp/UfQRd1B4OcWTZqpruNESzcubhNv/mA1G1S22Lml92t8+UGEJmgVj83VH35I+KuV9f8g+qrQiGJPPl1RV55wPr1Xl0+b44hrFXvqhLZ4ef644WYUz1b1qVAidd9tfD3w/NBBuvRYfKkohvCrrm5m3W/6GMK3FUQPoV/O0oH5T8QklaJSMyqfU2XEp1TkWc5DfPVwjS9dMnkfodQgPba61Jq1vHsnmr1pFQ3ss8hwQ8UUbrBV+7UauXhik3zzM1H9Dg1Up3fniEs/xjMdp/6XNCPlzi7t8IHRTanT1AN056X1cYq346bBdpxPuqmP42yskPrUuHbOCHUrR7Lu2s1+l8lnq5RX829tovut6qSiyvXqkZTdBtAeP/P1cq3KpuggVkhOSpVK63Nk3XnX5ngmpoy5s4mv3lwbTi2aTSWfm4GRruuihvk8Snu6dLAtLM1blQ7q15NfB9Du0+brPdH7miBq2o1unFsnP1NSV+duWFn187kt1bnVclRZrS6fsOc6SxoU8/bqJt11VrgLXKtpW3fW/aZth8W2iZ8Sejm97cFazr5ucBXRqkoLjscPQd7/4BE9fKQ+3HsS/iH1X+9dpgOfpISdtFgOkgdPqZIzb0n5FdH5uw/kV8CSlutMY+slE1+/ubCEOlUrTBlSF6Z63YfRsk0H6cLNAKd1jpr3J+UsIt/xGHMz3X1gKpMPpFuHV9OQnvWomHtKSpkihS7koXK1W9OwmZvI385O1G/Fo/ObafqANlSliP4YpKBiVepSv+EryfeOfXcYG3eE8p3qajpC2c3rx+VXRJlTJ5ZfqZGKMueX7s58sPgSmTz1KoW+u0971w6nbvVKUu6U0rFJ71aCGrUZQAs3naVXUdyaHjayjWjjSuVRD0FXd9PK/dKOpq5bn0qmc35FRIs45tj8hztCvQwdodyop2VHKAvr3FRucHzhd1hs7OUKbcG8XGjFp/wlG4ufXV3jQ1ci3AzOjehnxNOamKoXyyu+F0aXt10+Kb6KFZqXUiRXf85jxtHln2WlNHT17E16ZV+93yG0S5fxqUytjuLIIb4Bat1O5Ytr47tpPVpXIrefxJcGIe9u0ZUT0ue/F0hFia1IYj9nyEGl4kidGv66OGyLpOU60Zh6ycVzZk/acES+G9XH5lvgnGMUlzzbjqBOBaURYEv7dSLv0xwpElOTYYNV3U2srQC6eVnaX76rO19ObRpbWHSpP1hmfz7Nrp7R5dMa7qsj64S2und9j6HjPG/2jOI5Uic75S8tlbs8K8Ojxw4/qVHm63+Xye+zVGD/ljU1/W7FtW/arJ7i3+BYS+jaTXPHKBXV7T+FKuriqhDjHI3o1It26Y4p1+97DW/r9BsqOB+5d+mK+IpH9ubKpl0+Yknw83O0bvZAalHVw5BOMnpUpSa6cnf9gXsqb8bMTq2G96acYlIOoM3jmlLRXOmpQMkmNGTcKtrje4kevVXTqanbzoi/NdiOcwU/v09XrkhxNmExd8qkn9bJTtHx2i38b82r+63iS1USZi+sK+elBP3k6v1oWS+PDqLzNXu+xiOocwHpxpz/Doyj+iWyUhbdvrXqO5bW7vSlq/e17xzVJo/SFqeDqzanAw+xvsvEdBANi/PoGwcD6L58jcB17vwa1rnVibqymtl/nSXdJH5JdwxNRTtn152j0/V1dLxucBXoDDXrHI3KEhbplEKaZK1o/yN59e+FLl/Q3/nyv19+sOqOrthJMxoK2tfRtKCNOqmo0aSDtGZkQyog307E0xr7rp1D/To1oSol8lD6NHmoSvuptPNsVDZcWr7jKuTdZZrasiSVaNST5q/2odvP9Cc6QFew7KS5IztRpTyFqde8i/ROvGvf9QjB92nDkOpUsEpHGrN4B124E/Y7b5/3pWWze1O9IsWpxeAd9MTqytvncB2hLEGeMpQ/s7pRs58/XpdfEQ2obj6PixgajP4kVlTsnfrnhf9caljSg1p0n03rfW4Y8pQvz2/SkR2LaXCnqlSoWCtad96+6XjtYTyyTXm6PGnatBviOUhMjWuVVj3yRAvaxjHH5D+hwVJHaMMxp+V3dBetpTwpo+aNemGdm9zgeOykcbwJP0VuFnmApH406aeXa+jUGeMpgR6Q716pwZ/vcCyYN+JJ/Uxv5cKLG4CbFVBOK8ohD/XZ9EVMQ1pMG6Q1rdNlgoKVDCOHlKebDqT9G2aI38Mjq2tXyhGpIT70w1t6IedzD3d1plzyBbCakC5vR8O0adcf2Jph6dLGxAO02s60oU2+G36KsKg/NtGfs45RzJ/cqYf3WCodN+zSyqPDNOpfLYm8FHVixNauYNIuHkvbcBw78+nNUqGpVT6tvrzebkOd0D5BIS/lV7qS9BfrprBOnLaM/EqXRu67bj4ifAkyjLrwn1SdUinGG+XwZ6XR0j/qKE2vZ0ycfnh6V0pr1ELTaMJ08nKPmllxguQ+Ph7Z+0Nc7fIRc27uGUR/5fuLug9fRHvPStMFs0/3ztIhXbnbrakHla89lA7dMz+VI+NpANcdmk9dq4ZNE/74xkGaP7UXtaxbngpmS08ZPZrQiPkH6JGZao1W23EmISSI5OKL4sS3rr3GlOh67WbPb/1fitSUVv6HgK0PyMwsrN8tPu+NSkXfa3auf/VecZCm96lMyeW8k/dt97Jp1KN1XSpbOCtlSF6YWvddpEnnwc09g6lqfm3yKC3Zmw7SReN0EN3bjfRlJdOyzq1WVJTVevZeZx2Qp3VWejSCY9tTTePf9NLwm7pEyfW19NtrUCEn//bvCTpDAaKV+OTZbDxtOPUf+eybT6O6N6NyeY3nnA+gC1vHUZuqeahSt5V09738drRymaZ5VaTxu6XnNKUt1IambzlBV29II40f3L5K/qf20PR/8tHpWZWo1dB94hQEriWQdgytSV3nSSPhfkpdkUYuOUQXrt6WjsG923T5/GFaPrkmfTrUluq2X0gPQ9QfhPuHx4frCGWvL4yjbn03mn1mk7Zsn/qHn6XWpMpQ8hGf45CYyrYcTztPXKXb96Q4cuvqVTH+d8r/H42s0oTG74mqyW7iU7mGvShTLF2FJMZlWrUt/HSq/JytLXOladN+z92Gyng67WGhOgpxbLG9cUzb/EeI8Za2T+KpcU+Lx0hv36TWNGJD2MWjVhLm8aBqP0oXALtP+RvOVcQpcvWXCMZT5e44esaw/tenZ+nIISlyi52nDqopRa+pRKV02fQvrdNldqrcTJoRQmm6aX6m39q1UkYmjax2XLXUvikXo0vZ7Jgp16LTdJTRlZpjdGHPWsOzmtjlQ7508528AEai79SBEWmTT1tTJ2xndZ0Qvh2WZxEIJJ/ta+iu0ei0wwfPOrFuH7WeHRxEtVsulGe8SU11+y+lo+d09RBdOpHqIedp3/qJVPKXPdSyWidafcviAaVf0lek3rN0efGlw7Ri5khqVb8IpU8S1lj86d5Bmv1PUypavA7NPmy6Id2q7Rwy/1zkb9G3c+0GWtJfGxwTR/5H3/Me86c/qXqXuXTq1lVdHjGD/unQgAplCZu9gm9i3LVsoNh50Hqsj80jN58dHEx1Wi0QH4Mg5lH9ltidR4F51uc9QeHaHeDbEPk6y7Htqc5g+/W17rcPq0Xd5kltaNq080FE6Aw1KxG5dx9EgwYONhn6Dy5NyaycZ/ybF4vEae/Y1/efrZp2IvjpTcOdEuI0Q2HXEGAkRuy4lDZbRWraayQt3Hpc7EDkQn5ws9KGO94urutN7YcdiIJpOILo83vTX/p07xKaeEpq5cpa3ZvWr/+HqudLTfHkdBIjbnz6LWUOqtbWmw5sm0eZYl+QPnAhxg8x5ylBN+2dT15lMtEf8aU77vn8/po4I5WoO4iW79xDDZI8MtxxrsbhjZvEhgJ+7uLOc1sNU8Nc3dyZ+k0K69QxJW6ssHlTRm5+KBaq1oaI02erF0jbFo2UO3ITU4tpm2nRsIaUK3V8iiMNwqQf4scX43+XaYdo3cLc9PVi1M1ZFN+tGjUqI+3Yv96b6OQL8aXovwOrDc/Zqt++Af0pjzh0BsU4VlabOKZV/hMa4zht2nBG9yoxVe69mU7t6GuYZmxR5060ONxoTPvF/s2TStaUztWtmQcMU99GniJXT3mq3Md+u+RyKjFVKKD8DNi48uAZng5n8WnlNGIp3A7oRlmdGGfM06XLxaPIX2z11zZdZihd39BJHXG66Qu7lxqe6ccjq39RqJXGiBWX5AklKGVFb7r4UPl4Wgq7euaTNmIHe9KGVvlu2ZRhFafodGyiK2cdo8AL0+mfYafEi9Y0hT3FvO7tVW8n36SkTAjWbgeitv5gHW3y6a523xDj6DqhvYzPaeB76xpuAu7ul19ZO23yN8bo2jdnj830QCGuqAmjapkfKX5j20AasEiKK/k8PMXv5JEWaur2jqBPQ3xz2+cgR2dkl2nFpCXiiB+eGnj41kM0qWNpSp9EVw+R1/ghfmLKWrg+DV+ym9Z0+Y3uX1a/Tz/+lpGKV2tGQyauoaPnHtKNC9yp2ZcqZ5XOyZfnx2lkw8a06qL5baraTqMmtPLfqLt2YcZl35dA69prIove1272/Navj+7TXfkfEle1bjpE1xfh2mDqpmh9zc64nSlr4erUesAEWn/gPN2/wZ2TM6hbzUxyHh5Au6bVo25z/MX1raPLoyYvoRe6n8h51LAtB2lSpzKa5VH2sjcd3ImW6cD6vCfYP2rjoJZ1brWcW1aHp911Vv5ws0NFZd05/G+a5vTra/7tk5c8Fq8pf3PrTRv3aNfOB2HsvLxzdamoQtu21La9udCAsieQV/9O/C9NdiotN2w+PXqJHlhR3vifPSS/IsqTNpX8Cizhih0X8m1GLqUTV7YZOr8uL1tEh4yGzDvHLfL3ke5yixVandKGe6h2IJ05tk58xXPWd+1Zw+wDxP+XoiING9iZXO15z8YPMW/Ty/xzfmLFy04dhg0yTB+t1p+lB9GiJV0pV5K81GP6NPEZQ0zNqLs0mcvLr4jOXL0vv3KOkHdn6fgmKf4k9uxNHWoaj66KLGP54dS/W9RMDyZJRZXr1xNfhcRaTZv36o/tZdqx+JA4qvnHhJ2pYmnnFgTOiGN69uU/ianmwCU0rVt+SuHWmaZOryVejPLzsIa37En77mmZf8WnImXqiq+CY2+Wp74NoANbI0+Rqxd5qtxA8ju2R/zsx4T1qKDiM3gTU5oc2cRXPP3i1RvOnQbJEfTpks+G1uky9m+lqHpb6Xm0xtNNh345TjsWSo0RCbK0NTmyOnai1JQtm1RdfbrNn+5Ho8NtTdpwRL4bnY9NdOGMY/T16S7q1WasOFLgxz/akPfCZTS0bwHxop5vUhpkU6ObPRJTxuxSxxTnUWf8tXumTFTWH6wTffJpZ5bXthDPqfx1Zy/fFMsBdS6T3wFp3Cx3OFvzXNZvDV/7lvpRykeenbpBTx3Q1hroP526dtogdgYmLT6eZq+dToPqSmUn1+2n7Xb29H9haYhvbrt4xbHPpgq6epy2X5QSSua2faihYv1LLz7lbzGCev8lt4bb4KdE3KnZmebsP0cHjOqmEWeBscTUdlZv96WonHPBuOx7efQc3dDPK2mD6H7tFv63ntX9VvGlKi8vn6CDX6QEnczK5wG7uvDXBr2ofc2waaKVRP01e2Qxf+bOyerUy/sQnTk1w9Becnim0qM7zOM8asdFKR1kbvs3NcxvbkYo+/Moa3E6yGpzOjhuGLAipoNoUpzbkvf06/pjpEeuOJauzp0jrM7tp2GdWx3nltUROeo6KyrrzsZp6em2S06/vr54YrP8GC6i1j3bUg4zj4GNiusGV4HiHqzGUwt61JAK9jfX5tCmveouzkI/HqdNC6QGIX42mEceF76D2IFixXOnZp29xNehMQ/RSysqOqaExnxGgR/kBQve+G6iJRekkilhUU/KHO4m57BnNKmdsz5GbGdOLeocxs/UUvX8pQgdNJZkqTGeFs9sRxl/kZb5GUMT56kfdafvAGKHluyi/5x4A53xcwVUP8+CZzKNQolLVKeWqaTicu8C6Xi9ObXTkA48Wlcit5/El07j6DhminX5T2LyGrWaxrfPRfo9zPTXeJreJ7/4OujFZurRYRpd1nAayT/yl5RHIQbQ7tOX6OvT47R3Z+QpcvUiTpX7yeiiK2u9IpTNRPaUq3ANcfpktmZzVIzQ15bxszG0T5dxqViVNmL+ZDzddMDhzbRAvpuqXMuKlMFkHM1ORWtKI3SDY8+NNNVudGEpbTgm3/02jk3UcvQxekBrxw2iXY9CxZEC/ef/Te7x41KB9tNoTP3k4hq7h/XXfCS8JZnca1NC+Spv46od9ERlfHsbaL4RJSrrD9aKLvl0VJXXaklTzEuRxXf+ajr5XN2BMr4eSFSyFOWQortL0tcVOOY/91lChzQeeRL68RzNGjxOHP0S94/qNHp0A0oaIzHVHDFXvskmgGb16U97jefPdQLjNLR65iqHpnfj57L+7xc19ZC4FCNmxFqdbTLXaGt4vnnAnQAyMwGSWZG2E6X5o1T28RHimzmXr/e34kaH8KL/tVt2KlJVKuf5ty7T/VZ1HtCONWvkx/UkpvL5Iz+3/nvm2GsD5+P2kja6a1L25fktevFKfKlaVOZR6ujSwV9had6qdLB6jfza9KxIUeFbaTfKlKuq/CryLEjmfPqgTcelM8vqyBxznRW1dWfdb6ohpaWouL6O7tcNrgKdoWCD+FSuSS9DxwtfnFke3fOAVg3sQsseSjlz7nbtqFx6VDeN8ciCUePUPcPg8wfbH8asJDjWEhrQd6PFhy+HvDtOU0fOMTy0vER1zwgjP3+lxHKnkdppGoRgK2/L+wYk+qOA/IrUTTkmV6zVKlCyNKWVO0L14rt1Uj3qLla8ktSgn9Qh9ebacBox+YzTGglj/Z6YksuVVNVTuFiIl44WM44HVW6RU3zNN4DsO36P9m+YIaYDvrGjdiXnXzxrHccckf/ECs1LJUpnMnSESuJS0c7eNLaeNNpB62fdxv4tL3mUl27WubzEl7b58pS3SlPk6oVNlXtx2gHafeAArf3IO5OYKhfLZ/KiK27WetTDS/oNd9cPpDFrtX8GqjNxukzmwHTJx6tlPekLpOmm7xsuvGOF1Kfq5dKYTUPuNXpTvRTSGgsGqKlzaEertOGofDcqj40thOAgCgx07ngZxx2jIDrm3Zn6rJGeHV1h0EhqZhgpkIrq9h4mjkLQl8lHHskfOUGCgpWoqZuUFz49MoFmbbwrvjbn+Rlv6tP/qLykLCrrD9ZSyqejYlcdXSe0F4/eb9ojn5gHf36xhAYP0V0PWGhI+/poM/XuNlu+HkhMXq1rmp0J5tsXn8o26Ec5dcUY39Qzuv80uqzZYyH5GrkNeZ/mE5+Y2o8dRWXTSAcz5k/u1H7Y3+I1N99A1rPjNLrpxMsm4zT06l8pvVuqH/CU4X2mWj+tb8zf/qB8cj1E3WN4gkgINZeig8hv7nDapmpUeBAFmRw5qdV2nI/Lvroppbrv7uEDaLGfpcgTRD4zO0e6eedbuHbLU7WzYdSfuhuQuPzuQkN2SzuatHgvqlbE9W7Qtoejrw20Eug/l8Yvu6Fq/z5/eC6/sp6UR0lxTJs8Snt5qnaiiinkNK86HXSmf/ZIPY5Ji/fWpYPoM7r3W2k3ip+/EnVyk4770yPjaeZGy+0C767OpSETjslL9rGnrNbiiswR11lRXXd2r9HLUH46+/pa/O1ynTo6Xje4CqWWQQCL4udsTf17hY3uaVetCS30eSYuRxT8/DhN7VBfbCxiPIXYsB5F1N3Z4yDPz96I8mc4hSeNLJg5tR5V8ppKZx6bzvRCXh+nxQt3ia9jh3hRloxyTmmXxPTyeBeqU38o7fJXvvPl5fXN1LdJPZonPwOF526PPFVFXMrpXlt8xdM0TJ24yexx5t8ybuR0+Y5Mx7nggKnczMWhdLlLGe7OmjthjtmRb6Efb9GyCcMNU5PYw5pRd3nrDzJM6bhvUjVqM3Aj3bVw09Oj86tpSEfdNu24iS1mnByUr7rUQBvga7myyOuMmqKmAcKx3Co0JQ/xAiiApo3pTAvWSDXt1HXrU8l0jilKzcVbbeOYs/OfVNTAMNpB/bNu1UlMRSpI0zh+fLmYRozYIeYvSlPk6ulHOvGdf507LhLfix1c3cQUuXrxqXy7gXLDSwAt6lGdOk31oVdmL8AC6dbhudStt+WGZmfTp0vx1zgkXcanMrU6ijdr8N3Ki4aOoxX7pQvv3J1rUKE/xJcmxfqtJLUdVFP8f0t1Dr3Qd/dp15zONELFBalpurQxfjDNEtPGFLvThiPy3ag7NtbjC+9aBTNQtqzpqfGAAyRPJGG3l36X6N57eUGBo45R4IV5NGqCn/g6a/VpNKxtrnCd+v9LWtEwawN/74hR6m780KaOmp0adfeiP8TiKYAWdq1hMo8SggLowJw2VLnGGHr2u+VZW6Kq/mC9yPl05ynH6KXKfFqr6wRH1wlDYp61cxrguJTPaxB1KijVza5u7kL1m+nyO+myLZKHJ+dS6/qdaNdjaR/ztPGmtiWd13j69dEu6lLWnTJ6NKEpe7TLw176+ZvNR+JmbUr9ekrTX7++MJ7qNepPu6+Yj/jBz8/RkmGRO5eM3dg2nsatlQ522R7zqEuFX8XXevFzht3syN87aqafqsZLba59jNOQlN6bD9hocvrF+4fHUfNWY2jFOF2+MMa6GyXipM5JxXJICeX6nLG00mwjfhBdXD2exm+Thwwp4Pz5n1GzqUOJOjRmoz+9N7kqb2sxrZZnqsiaL2O4KSJ5O4NH2rCdvLrtRHFLm1j2DawplgN8U87ganVpzIaLir9BCA6gnZO8qKOunBpUtQ7NOREWt51x7WZvucflbe/BUjnPv5V/w9iNNxTjYMi7W7R6WFPqNPa0eIMM39j6z/AGlMxp5yuIfOZ6UYGUeeiv9ovoppl8x1patnmEvzawfFNVVFyzS6PqR9DUviXN5k3s/e3VtHSplNfGy5aP0kl9R6pxHlXUkEeNo5Vmby6wnEcpsff8cTroJdd31acDqR7L6WDICJ6VQFyMFmzJe0ZP/eT0G99ixnGn5j2bicddtxe0sIvULqBU3+S8luvcpcsNJZ8nWu2p7WV12zF+Ns+GoGffdZZyvhLVdWfj8tPZ19fpcpc0/PZ5E+fQJTPXTlq2JX9voriKBt8uaXTPlEbSswOCnh+iwfXcqUDJJvTPuDk0ZxaHsdSneUnKm6cOjd8iZXI89c/MrYMpj5k5vx2F7yxKl0QqIJ4e6U1N2wwV93PGyM7UZbazn+cUWdzY0vip/w6Mo+r5C1FFrwG0cNNB8j3uK4eDtHZaZypZui7Nk+8ebjqjLRXU4FGFcb42o8kL+lC8M3OpdYVsEc7jMOpazYNyl+pIq/ykCzw+j5NmdqE/Fdo+kpbrbBj1xZ0ctWsPpY2+N+mFXNHnUSGvH16iLbqColTpjnQlVm7pA439mvhPuUJCtL5vfeo8XPo9/3RqQqsu2lZYqI1D3GAytLfUKckj32qUa0WLd/rTo7f65osgehtwkw6vHU6NypegpQ8yGAo8+6gfdcd3m/fwXkIN8kvFwJHFnalo1sLUqu9YWrtTH+d8yefgJpo3shdVKZKSClbpSfM3j6WBk4/Z0XEVn6p1nWJonOTKYvPBK+n0zQD6Il8vhH4IpLtXdtG0LiWpYkc/+iV31BdVcVJXorp1pcp44Pmzhgf5N65Vmn7RcPfUxlvFOLbD9jjm7PxHjH9WPutWreT5K8rPTQiggAA+ZspT5OqFTZUb5s8OpU1OkavH0y1NWjqYiiSTvmvzuHqUL38J6jF8MW09EJaGju1eRaP7N6Vi7tmoRKOhtH5lZxq37G6UjFAyTZcuu0yW7yZ2TLrkkWr66eP2bZSeAxJDyE4N/vJUdXMU3+wxf1gZ6UJLrnMULNmRxi7YRIcN8dSXtq8dJ9Y98ujys9bDNtLcjgNo+23bj3ZY2hhvd9qwN9811QAeVcfGOgG0d+kEOi0XSEcWjaU98vNjbZOIknhI8enTi7nUuXlPmjhzDs2aMIDaDNgRqeFH62PEI+N6th4jlgU//OFFw4bUVGxIje8WdvOguRs/HFFHTVJqAHn/LXXehM+j9PW7OWLeVLywO3kN20G/VvWmGYPC7sQ2xVHx2BEi5dPj61N+lfn02GXalEmOqhMmTlpEfhVAk7s3of5ivX0s9W06gI6+kD9SSTqns6lmRumLxfwuXx4xv+N0xXGF01bD0nmoUK2htF9OE1mre9Pc/s68wTWQ9szuQBuvPKNP9w7SxOadaMMV7fKRTs3M5SNcx55Gs9pIaerNhSXUqmw2Kt14AE1dvdMQl7hMWL9ouHjtlNbtLxowZyON7jyWzis05AVeCHtO6O+5B9HA7sozUmT6qzf9XTeZ+L37JrWhkeuV6xCOuPbhNDR5zQRDfY3Te6lcynHDo9FUOvVEEKcML1EmB/0s74s62anVCGkUrNSIX5J6zNhBV+8HGvLMz4EBdPXEahrkVZJqTXtKGbOb/4If/5BG5k/vXIHyFKtK/YavpD2+Yen+6O7F1Ee3rUo9pXPAN/r2aiTNBGPM+u30pp6Nwt8cE1Uy/TWOlo6vbegQnd6lIuXKV0LMr8OVAwXcqc3EY+L+J8hThorkMO6Ud8y1G5d7aTUs97icn9G/qJgG+Ld6dy5Jbp5Vqa9RmTekZzUqnK049ZrjI/5WjqtDNk6nvzJY3l+tBF1dSoNH7KfHQgCd3zqQuo46YNeUyo5I95II1wZda0TPa/YfpDsqOW8qnbMw1es+LFxdxPf4Tpo7oCmVKt5TvImHz3mvsc3I+lOuy6OG96ZcuktGjl+Dq5XSXctutyuPYlqfP07ztqSDoZtmUJVoN3Of9XnPz7miIA7qJCk10DAoQaxv6urcXN80zmuH9Kwn5rVc5/6p5ECaOaaKvL79bC2ri5fOQb9ocNptv84aSNsU5vWNkrpzhMdEcFqaN7SsDb/JzrYH3W8f0ivs5rua5e1r5wMTQgVBQHDtEPRkk9A4bQohefLkQtpEU4QrIcrr2RbeCqcWtxHypUwubt9cKNtqlnDxhdI2wsLHC96G9fuuf6q4jj3h1ILq4fZJHzLkHC2c+xi2nvX78UxY1iGNuL6pY8zbTJHC3DbfChe3DBOqu0vnylxIkayQMGTldeFzpG1YE3T73DGtuD39PgdcnCPUzWv++z1qDhEO3g1V2F5YCP5wSZjR3kPx//Uhbe7awoT114Q3542Oy7onits74p1e/DxV0pbC3oeRP1cOl4SJNVKF+059qDLimM3HTm0cChXuC9uG1RVyyr9NKfB57D7dT3j5ICyNlp/gZ7SNsGBNnAz+cFYYXT3st3vptmnq9wZ/uCmsHGp+P/WB97eb91HhxRflbVkT3l5ZLDQtYjmu7br+Kdz53/NAKe5ZTn8Rw52tbQzfozaveXVyXLjj5N5ghfBI0/yUgzXxVss4pl3+w+crhW490+crLHD6L59av90qwkK/T4rrWRfC4gOHP3NOFi4HK60XFp4cGGhYn8PUg+r34+urs8JkC/mdPujzvXe6f4y4HWuCI8ooDramS7X58tV1TcNti9PQY90HEdczF575zbG4j/rA+7rz8hvF7agP2pfNjsp3tTw21sexs8KIzNJ3K8ext8LWgVKdg0PKpI2Erf+Zzx8shScHBgm5FOqf5ratzTG6L8xrnlpcR12+dV9Y1j2vvH5ZYfZx5fXV1y+sCZ919fS2Qv5UkbcbFtyElmOOifmScdm46LSlup528dj4WsVUPSh8CMvT1ORBVufTG66bzKetTxscVJbX3qdV1Qk5fH11UOieXzkud1tx17CeNcf2a6Cu/t7BM9L2IgZxX3V1C0tlmbXlhPGxLT/htKDLRiKs80xY2z2sfOeg/hwoBy7/lc6L6Xzks+oygQNf//o9ibgNTj++Qp8CKcV10uXqIOyxeG2lq9vXkPIdPp6774QorOeYax8OatPQn4VaCmvOKeWb6uoh1/dbPrZSm8K9cNtTqud9CfBVne49ao4WzjyPvA1btuMXoLwddXmH2msatetJgcs+r6KW42zZVguFa2+Ut+GIOuKpBTXEa4aI24lY7lmTl1zfrS59imX8NfPltyPKpo/+3uHyHFPxV31Qm+6tizP6wOfdq6iUV5kKas+7TcezY1qz15UhX58J+2a3VtUeyeX7zEPKdV+1+2ZrHmX6eNty/ixf21qTDvjcKW1DH2y6JsgixRlr4pqpYF3ek8FCO4T16UB9uxHXuS21jbsJzQZtEx4GWVfnVhusKatXn32tsA3b8gl9sPY6a8clc9fp2rancjBbd15+R6HeGRVtDxz4t9cTcmn42xHCh6i5bQJcSHzK7zWHTl45QStm/kON//Kk9PIdfjw6ImthT2rSbjytPnyV9sxrRzkSyh9FkfwtltCehX2pVj799K6JKWeZZjRsUgOLo4AcLz7lqDqINhw/R9sWTKBW9YtQoSxh05XFSZSRilduRr0nLqOTV0/Q4AaZNL8T+4+cbWiV7zlaPrkd1S6SyXCX2o9p8lLJum1pytLjtGf9P1RCfpaNKTF/yk7tZ/rSqe0zqHuDIoY4EUNITQWKNqBuE5bRsSPrqEetzCQO4HKI7NRlyUGa3qchuaWTvoSPYcUmI6hv4/w2Hzv1cSgVVR60hg77LqfBzSob9oHXz1a4ErUZMJ12nj9BEzvmo1+kAYeasWbUXcyf/qT6g9fQiTN7aJbuvNcpnpeSG5UM6fN4iud+8rw9dOH2CZrUqSj9Hn4QnU3iZfWiRYfO0fqZfc3GtfKZos9zK3hkWzN5akBWs2FlB0ypZE281TKORU3+Y82zbtULmyqXmZsiV08/VS6LHdyGihZSH+9i/eZOXXX53cWj62jyoOZU3jMsPvO+cDlYrclgmr32MF08I+V71o2WcB5Hp8sMpetTtR/DfjynIWunY0qUr41uH6/QvvUzqK8u3keMp0WKSXF/o67e4bPhH6qQLfyUg9bTPm04Kt91/rGxRnwq33YhNSuaRNyXVpMG2/3s+CSlhtH2daOoUbmwuJohT30aMKMPlTTkh+HZf4zCP1+pwuBR1MzslNosFdXtKU1fxc8bHNtOOZ9zTB01rq6ePpuO/7uXZgwOnz8lzyTV0Tf7naR5fYpYnS9FVf3BFlbn0zUzaZxPqyyvO+VXXSfkKbzG7lir215pQz2b82qvXtOpXfmIj7FQJ1Y8Xf19hg/5H498nKT0IdXhj/pLdQvnl2WJqVzT/lRUw9ErSUoNpx3rrclH4kYqE8LOp3QO+Dh1nTiP9vvdFq9/8yaVPzTg54R2oWUPeTRE+OeEmsJ1+/aDpVFJITF3ic8PjTx1mmOufVhYGlpOIzs2oMJ5U8ufGNcRDtO5Y/OpTh7by5aMpQfR+sPHacHI5lQub1g85vyK0+jcreflNgXL9ZDYiTzCpfsKCnlUk3YjxW36bOhL7iam7Ld2O3kTyR9EI1z2LTx4hfYunxgpzkr7r2+vaU6ZTJw+R9QR87dYTLs1Lvcyltelz1PK6ZN/qz4eHeQyPrNt9Vl7/JCjGg1qmtmoDLKX49I94/O+8ODZaHvNHiN2Yirddi6duHjY0B4ZMU/m/RwzfSud8VtH7UrYV/fVMo+SOOb8WZMOolN7i5Jvp92I69xzxLjI8SNinbua7pyu8TlCC4dVoeRaNKQosKasruuuwRSDEVh7nVUxu7n0qH17qtm6c4W0ijM6RM31Nf/21XQoCtqSvxcxuEdUfg0A35UAWtGpIPXZ9IXifP2bdj7tSlk079gBcCWXaXLNijTxVAj9mLAzrTvVl9yi/CYKAAAAbRybnoEajP5MMQUPmumzNhpOmQYgOeadnxqMCaaR286RlzviKQB8W74+3UwtC3ei/yrNoa3elSkh2mEAAACcAkUuAACACm98N9Fi8ZmARB5tKqMjFAAAXMhl8tv9VXwVMyQHpRSfswkQ/Xx9uouWLn1CCbK0pSJuiKcA8K0JovObFtPBLwJVrlgEHaEAAABOhGIXAADAoge0YcE8einoCs7QklSnUg75fQAAgOgoiA7MmEaX38mLFtzYNoeWXJBu+EnjVYQyRu9Z0+A7JQTfp7XjBtGux4moWe8GlAGtGQDwjXl+Zi79M8aPfs89iOpV0mJKRQAAAFALlw8AAAAW3Ng2nqbtlZ4Rl71Jc5PPowMAAIgO3viOp55jxlK9+v1p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      ],
      "metadata": {
        "id": "XysxrFB1-hbQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Installing transformers and loading up BERT Sequence Classification model"
      ],
      "metadata": {
        "id": "CVuMugqAOSuk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSY8tMVX-eHQ",
        "outputId": "6613c32d-aba0-44c0-db9e-2f3210920082"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 5.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 33.6 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 32.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.10.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
            "\u001b[K     |████████████████████████████████| 67 kB 3.3 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 34.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.4.0 pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.10.3 transformers-4.15.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "\n",
        "model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
          "referenced_widgets": [
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        },
        "id": "DDdFVf7nMB-3",
        "outputId": "c13ef871-c0bb-401b-a7a0-7f489ea561c7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "5110cf57eeda4ca4bfd46002157d5115",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2
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              "Downloading:   0%|          | 0.00/511M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
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            ]
          },
          "metadata": {}
        },
        {
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          "data": {
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              "model_id": "43ef7988730f4435a470edb497e35308",
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              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "720e8914d9114dcca99b65843756e2a1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jyMHmfPMJjD",
        "outputId": "e3e3d179-9254-48a6-e0d2-ea99f23ce30d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"tf_bert_for_sequence_classification\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bert (TFBertMainLayer)      multiple                  109482240 \n",
            "                                                                 \n",
            " dropout_37 (Dropout)        multiple                  0         \n",
            "                                                                 \n",
            " classifier (Dense)          multiple                  1538      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 109,483,778\n",
            "Trainable params: 109,483,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading data (IMDB Dataset)"
      ],
      "metadata": {
        "id": "eCchWqCUOZSn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "\n",
        "URL = \"https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\"\n",
        "\n",
        "dataset = tf.keras.utils.get_file(fname=\"aclImdb_v1.tar.gz\", \n",
        "                                  origin=URL,\n",
        "                                  untar=True,\n",
        "                                  cache_dir='.',\n",
        "                                  cache_subdir='')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PeDzJs0_MMvu",
        "outputId": "295ca11a-822b-4c0e-82f5-72c199aa75ad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
            "84131840/84125825 [==============================] - 3s 0us/step\n",
            "84140032/84125825 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "main_dir = os.path.join(os.path.dirname(dataset), 'aclImdb')\n",
