minicurso_pratica_03_tensorflow_80k.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"49fdb0be-4d11-432b-815f-15bca5de9aa9"},"source":["# Minicurso Processamento de Linguagem Natural - Prática 3\n","\n","Autores:\n","* Fernando Sola Pereira\n","* Eduardo Soares de Paiva"],"id":"49fdb0be-4d11-432b-815f-15bca5de9aa9"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8627,"status":"ok","timestamp":1651937934042,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"},"user_tz":180},"id":"f0cf44bb-c3b8-4b80-8904-2e207131096f","outputId":"e89a84c0-d1e5-440a-9e81-d977e687bf92"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K     |████████████████████████████████| 4.0 MB 33.3 MB/s \n","\u001b[K     |████████████████████████████████| 6.6 MB 64.0 MB/s \n","\u001b[K     |████████████████████████████████| 596 kB 65.9 MB/s \n","\u001b[K     |████████████████████████████████| 77 kB 9.2 MB/s \n","\u001b[K     |████████████████████████████████| 880 kB 63.3 MB/s \n","\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}],"source":["!pip -q install transformers"],"id":"f0cf44bb-c3b8-4b80-8904-2e207131096f"},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ng_GxRjsqIDL"},"outputs":[],"source":["##########################################\n","# libs python\n","##########################################\n","import re\n","import time\n","import warnings\n","\n","##########################################\n","# libs externas\n","##########################################\n","from IPython.display import display, HTML, Latex, Markdown\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","import seaborn as sns\n","from sklearn.metrics import classification_report, confusion_matrix\n","from sklearn.metrics.pairwise import cosine_similarity\n","from sklearn.model_selection import train_test_split\n","\n","import tensorflow as tf\n","from tensorflow.keras.layers import Dense\n","\n","from transformers import AutoTokenizer\n","from transformers import TFBertModel\n","\n","##########################################\n","# configurações\n","##########################################\n","warnings.filterwarnings('ignore')\n","sns.set_style(\"whitegrid\")\n","pd.options.display.max_rows = 2000\n","pd.options.display.max_colwidth = 200\n","\n","##########################################\n","# variáveis globais\n","##########################################\n","DEFAULT_RANDOM_STATE = 42"],"id":"Ng_GxRjsqIDL"},{"cell_type":"code","execution_count":null,"metadata":{"id":"cDp6yC6OvLnG"},"outputs":[],"source":["##########################################\n","# dataset \n","##########################################\n","df_lame = pd.read_csv('https://docs.google.com/uc?export=download&id=1_EKfnjomkWks4VqTMIpcEIb6nB5P0Xz2')\n","df_lame.columns = ['label','text']\n","df_lame['label'] = df_lame['label'].apply(lambda x: 1 if x == 'positivo' else 0)\n","\n","# SAMPLE_SIZE = 10000\n","# s_labels = df_lame['label'].value_counts(normalize=True).sort_index()\n","# df_lame = pd.concat([\n","#     df_lame[df_lame['label']==0].sample(int(SAMPLE_SIZE * s_labels[0]), random_state=DEFAULT_RANDOM_STATE), # ~0.427427\n","#     df_lame[df_lame['label']==1].sample(int(SAMPLE_SIZE * s_labels[1]), random_state=DEFAULT_RANDOM_STATE), # ~0.572573\n","# ])"],"id":"cDp6yC6OvLnG"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["950ea8e3d3754c5fb6988cf56da65f2a","0fe17f1dbb474f0497da01f6e78b9ed2","e6a540172a6d44518875da996a80f4ec","baa107910eda47e29aad6a2ac799bc0a","96f5f263e3924cbe965aedc040b88c76","9f044fc535cf4ca59ef0428f077ce27b","414337d7fbd44d97a866ff572acb8370","d2437a9006e843a4a8098e0e906e5cad","afa86e78cf354fc4a365f703f5153512","1666a7073a804e7abf212a0b0fac5188","3a174a496132412095e60b963f93d28b","553c26d73cdd417c922de204162f4e60","0998ae8288bb4d57b8059445a93b8f2b","53438a6b9be24be9801e459b148380df","a34edd3a3ad341eabd21b48e1caa0883","51940eb7bd1243baa9473f4552e3ab63","ab0c39fb1666410489a4c64bd4a36828","1cf24a46bb714962ba2d4d59c8611372","69cc449d61bc40a6b06ef71bb11b1044","934