        "train_dir = os.path.join(main_dir, 'train')\n",
        "remove_dir = os.path.join(train_dir, 'unsup')\n",
        "shutil.rmtree(remove_dir)\n",
        "print(os.listdir(train_dir))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wqit_PMhMW2w",
        "outputId": "d267efc9-1729-4b29-901d-3f1fc247dd43"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['urls_neg.txt', 'pos', 'labeledBow.feat', 'urls_unsup.txt', 'unsupBow.feat', 'neg', 'urls_pos.txt']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train/Test split"
      ],
      "metadata": {
        "id": "Dka3A8_YOdc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "    'aclImdb/train', batch_size=30000, validation_split=0.2, \n",
        "    subset='training', seed=123)\n",
        "test = tf.keras.preprocessing.text_dataset_from_directory(\n",
        "    'aclImdb/train', batch_size=30000, validation_split=0.2, \n",
        "    subset='validation', seed=123)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czZaRN7JMaUW",
        "outputId": "dcaddd22-8b23-456a-fd4e-a0a995c4bbc2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 25000 files belonging to 2 classes.\n",
            "Using 20000 files for training.\n",
            "Found 25000 files belonging to 2 classes.\n",
            "Using 5000 files for validation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in train.take(1):\n",
        "  train_feat = i[0].numpy()\n",
        "  train_lab = i[1].numpy()\n",
        "\n",
        "train = pd.DataFrame([train_feat, train_lab]).T\n",
        "train.columns = ['DATA_COLUMN', 'LABEL_COLUMN']\n",
        "train['DATA_COLUMN'] = train['DATA_COLUMN'].str.decode(\"utf-8\")\n",
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "id": "3k1aLyECNJ7P",
        "outputId": "653538bb-212c-460b-c2f7-9e8c15146152"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "                                         DATA_COLUMN LABEL_COLUMN\n",
              "0  Canadian director Vincenzo Natali took the art...            1\n",
              "1  I gave this film 10 not because it is a superb...            1\n",
              "2  I admit to being somewhat jaded about the movi...            1\n",
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              "4  A truly frightening film. Feels as if it were ...            0"
            ]
          },
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        }
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        "for j in test.take(1):\n",
        "  test_feat = j[0].numpy()\n",
        "  test_lab = j[1].numpy()\n",
        "\n",
        "test = pd.DataFrame([test_feat, test_lab]).T\n",
        "test.columns = ['DATA_COLUMN', 'LABEL_COLUMN']\n",
        "test['DATA_COLUMN'] = test['DATA_COLUMN'].str.decode(\"utf-8\")\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iwl9FP8dZYkK",
        "outputId": "76dd3d4b-9f85-4fb9-e401-84d89e2492c2"
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      "execution_count": 8,
      "outputs": [
        {
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "                                         DATA_COLUMN LABEL_COLUMN\n",
              "0  I can't believe that so much talent can be was...            0\n",
              "1  This movie blows - let's get that straight rig...            0\n",
              "2  The saddest thing about this \"tribute\" is that...            0\n",
              "3  I'm only rating this film as a 3 out of pity b...            0\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.iloc[1]['DATA_COLUMN']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "hawYG577Nnft",
        "outputId": "4580023d-fdcb-4ab5-d463-e25f133bfd90"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"I gave this film 10 not because it is a superbly consistent movie, but for it's pure ability to evoke emotions in its audience. The story of one-woman's-struggle-against-all-odds is an old cliché by now, but very few films have carried it off with so much warmth and sincerity as The Color Purple.<br /><br />It also showed a different side to the African-American experience - showing that after slaves were granted freedom many fell into the ways of the hated 'white man' and were abusive of their own people. I find this an important point as it goes against the portray-white-on-black-violence-and-win-an-Oscar trend.<br /><br />Also the acting performances are superb - especially Oprah who I now have a new found respect for.<br /><br />Well worth watching - but keep some tissue handy.\""
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.iloc[1]['DATA_COLUMN']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "VxaYQ0GQOiRB",
        "outputId": "9ad40ddd-021d-4692-f310-dfe62667961f"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'This movie blows - let\\'s get that straight right now. There are a few scene gems nestled inside this pile of crap but none can redeem the limp plot. Colin Farrel looks like Brad Pitt in \"12 Monkeys\" and acts in a similar manner. I normally hate Colin because he is a fairy in general but he\\'s OK in this movie. There were two plot lines in this movie-= one about a kid who throws rocks through windshields of moving vehicles and the other about a woman with a moustache. Let\\'s face it- this movie has no freaking idea of what it wanted to say or where it wanted to go. THe characters story lines intertwine on some levels but are in no means worthy of being included in a script. The whole thing is weak and pointless and then there is an occasional OK scene. But overall- Don\\'t bother unless you love irish accents so much that you can watch mediocrity and it is rescued by everyone sounding like the Lucky Charms elf -an American fetish that has catapulted some truly crappy movies to success.'"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN): \n",
        "  train_InputExamples = train.apply(lambda x: InputExample(guid=None, text_a = x[DATA_COLUMN], text_b = None, label = x[LABEL_COLUMN]), axis = 1)\n",