e1464dabc4a54a52ab6099211870f","d13b8b1d8818449c920fdf69a20d8adb","c4a834312cd047aa81da6ef2e446e177","053374f1387e4d628284551af55d9620","6ec525c7c68d4b62b1022daca8bb56f9","d8f87add370e4d8fb85ddf123201e188","26e1d7c507e545d9ab9c26a3bfdd6e89","d77a4d7595b8440caba20aa5678aea27","e7ad7285391347c685b50de3470a60d1","22b78faeaed54dbe8e97359a16f1bc87","732ce15261324785a709d7ef49f1f320","c972e97827144bb3b09cf4d581688db5","71fcbb4546484662848c1d161aaa0bba","5c42036b965841b19d72e6172ac6f65d","98abde78badf4ad7847816cd6f915391","05318a895620444c875e15139b4baf22","146910961b8c4647a3a455ace9eb90e0","355f0828ca0f4182a6ca1f75d5291f40","280a1eb7d5354d3b81afbd4f6ddea0dc","2fcfa7b0ceeb4bc58cc0627fb6002685","289aa4c9f40f4bb1a9cb2f2cb6dcfce2","72b93bcfd7aa4597a467e7bdfd23a9cc","5b4b3a6a1c6f48b386c805b122eff6ab","831a70fc140443f8bd91df07decae578","00f7a61f9286403f981734dd4a2fd5e0","16ee73d3950e48d6ae72088192862075","a10b527b60c7498a9d7c5b2b2db90618","349833af2d7944b59ba7e822452a1d2e","9c12e85729f44fa8af07cdd2cd36eaca","5a277a65e490414d8ae81d621f19ffdd","ceb56613291b4383a52f9824850dfd99","7b037300bd6b4c33b89ff2176dbd3ea1","10138c93ea664cd48aa551dcaaab9ebd","a774db1f91d24ea1aec9b18790fafe8c","06832f98dc244a1abfdda91a69a3c715","870d72488b03460daa6365f07edcb6d5"]},"executionInfo":{"elapsed":44114,"status":"ok","timestamp":1651937986227,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"},"user_tz":180},"id":"q62Z-XglvYzg","outputId":"c718719d-e7d2-446c-f06f-33757d710ed5"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/43.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"950ea8e3d3754c5fb6988cf56da65f2a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/647 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"553c26d73cdd417c922de204162f4e60"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/205k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"053374f1387e4d628284551af55d9620"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"98abde78badf4ad7847816cd6f915391"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"16ee73d3950e48d6ae72088192862075"}},"metadata":{}}],"source":["X_train, X_test, y_train, y_test = train_test_split(df_lame.drop(columns='label'), df_lame['label'], stratify=df_lame['label'], test_size=.3, random_state=DEFAULT_RANDOM_STATE)\n","X_train, X_val, y_train, y_val = train_test_split(df_lame.drop(columns='label'), df_lame['label'], stratify=df_lame['label'], test_size=.2, random_state=DEFAULT_RANDOM_STATE)\n","\n","tokenizer = AutoTokenizer.from_pretrained(\"neuralmind/bert-base-portuguese-cased\")\n","\n","s_dct = X_train[\"text\"].apply(lambda x: tokenizer(x, padding=\"max_length\", truncation=True, max_length=512))\n","X_train['input_ids'] = s_dct.apply(lambda x: x['input_ids'])\n","X_train['token_type_ids'] = s_dct.apply(lambda x: x['token_type_ids'])\n","X_train['attention_mask'] = s_dct.apply(lambda x: x['attention_mask'])\n","\n","s_dct = X_val[\"text\"].apply(lambda x: tokenizer(x, padding=\"max_length\", truncation=True, max_length=512))\n","X_val['input_ids'] = s_dct.apply(lambda x: x['input_ids'])\n","X_val['token_type_ids'] = s_dct.apply(lambda x: x['token_type_ids'])\n","X_val['attention_mask'] = s_dct.apply(lambda x: x['attention_mask'])\n","\n","s_dct = X_test[\"text\"].apply(lambda x: tokenizer(x, padding=\"max_length\", truncation=True, max_length=512))\n","X_test['input_ids'] = s_dct.apply(lambda x: x['input_ids'])\n","X_test['token_type_ids'] = s_dct.apply(lambda x: x['token_type_ids'])\n","X_test['attention_mask'] = s_dct.apply(lambda x: x['attention_mask'])"],"id":"q62Z-XglvYzg"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":510},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1651937986228,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"},"user_tz":180},"id":"dNwBzu6y8Y_Q","outputId":"905241bd-4f79-4c39-bfc7-b0f27dfb9e81"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                                                                                                                                                                          text  \\\n","42913                                                                                                                                       Excelente. Compro semprep este site. Entrega  rápida. Parabéns....   \n","54296                                                                                                          O produto não é bom nem ruim, apenas não é um item que se torne indispensável no meu dia a dia.   \n","81183  Muito bom.. Ficou um pouco pequeno pro meu notebook Samsung 15.6 mas foi erro meu não ter ligo direito as especificações. Mesmo assim é um material que estica um pouco e coube perfeitamente. Muito...   \n","83273                                                                                                                               Atendeu minhas expectativas... super recomendo ! Imagem e som excelentes !   \n","31241                                                                                                                                                  Ele ficou lindo na minha Sala, super funcional, Adorei.   \n","\n","                                                                                                                                                                                                     input_ids  \\\n","42913  [101, 18116, 403, 119, 2174, 157, 1684, 22291, 860, 3834, 119, 1524, 421, 6740, 119, 959, 22295, 13986, 119, 119, 119, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...   \n","54296  [101, 231, 3576, 346, 253, 4062, 2798, 16173, 117, 820, 346, 253, 222, 18685, 179, 176, 745, 22279, 21229, 1050, 202, 7343, 644, 123, 644, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...   \n","81183  [101, 12925, 4062, 119, 119, 13639, 222, 1695, 3265, 258, 7343, 202, 185, 9081, 3021, 2515, 833, 997, 119, 888, 449, 262, 7441, 7343, 346, 370, 1692, 22280, 2368, 260, 17117, 315, 119, 4823, 1016,...   \n","83273  [101, 7354, 2071, 7122, 22281, 15686, 119, 119, 119, 1229, 9099, 22280, 106, 8737, 705, 122, 4081, 19710, 106, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...   \n","31241  [101, 787, 1767, 1863, 243, 229, 7122, 8088, 117, 1229, 8346, 117, 19183, 8393, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...   \n","\n","                                                                                                                                                                                                token_type_ids  \\\n","42913  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","54296  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","81183  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","83273  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","31241  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...   \n","\n","                                                                                                                                                                                                attention_mask  \n","42913  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n","54296  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n","81183  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...  \n","83273  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n","31241  [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  "],"text/html":["\n","  <div id=\"df-11a60a3f-74ee-4dfb-8ef0-0a3620aa9bdf\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>input_ids</th>\n","      <th>token_type_ids</th>\n","      <th>attention_mask</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>42913</th>\n","      <td>Excelente. Compro semprep este site. Entrega  rápida. Parabéns....</td>\n","      <td>[101, 18116, 403, 119, 2174, 157, 1684, 22291, 860, 3834, 119, 1524, 421, 6740, 119, 959, 22295, 13986, 119, 119, 119, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>54296</th>\n","      <td>O produto não é bom nem ruim, apenas não é um item que se torne indispensável no meu dia a dia.