        "  validation_InputExamples = test.apply(lambda x: InputExample(guid=None, text_a = x[DATA_COLUMN], text_b = None, label = x[LABEL_COLUMN]), axis = 1)\n",
        "  \n",
        "  return train_InputExamples, validation_InputExamples\n",
        "  \n",
        "def convert_examples_to_tf_dataset(examples, tokenizer, max_length=128):\n",
        "    features = [] # -> will hold InputFeatures to be converted later\n",
        "\n",
        "    for e in examples:\n",
        "        # Documentation is really strong for this method, so please take a look at it\n",
        "        input_dict = tokenizer.encode_plus(\n",
        "            e.text_a,\n",
        "            add_special_tokens=True,\n",
        "            max_length=max_length, # truncates if len(s) > max_length\n",
        "            return_token_type_ids=True,\n",
        "            return_attention_mask=True,\n",
        "            pad_to_max_length=True, # pads to the right by default # CHECK THIS for pad_to_max_length\n",
        "            truncation=True\n",
        "        )\n",
        "\n",
        "        input_ids, token_type_ids, attention_mask = (input_dict[\"input_ids\"],\n",
        "            input_dict[\"token_type_ids\"], input_dict['attention_mask'])\n",
        "\n",
        "        features.append(\n",
        "            InputFeatures(\n",
        "                input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, label=e.label\n",
        "            )\n",
        "        )\n",
        "\n",
        "    def gen():\n",
        "        for f in features:\n",
        "            yield (\n",
        "                {\n",
        "                    \"input_ids\": f.input_ids,\n",
        "                    \"attention_mask\": f.attention_mask,\n",
        "                    \"token_type_ids\": f.token_type_ids,\n",
        "                },\n",
        "                f.label,\n",
        "            )\n",
        "\n",
        "    return tf.data.Dataset.from_generator(\n",
        "        gen,\n",
        "        ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n",
        "        (\n",
        "            {\n",
        "                \"input_ids\": tf.TensorShape([None]),\n",
        "                \"attention_mask\": tf.TensorShape([None]),\n",
        "                \"token_type_ids\": tf.TensorShape([None]),\n",
        "            },\n",
        "            tf.TensorShape([]),\n",
        "        ),\n",
        "    )\n",
        "\n",
        "\n",
        "DATA_COLUMN = 'DATA_COLUMN'\n",
        "LABEL_COLUMN = 'LABEL_COLUMN'"
      ],
      "metadata": {
        "id": "T1IWQdTYUyuK"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_InputExamples, validation_InputExamples = convert_data_to_examples(train, test, DATA_COLUMN, LABEL_COLUMN)\n",
        "\n",
        "train_data = convert_examples_to_tf_dataset(list(train_InputExamples), tokenizer)\n",
        "train_data = train_data.shuffle(100).batch(32).repeat(2)\n",
        "\n",
        "validation_data = convert_examples_to_tf_dataset(list(validation_InputExamples), tokenizer)\n",
        "validation_data = validation_data.batch(32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9zn1Vv8U3to",
        "outputId": "d7a0105d-0293-47e3-ffbc-5cc79e65e226"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2232: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "uEwgf9i6ONzN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0), \n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\n",
        "\n",
        "model.fit(train_data, epochs=1, validation_data=validation_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YoivHFyZT7r",
        "outputId": "2ea31e39-d32b-4291-ee8d-ae2f94fd1313"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1250/1250 [==============================] - 2072s 2s/step - loss: 0.2686 - accuracy: 0.8850 - val_loss: 0.3415 - val_accuracy: 0.8830\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f2a60278910>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the model"
      ],
      "metadata": {
        "id": "gg5EomTTOPLp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights('model_weights', save_format='tf')"
      ],
      "metadata": {
        "id": "3EGRZqn1kIue"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Downloading weights and loading up a model."
      ],
      "metadata": {
        "id": "gWIIhXeVN7fF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1oelRnAixYd0ol3C1zupmmIAOEqANPud0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWqY_r5xNllZ",
        "outputId": "4d7d9b2b-b98e-4b9a-ed36-309ab6f573e6"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1oelRnAixYd0ol3C1zupmmIAOEqANPud0\n",
            "To: /content/model_weights.data-00000-of-00001\n",
            "100% 1.31G/1.31G [00:17<00:00, 75.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1fFwX6ISCZhXjyFxjei-RVYnlJ6LxBw5Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGAXYnYTMC-o",
        "outputId": "affaf8e3-4a85-4638-8544-6acb655837dc"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1fFwX6ISCZhXjyFxjei-RVYnlJ6LxBw5Y\n",
            "To: /content/model_weights.index\n",
            "\r  0% 0.00/44.1k [00:00<?, ?B/s]\r100% 44.1k/44.1k [00:00<00:00, 41.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "\n",
        "load_model = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\")\n",
        "# tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "load_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0), \n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
        "              metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\n",
        "load_model.load_weights('model_weights')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CikbD-1MnOw-",
        "outputId": "94e18893-8dc6-4ac4-cbcc-ddf64d0291fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f29e9848690>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting based on sentences."
      ],
      "metadata": {
        "id": "Biohf06xN_R9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_sentences = ['This was an awesome movie. I watch it twice my time watching this beautiful movie if I have known it was this good',\n",
        "                  'One of the worst movies of all time. I cannot believe I wasted two hours of my life for this movie',\n",
        "                  'It was interesting at first, main character was beautiful until it got bored and I couldn\\'t watch it no more']"
      ],
      "metadata": {
        "id": "rUGjJvsYIk-a"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(sequences):\n",
        "  tf_batch = tokenizer(sequences, max_length=128, padding=True, truncation=True, return_tensors='tf')\n",
        "  tf_outputs = load_model(tf_batch)\n",
        "  tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)\n",
        "  labels = ['Negative','Positive']\n",
        "  label = tf.argmax(tf_predictions, axis=1)\n",
        "  label = label.numpy()\n",
        "  for i in range(len(sequences)):\n",
        "    print(sequences[i], \": \\n\", labels[label[i]])"
      ],
      "metadata": {
        "id": "FdExd6C5Hjx6"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict(pred_sentences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxp_-xWOKyxs",
        "outputId": "afa13573-f1fe-436f-eed7-e2dc144df23b"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This was an awesome movie. I watch it twice my time watching this beautiful movie if I have known it was this good : \n",
            " Positive\n",
            "One of the worst movies of all time. I cannot believe I wasted two hours of my life for this movie : \n",
            " Negative\n",
            "It was interesting at first, main character was beautiful until it got bored and I couldn't watch it no more : \n",
            " Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(['hey how are you doing'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZcQmOmfPHPC",
        "outputId": "78e5ac75-1e4f-4fa7-b615-2e7f099b194e"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hey how are you doing : \n",
            " Positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict(['lets talk later'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JRG85BK-PPsA",
        "outputId": "4e1a4dd9-cc4c-4e2c-931b-5767132d7349"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "lets talk later : \n",
            " Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Predicting sentiment analysis problem of news using api"
      ],
      "metadata": {
        "id": "XkMejPiQRRBz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### https://newsdata.io/"
      ],
      "metadata": {
        "id": "8W_NjQP-RWDo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "api_key = 'pub_4061d3b8d6397e4057f769ca86556ad262cf' # gg\n",
        "topic = 'stock market'\n",
        "\n",
        "import requests\n",
        "\n",
        "req = requests.get(f'https://newsdata.io/api/1/news?apikey={api_key}&q={topic}')\n",
        "req"
      ],
      "metadata": {
        "id": "XT2IlPyyPSNn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "req.json()"
      ],
      "metadata": {
        "id": "5WQGb2jxQTGu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "contents = [res['content'] for res in req.json()['results'] if res['content']]\n",
        "contents"
      ],
      "metadata": {
        "id": "QVemOULcQUZN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict(contents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQ-bTrKUQXAu",
        "outputId": "91b8f3d1-ef86-4254-d831-a9371f8768fa"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "If you were lucky enough to avoid any investment losses in a bullish 2021, then 2022 has changed your luck. The S&P 500 (SNPINDEX: ^GSPC) is now down more than 9% year-to-date, a move that played out without any warning. And it may well be moving even lower before all is said and done. It's an uncomfortable outcome for veteran investors, while newcomers to the market may be feeling a mix of horror and shellshock.If that's mentally where you are at this point, don't sweat it. Market corrections and the occasional soured trade are nothing unusual, and certainly no reason to give up on investing. However, this may be the right time to suggest changing your tack. Exchange-traded funds (or ETFs) can help curb some of the volatility inherent to owning individual stocks, and these three ETFs in particular can offer even more stability.It's a painfully obvious option, but the Invesco S&P 500 Low Volatility ETF (NYSEMKT: SPLV) will indeed limit the severity of the swings that might otherwise shake you out of a stock, or shake you out of the market altogether.Continue reading : \n",
            " Negative\n",
            "After being one of the best-performing industries of 2020, renewables have since struggled amid supply chain issues, uncertainty regarding the future of solar subsidies and tax credits, and rising interest rates and inflation.One of the few renewable energy stocks that had been performing well was SolarEdge Technologies (NASDAQ: SEDG). After reporting its Q3 2021 earnings on Nov. 2 2021, shares of SolarEdge proceeded to reach a new intraday all-time high of $389.71 on Nov. 22, 2021. But in just two months, the stock has plummeted over 45% and is currently trading around a 52-week low.Here's why shares of SolarEdge are too good of a deal to pass up, even if the U.S stock market keeps falling.Continue reading : \n",
            " Negative\n",
            "The stock market seemed to get itself back on a more even keel on Thursday morning, seeking to reach some level of stability following massive movements earlier in the week and throughout the month of January. As of 8:15 a.m. ET, futures on the Nasdaq Composite (NASDAQINDEX: ^IXIC) were up a bit more than half a percent, leading other major market benchmarks higher.This has been a busy week for companies releasing earnings, and a couple of Nasdaq-listed stocks made big moves higher as they told investors about how the last few months have gone. Two of the biggest gainers were Xilinx (NASDAQ: XLNX) and Seagate Technology (NASDAQ: STX), both of which have strong ties to the tech industry. Below, you'll find out what each of those two companies said and why shareholders are celebrating.Shares of Xilinx were up more than 6% in premarket trading Thursday morning. The maker of field programmable gate arrays and other semiconductor chips enjoyed strong results and also had good news on its potential acquisition.Continue reading : \n",
            " Positive\n",
            "In the current market sell-off, tech stocks have been taking a beating. The Federal Reserve is poised to raise interest rates several times this year, which is taking a toll on the stock market. An old saying in the stock market is \"don't fight the Fed,\" which means investors should be careful when the Federal Reserve is raising rates.In a rising-rate environment, growth stocks generally suffer since most of their value is based on future expectations, and rising rates undercut the present value of those expectations. Here is one growth stock that has seen a correction, though its long-term story remains intact. Image source: Getty Images.Continue reading : \n",
            " Positive\n",
            "It's easy to invest in the stock market when it's consistently moving higher, and for the most part, that's been the case since the pandemic market bottom in March 2020. But the market has been especially volatile since November 2021, particularly in the technology sector.Many individual tech stocks have collapsed by 50% or more, plunging them deep into bear-market territory. But there's a handful of high-quality names that consistently outperform and two that are especially worth buying when uncertainty takes hold.Image source: Getty Images.Continue reading : \n",
            " Positive\n",
            "So far, 2022 has not been friendly to the stock market. Pricey growth stocks have been crashing for months, and now the major indexes are starting to drop. This isn't a market crash yet, but there's a lot of pandemic-era excess that needs to be wrung out. Something like this was bound to happen sooner or later.For long-term investors, a market crash is nothing more than a buying opportunity. It's always good to have a watch list of stocks you'd like to buy at lower prices. Three stocks that stand out to me right now are Boston Beer (NYSE: SAM), Cloudflare (NYSE: NET), and Best Buy (NYSE: BBY).Image source: Getty Images.Continue reading : \n",
            " Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic = 'economy'\n",
        "req = requests.get(f'https://newsdata.io/api/1/news?apikey={api_key}&q={topic}&page=4')\n",
        "contents = [res['content'] for res in req.json()['results'] if res['content']]\n",
        "contents"
      ],
      "metadata": {
        "id": "4kgok-5hRNhb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict(contents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "azE06pggSmBV",
        "outputId": "a72eb137-f228-4002-e164-678116052ffc"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As we near the end of the first month of a new year, the stock market is experiencing a lot of turmoil. 2021 was a huge recovery year after the 2020 crash, and the market ended on a high, with the S&P 500 up 26.9%. Those gains have not been repeated -- so far -- in 2022, and the S&P 500 is down about 7% as of midday on Jan. 26.There are a number of reasons for investors to worry. High on the list is the omicron coronavirus variant, which is still wending its way through the world and shutting down many parts of the economy. Another factor is premium valuations on growth stocks, whose prices are falling more in line with their real growth prospects. Their prices are also falling on concerns about the Federal Reserve raising interest rates this year to combat inflation. Higher rates affect growth-focused companies' ability to raise cheap capital to fund their expansion.Image source: The Motley Fool.Continue reading : \n",
            " Positive\n",
            "It was a dismal year for the British auto industry, with the worst production numbers since 1956 Fewer than 860,000 vehicles rolled off British production lines last year, the lowest level in 65 years, as factories slowed down or stopped work due to a severe shortage of semiconductors. Other factors affecting production included widespread staff absences as workers were forced to go into isolation, the Society of Motor Manufacturers and Traders (SMTT) said. According to the SMTT, total car production was 6.7% lower than in 2020 and 34% below pre-pandemic levels. Covid-19 disruptions triggered a global shortage of semiconductor chips, leading to an even worse 2021, it said. Semiconductors are a vital part of modern cars, with each vehicle typically having between 1,500 and 3,000 chips to operate. The trade group’s chief executive Mike Hawes described 2021 as “a dismal year,” adding that “there's no hiding it.” However, he suggested that despite the miserable year there is optimism, largely because of almost £5 billion in planned new investments by the automotive industry, many of them in electric vehicles or technology.  READ MORE: Ford to become first US automaker to mandate vaccines for 30,000+ workers Hawes said the industry had managed to cope with the extra costs of Brexit, but warned that it still faces a growing challenge from a spike in energy costs of up to 70%. According to him, the industry urgently needed “measures to mitigate the escalating energy costs which are threatening viability,” because higher costs “will flow through to prices,” adding to pressure on consumers. For more stories on economy & finance visit RT's business section : \n",
            " Negative\n",