</td>\n","      <td>[101, 231, 3576, 346, 253, 4062, 2798, 16173, 117, 820, 346, 253, 222, 18685, 179, 176, 745, 22279, 21229, 1050, 202, 7343, 644, 123, 644, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>81183</th>\n","      <td>Muito bom.. Ficou um pouco pequeno pro meu notebook Samsung 15.6 mas foi erro meu não ter ligo direito as especificações. Mesmo assim é um material que estica um pouco e coube perfeitamente. Muito...</td>\n","      <td>[101, 12925, 4062, 119, 119, 13639, 222, 1695, 3265, 258, 7343, 202, 185, 9081, 3021, 2515, 833, 997, 119, 888, 449, 262, 7441, 7343, 346, 370, 1692, 22280, 2368, 260, 17117, 315, 119, 4823, 1016,...</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...</td>\n","    </tr>\n","    <tr>\n","      <th>83273</th>\n","      <td>Atendeu minhas expectativas... super recomendo ! Imagem e som excelentes !</td>\n","      <td>[101, 7354, 2071, 7122, 22281, 15686, 119, 119, 119, 1229, 9099, 22280, 106, 8737, 705, 122, 4081, 19710, 106, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","    </tr>\n","    <tr>\n","      <th>31241</th>\n","      <td>Ele ficou lindo na minha Sala, super funcional, Adorei.</td>\n","      <td>[101, 787, 1767, 1863, 243, 229, 7122, 8088, 117, 1229, 8346, 117, 19183, 8393, 119, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...</td>\n","      <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11a60a3f-74ee-4dfb-8ef0-0a3620aa9bdf')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-11a60a3f-74ee-4dfb-8ef0-0a3620aa9bdf button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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"]},"metadata":{},"execution_count":5}],"source":["X_train.head()"],"id":"dNwBzu6y8Y_Q"},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":160,"referenced_widgets":["241e2d09b75f4bd4a2979accea81c5c2","5f841eab16174a6789db4b16a3ae22cc","1d70799ca2c24db19207a00eeed91f7f","b21b8bf428ca4e6284de612b484981d4","0bc82af01a834bd3a4499a26a8a25da7","78c40bacf3e34f4097dd930877bed4e0","cb73298850724286bdc0fba04aa5b49c","682979821ae041d6b5215e456d45d41a","d25609e078c84295a39b0dac9ac66781","1c2f20afa27f4efa9c1e8e6caedb93f2","335da3dc52a44df48bb611948b6ce6cc"]},"executionInfo":{"elapsed":15178,"status":"ok","timestamp":1651938001400,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"},"user_tz":180},"id":"3eO1i4pkwXOF","outputId":"a53704f1-9d47-41ce-b6eb-914f867805ac"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/418M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"241e2d09b75f4bd4a2979accea81c5c2"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFBertModel: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight']\n","- This IS expected if you are initializing TFBertModel 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 TFBertModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n","All the weights of TFBertModel were initialized from the PyTorch model.\n","If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"]}],"source":["bert_model = TFBertModel.from_pretrained(\"neuralmind/bert-base-portuguese-cased\", from_pt=True)"],"id":"3eO1i4pkwXOF"},{"cell_type":"code","source":["bert_model."],"metadata":{"id":"QZfHwV5-QXI7"},"id":"QZfHwV5-QXI7","execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"t5Uq--uC98k_"},"outputs":[],"source":["# mostrar a saída do bert"],"id":"t5Uq--uC98k_"},{"cell_type":"code","execution_count":null,"metadata":{"id":"wFrLifyMLLqN"},"outputs":[],"source":["class BertClassifier(tf.keras.Model):\n","    def __init__(self, bert: TFBertModel, num_classes: int):\n","        super().