            "KANGAR, Jan 27 — The development of the Perlis Inland Port (PIP) coupled with Perlis’ position near the Thai border is expected to facilitate increased cross-border trade between Malaysia and Thailand, said the Northern Corridor Economic Region (NCER).In a statement today, NCER said the Northern Corridor Implementation Authority’s (NCIA) development of the port will directly catalyse the transition of Perlis from an agronomic state to a border state economy and accelerate socio-economic recovery in the northern state.The PIP is a public-private partnership project that will be the anchor logistic hub for the integrated development in Chuping Valley and cargoes from southern Thailand, further strengthening the Chuping Valley Industrial Area (CVIA), NCER AgriBio Economic Zone (NAEZ), and Chuping Agro Valley (CAV) projects.“NCIA will develop the external infrastructure (bonded road, railway spur line and flyover) while the development of the port itself will be undertaken by private investors.“The private investors are planning to develop the PIP in three phases, whereby the first phase will comprise of a rail park (three rail sidings, reach stackers and open yards) and logistic park (pack-houses, warehouses, storages and silos),” said the statement.It added that the construction of the port will start in February 2022 and full operations will commence by 2024, while the logistics park is expected to handle a total capacity of 250,000 twenty-foot equivalent units (TEUs) once Phase 1 is completed.“The Perlis state government is working closely with NCIA to expedite the development of this project.“The PIP is well-positioned to strengthen and further safeguard the competitiveness of Malaysia’s northern trade border in anticipation of other developments in the region,” said Perlis Menteri Besar Datuk Seri Azlan Man after chairing the first State Steering Committee meeting for 2022, here, today.The Menteri Besar also said the development of the port is expected to place Perlis on the right track towards strong overall growth in 2022.NCIA chief executive Datuk Seri Jebasingam Issace John also noted the importance for the NCER to strengthen its border town logistics infrastructure and complementing services.“The development of the PIP and the upgrading of Penang Port must be carried out as planned to leverage on first-mover advantage,” he said.According to the statement, the CVIA, which is also strategically located near the Thai border, will leverage the regional cooperation of the Indonesia-Malaysia-Thailand Growth Triangle (IMT-GT) and boost the development of the area with various commercial activities that are innovation-based, knowledge-intensive investments within the high-growth and high-value sectors.“To date, with the industry ecosystem development and integration already in place in CVIA, more than RM2.5 billion in committed investments have been achieved and 1,870 job opportunities created.“A further RM449 million in investments and 850 jobs are in the pipeline from domestic and foreign innovation-based and knowledge-intensive anchor investors,” said NCER.The construction of CVIA’s Phase 1 development is on track with 100 per cent sectional completion targeted by October 2022, it added. — Bernama : \n",
            " Positive\n",
            "ShutterstockScott Morrison will announce $1 billion over nine years for ground and water programs for the Great Barrier Reef when he visits the area on Friday. The Prime Minister said the $1 billion would extend the federal government’s investment under the Reef 2050 Plan to more than $3 billion. “Funding will support scientists, farmers and traditional owners, backing in very latest marine science while building resilience and reducing threats from pollution in our oceans and predators such as the Crown of Thorns starfish,” he said. “We are backing the health of the reef and the economic future of tourism operators, hospitality providers and Queensland communities that are at the heart of the reef economy.” Read more: 5 major heatwaves in 30 years have turned the Great Barrier Reef into a bleached checkerboard Last year the government successfully fought off an international move to have the reef placed on the list of “in danger” world heritage sites because of the threat climate change posed. The reef has suffered repeated bleaching. After an intense lobbying campaign by Australia the World Heritage Committee decided instead “to observe further developments in the coral reef off the Australian coast closely”. Australia was asked to submit a progress report on planned protective measures. The report is due next month. The timing of the package’s announcement is in the context of Australia’s continuing attempt to convince the committee it is making every effort to preserve the reef. The committee has ordered a scientific inspection of the reef, although no date has been set. Read more: Is Australia really doing enough for the Great Barrier Reef? Why criticisms of UNESCO’s 'in danger' recommendation don’t stack up The World Heritage Committee has ordered a scientific inspection of the reef. Shutterstock The package comprises: $579.9 million for water quality – working with land managers to remediate erosion, improve land condition and reduce nutrient and pesticide runoff $252.9 million for reef management and conservation, including reducing threats from Crown of Thorns Starfish, implementing advanced health monitoring systems and the preventing illegal fishing $92.7 million to research and use world leading reef resilience science and adaptation strategies $74.4 million for traditional owner and community-led projects including species protection, habitat restoration, citizen science programs and marine debris. The Great Barrier Reef is in the Liberal seat of Leichhardt. The seat is marginal although its member, Warren Entsch, has enjoyed a strong personal following. Read more: Not declaring the Great Barrier Reef as 'in danger' only postpones the inevitable Michelle Grattan does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. : \n",
            " Positive\n",
            "This month about as many people have died with COVID in Australia – more than 1,000 – as die in the whole of a bad year from influenza. “Because of extraordinarily high virus transmission, we’re getting more deaths now in this ‘enlightenment’ COVID time than in the ‘dark ages’ time,” says Brendan Crabb, research scientist and director of the Burnet Institute. “On a seven-day average, we are now seeing more than 60 deaths every day, with no sign yet of a decline. Lessons from the UK and US are that without effective controls we may settle on a high baseline toll beyond the Omicron peak that is not much below this.” Yet as deaths suddenly spiked in the last few weeks, attention on them doesn’t seem to have spiked proportionately. The pandemic news of January has been been dominated by the shortage of RATs, supply-chain problems, and pressures on the health system. We have not learned to live with the disruption of Omicron, or as yet to strike the most effective response to it, but we are not, it seems, traumatised by its current death toll. At least, not most of the country. This week Western Australian Premier Mark McGowan justified delaying his plan to open the state border on February 5 by highlighting the deaths elsewhere. “As you’re seeing over east, huge numbers of people are dying,” he said. He’s copped plenty of criticism for his turnaround. In COVID’s early days in Australia, deaths were very much front and centre in public attention. Now, despite a highly vaccinated population and a less lethal variant of the virus, the substantial death numbers are higher than the narrative of late 2021 led us to anticipate. However, they aren’t leaving such a deep imprint on the public consciousness. The earlier dire warnings to the unvaccinated about the risks they ran were absolutely accurate. But the statistics are also showing that many of those dying with COVID were vaccinated. Clearly vaccination is not the be-all-and-end-all it might have sounded. Just as in driving your car, the seat belt offers protection but is not a guarantee of survival in a crash. Anyway, the authorities have quickly changed their messaging from the importance of being doubly vaxxed to the necessity of that booster. Crabb says vaccines are “brilliant” and the most important thing people can do is to have three doses. “But it doesn’t make you bullet-proof, nor protect those around you. Any effective strategy has to be more than vaccines.” On various fronts, Australia’s journey through Omicron has not been managed effectively, which reinforces the already strong argument for a royal commission into how the pandemic in general has been handled. Anthony Albanese this week took his “small target” strategy to the extreme when, appearing at the National Press Club, he was asked whether if he becomes prime minister he would set up a royal commission. The Labor leader declined to commit. There’d need to be an “assessment” of what had been done, he said. He conceded Labor had considered a royal commission, but the matter hadn’t been though its “process”. Albanese mightn’t want to take attention off the RATs shortage and other issues by promising a royal commission. Or perhaps he fears advocating it would open him to Scott Morrison’s again accusing him of playing politics. It could be cast by critics as tit-for-tat for the Abbott government’s royal commissions into its Labor predecessor, notably into the “pink batts” scheme which resulted in many house fires and several deaths. But the case for a commission is overwhelming, especially to inform us about what needs to be done to ready for pandemics in the future. It is needed to investigate all fronts: health, economics and governance. Especially in the early stages of the pandemic, everyone welcomed “health advice” being followed by governments. But the term “health advice” concealed some sharp differences that occurred among experts. These continue today, as we grapple with Omicron. A royal commission would enable better understanding of the debates that have gone on, and go on, within the health establishment, including at the Australian Health Protection Principal Committee (AHPPC), the group of federal and state advisers to the national cabinet. Mostly, differences within that group have been mired in secrecy. Unreadiness and slow reactions have led to some of the serious problems in handling COVID. These have included the initial slow rollout of the vaccines, and latterly the tardiness in obtaining RATs. Political failures account for part of these problems, but what about the bureaucracy? The pink batts royal commission identified faults in the Environment Department. Has the federal Health Department been found wanting in this crisis and what changes are needed? And what about the responses of the state health systems? In general the Australian economy has held up well during the pandemic, and that has been substantially due to government programs. At the same time, some funds have been wasted and an outside investigation would be instructive on how things could be done better if circumstances repeated themselves. A detailed probe into the supply-chain issues could lead to more belts and braces for the future. The debate about governance has been fraught during COVID, which exposed just how much power the states have in the federation. Morrison’s innovation of the “national cabinet” has run side by side with premiers taking their own courses on matters from schools to borders. Critics of the states would have liked to see Morrison able to exercise near-total authority. Conversely, many of Morrison’s critics believe tough premiers were what held the deaths in check for so long. How the federation has operated during COVID and what adaptations are needed would be major issues for a royal commission. And then there’s Australia’s international response. Much attention was on the government’s call for an inquiry into the pandemic’s origins, but what about Australia’s role in a co-ordinated global response? Like Albanese, Morrison’s line is that the time to talk about “reviews or whatever” is not now. But calls for a royal commission are coming from across the political spectrum, ranging from backbench Labor through the crossbench to some on the right in the Coalition, although motives vary and there are differences about timing. Some of those against such an inquiry point to the drawn-out nature of royal commissions. But the investigation would not divert from current efforts and could be staged, reporting progressively. To the argument “not another royal commission”, the answer is that such comprehensive investigations look under the rocks, produce a wealth of information, and concentrate the attention of policymakers. Who would say the royal commissions into banking and aged care were not worth the effort? A royal commission would not take away from Australia’s undoubted successes in dealing with COVID. Rather, it would be a form of insurance, putting us in the best position to confront a possible Mark 2 pandemic. Michelle Grattan does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment. : \n",
            " Positive\n",
            "Microsoft India has announced a new skilling initiative that is aimed at helping small and medium businesses (SMBs). The new Microsoft program will provide digital skills to SMBs working in India. The new initiative has been developed considering that SMBs contribute to around 30 percent of India’s GDP and provide employment to over 114 million people. According to Microsoft, these new digital tools will allow small businesses to respond to changes in the market, streamline their organizations, protect against threats and plan for long-term business resiliency. Harish Vellat, Country Head – Small, Medium & Corporate Business, Microsoft India, said, “SMBs have been at the forefront of our country’s economic rebuilding following the pandemic and are facing immense pressure to keep up with the dramatic shift in how we work and live. It is critical that they are equipped with the skills that can help them develop long-term strategies for their business and reimagine new paths for their success. By providing these skill-building resources, we are fulfilling our commitment to creating an inclusive economy and helping SMBs transform and thrive.” What kind of courses will Microsoft provide? Under the new initiative, Microsoft aims to provide flexible skill-building approach via e-learning and online training assets from sources like Microsoft Learn and LinkedIn Learning. Through the resource hub, businesses can explore expertise, technical training, online workshops, and best practices, as well as earn Microsoft certifications on technical courses. The courses will offer digital skills to use technology for customer relationship management (CRM), digital marketing, advertising and branding. Businesses can learn how to use tools that work together to help them keep departments connected, lower overhead costs and maximize efficiency. The employees can be educated about best practices that can help them protect customers’ and employees’ data. In avoiding breaches, these businesses can avoid the associated high costs and reputation loss. Businesses will be able to analyze available data, understand trends and move more quickly using some of the new courses. The initiative will also provide foundational principles in finance, marketing, bootstrapping, selling. The post Microsoft launches new skilling courses for small and medium businesses appeared first on BGR India. : \n",
            " Positive\n",
            "A general view of the city is seen in Yangon in this file picture taken on September 6, 2013. — Reuters pic Follow us on Instagram and subscribe to our Telegram channel for the latest updates. YANGON, Jan 27 — Myanmar has approved US$3.8 billion (RM10.5 billion) in foreign investment since a coup a year ago, its military government said today, owing to what it called a return to stability and confidence in its economic potential.It did not disclose details of the investments, but said the projects approved included US$2.5 billion in a liquefied natural gas power plant. That figure accounted for two-thirds of the overall amount cited.China was the top investor, it said, without disclosing the amount. Myanmar received US$516.4 million from one Japanese firm, a combined US$442.2 million from four Singaporean investments, US$75.5 million from two Thai ventures and US$66.1 million from five South Korean businesses, it said.Myanmar’s economy has nosedived since the army overthrew an elected government and launched a bloody crackdown on opponents, with a struggle to impose order amid widespread civil unrest and armed resistance from pro-democracy militias and ethnic minority rebels.Instability, uncertainty, Western sanctions and concerns about reputation damage have led to a corporate retreat from Myanmar, most notably from its energy sector, historically a lifeline for the military during decades of trade restrictions.Australia’s Woodside Petroleum today joined multinational oil majors Chevron Corp and TotalEnergies in announcing exit plans.The United States yesterday issued a business advisory warning of heightened risks in doing business in Myanmar, especially when the military is involved. The military runs a network of its own businesses dealing in lucrative areas like energy, gemstones mining and teakwood.A joint statement today from the junta’s investment and information ministers said Myanmar had endured economic sabotage by foreign-backed groups and had “largely restored national stability by the second half of 2021.”While some countries discourage doing business with Myanmar “many of our foreign partners choose to work quietly with us, fully recognising Myanmar’s economic potential as well as its unique challenges,” it said.In the 2019-2020 and 2020-2021 fiscal years, Myanmar’s foreign investment fell from US$4.9 billion to US$3.8 billion, data from Myanmar’s Directorate of Investment and Company Administration showed. — Reuters : \n",
            " Positive\n",
            "Singapore will host the Formula One night race for another seven years from 2022 to 2028, organisers said January 27, 2022, after the coronavirus pandemic scuppered the event for two years running. — AFP pic Follow us on Instagram and subscribe to our Telegram channel for the latest updates. SINGAPORE, Jan 27 — Singapore will host the Formula One night race for another seven years from 2022 to 2028, organisers said today, after the coronavirus pandemic scuppered the event for two years running.The street race around the city’s Marina Bay financial district was held 12 times since its debut in 2008 and saw 268,000 spectators in attendance when it was last held in 2019.But the pandemic forced the event out of the calendar in 2020 and 2021 as authorities imposed tough curbs to deal with the virus.Singapore was the first night race in the Formula One calendar, offering a street circuit that runs through the heart of the modern city-state.Organisers said this is the fourth contract renewal for Singapore and the seven-year extension is the longest for the race so far.“This will give Singapore a longer runway to capture pent-up demand as international travel recovers,” Singapore GP and the Singapore Tourism Board said in a joint statement.Singapore is gradually opening up its borders and easing domestic curbs with most of its population fully vaccinated.“We have decided to continue hosting the F1 race for another seven years, after thoroughly evaluating the long-term benefits that a term extension could bring to Singapore,” Transport Minister S Iswaran said.“The Singapore F1 race continues to be a strong focal point for tourists, global events and business meetings.”F1 president and chief executive Stefano Domenicali welcomed the extension of Singapore’s hosting.“Singapore holds a special place on the F1 calendar, and this extension is part of our long-term commitment to continue to grow the sport in Asia,” he said.Singapore’s trade and tourism-reliant economy suffered its worst ever recession in 2020 due to the pandemic but rebounded back to growth last year.The race has generated more than S$1.5 billion (RM4.7 billion) in tourism receipts and attracted more than 550,000 international visitors since 2008, according to organisers. — AFP : \n",
            " Positive\n",
            "BQ Blue’s special research section collates quality and in-depth equity and economy research reports from across India’s top brokerages, asset managers and research agencies. These reports offer BloombergQuint’s subscribers an opportunity to expand their understanding of companies, sectors and the economy.Continue Reading. Read more on Research Reports by BloombergQuint. : \n",
            " Positive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic = 'covid'\n",
        "req = requests.get(f'https://newsdata.io/api/1/news?apikey={api_key}&q={topic}&page=4')\n",
        "contents = [res['content'] for res in req.json()['results'] if res['content']]\n",
        "predict(contents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YN9UDxu3S9sO",
        "outputId": "3c50f312-51f6-4890-f136-b59b6eba74f8"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prevalence of Omicron sub-lineage BA.2 is gradually increasing in India, said Sujeet Kumar Singh, Director, National Centre for Disease Control (NCDC), on January 27. Just last week, the UK Health Security Agency flagged the BA.2 sub-variant and designated it as a ‘variant under investigation’. In its latest weekly bulletin, the World Health Organisation noted that a number of countries had reported an increase in the BA.2 sub-variant in the last seven days. Addressing a Health Ministry press briefing in Delhi, Singh said that Omicron sub-variant BA.2 is more prevalent in comparison to the BA.1 variant in India, adding that the BA.3 sub-variant has not been detected in India yet. \"Earlier, the BA.1 variant was dominant among the samples collected from the travellers. Now in community settings, we have found that the BA.2 sub-variant is gradually increasing,\" he said. Talking about increasing cases of Omicron, Singh said that out of the total genome sequencing reports received so far, increasing cases of Omicron have been found in the month of January. Of the total reports received, 1,292 Omicron cases were found in December last year, whereas the number of Delta cases was over 17,000, he said. In January, so far, 9,672 Omicron cases have been found against 4,779 Delta cases. Mainly three states — Maharashtra, Odisha, and West Bengal — have reported the Delta variant on the basis of genome sequencing, said Singh, adding that it does not mean that only the Omicron variant is being reported everywhere. He emphasised that the Delta variant has not gone yet. Unvaccinated higher at-risk Talking about COVID fatalities, he said that the ones who are unvaccinated and people with comorbidities are in the high-risk group. \"Around 64 per cent of those who died in Delhi were from the unvaccinated group with major comorbid population,\" he added. ICMR chief Balram Bhargava said that vaccines have remained beneficial for India. \"Vaccine reduces deaths considerably in the vaccinated population compared to the unvaccinated individuals. Around 95 per cent adult population in the country have received the first vaccine dose, while 74 per cent have been fully vaccinated,\" said Bhargava, as he urged the states lagging behind in vaccination to ramp up the drive. : \n",
            " Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusion\n",
        "\n",
        "- Once again, I've decided to use transfer learning, and used BERT Classification model for sentimental analysis, It was successful and I once again enjoyed using powerful architectures without a need to build them myself. This came in handy and works pretty well.\n",
        "\n",
        "-  I've learnt how to save model weights, as this model cannot be saved with a simple .save() method, and also how to load them, I also included this in my github.\n",
        "\n",
        "- I decided to use an API to get some news and tried sentimental analysis on that content, it works very well from my point of eye.\n",
        "\n",
        "- Training takes a while and I'm interested in making faster, simpler models in the future, as there isn't much motivation left after running training loop for 20 minutes every time I wish to test something new."
      ],
      "metadata": {
        "id": "8vLS-q6qTyvK"
      }
    }
  ]
}