__init__()\n","        self.bert = bert\n","        self.classifier = Dense(num_classes, activation='sigmoid')\n","        \n","    @tf.function\n","    def call(self, input_ids, attention_mask=None):\n","        outputs = self.bert(input_ids, attention_mask=attention_mask)\n","        cls_output = outputs[1]\n","        cls_output = self.classifier(cls_output)\n","        return cls_output\n","\n","model = BertClassifier(bert_model, num_classes=1)"],"id":"wFrLifyMLLqN"},{"cell_type":"code","execution_count":null,"metadata":{"id":"EG_q29BKVRzx"},"outputs":[],"source":["NR_EPOCHS = 3\n","BATCH_SIZE = 16\n","\n","steps_per_epoch = X_train.shape[0] // BATCH_SIZE\n","validation_steps = X_test.shape[0] // BATCH_SIZE\n","\n","# Loss Function\n","loss_object = tf.keras.losses.BinaryCrossentropy()\n","train_loss = tf.keras.metrics.Mean(name='train_loss')\n","validation_loss = tf.keras.metrics.Mean(name='test_loss')\n","\n","# Optimizer\n","total_steps = steps_per_epoch * NR_EPOCHS\n","optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)\n","\n","# Metrics\n","train_auc_metrics = tf.metrics.BinaryAccuracy()\n","validation_auc_metrics = tf.metrics.BinaryAccuracy()"],"id":"EG_q29BKVRzx"},{"cell_type":"code","execution_count":null,"metadata":{"id":"yPnnCLqEVT0v"},"outputs":[],"source":["# def create_dataset(data_tuple, epochs=1, batch_size=1, buffer_size=100, train=True):\n","#     dataset = tf.data.Dataset.from_tensor_slices(data_tuple)\n","#     # if train:\n","#     #     dataset = dataset.shuffle(buffer_size=buffer_size)\n","#     # dataset = dataset.repeat(epochs)\n","#     dataset = dataset.batch(batch_size)\n","#     # if train:\n","#     #     dataset = dataset.prefetch(1)\n","#     return dataset\n","\n","\n","@tf.function\n","def train_step(model, token_ids, masks, labels):\n","  labels = tf.dtypes.cast(labels, tf.float32)\n","\n","  with tf.GradientTape() as tape:\n","    predictions = model(token_ids, attention_mask=masks)\n","    loss = loss_object(labels, predictions)\n","\n","    gradients = tape.gradient(loss, model.trainable_variables)\n","    optimizer.apply_gradients(grads_and_vars=zip(gradients, model.trainable_variables))\n","\n","    train_loss(loss)\n","    train_auc_metrics.update_state(labels, predictions)\n","\n","\n","@tf.function\n","def validation_step(model, token_ids, masks, labels):\n","    labels = tf.dtypes.cast(labels, tf.float32)\n","\n","    predictions = model(token_ids, attention_mask=masks, training=False)\n","    v_loss = loss_object(labels, predictions)\n","\n","    validation_loss(v_loss)\n","    validation_auc_metrics.update_state(labels, predictions)\n","\n","\n","def train(model, train_dataset, val_dataset, train_steps_per_epoch, val_steps_per_epoch, epochs):\n","    for epoch in range(epochs):\n","        print('=' * 50, f\"EPOCH {epoch + 1}\", '=' * 50)\n","\n","        start = time.time()\n","\n","        for i, (token_ids, masks, labels) in enumerate(train_dataset):\n","            train_step(model, token_ids, masks, labels)\n","            if i % 50 == 0:\n","                print(f'Train Step: {i+1}, Loss: {train_loss.result()}, Accuracy {train_auc_metrics.result()}, Rows: {(i+1) * BATCH_SIZE}')\n","        train_auc_metrics.reset_states()\n","        \n","        for i, (token_ids, masks, labels) in enumerate(val_dataset):\n","            validation_step(model, token_ids, masks, labels)\n","\n","        print(f'\\nEpoch {epoch+1}, Validation Loss: {validation_loss.result()}, Validation Accuracy {validation_auc_metrics.result()}, Time: {time.time()-start}\\n')\n","        validation_auc_metrics.reset_states()\n","\n","        print('\\n')\n","\n","\n","train_input_ids = [l for l in X_train[\"input_ids\"].values]\n","train_attention_mask = [l for l in X_train[\"attention_mask\"].values]\n","\n","val_input_ids = [l for l in X_val[\"input_ids\"].values]\n","val_attention_mask = [l for l in X_val[\"attention_mask\"].values]\n","\n","test_input_ids = [l for l in X_test[\"input_ids\"].values]\n","test_attention_mask = [l for l in X_test[\"attention_mask\"].values]"],"id":"yPnnCLqEVT0v"},{"cell_type":"code","execution_count":null,"metadata":{"id":"UhHocxD64-E4"},"outputs":[],"source":["train_dataset = tf.data.Dataset.from_tensor_slices((train_input_ids, train_attention_mask, y_train))\n","train_dataset.shuffle(len(train_dataset))\n","train_dataset = train_dataset.batch(BATCH_SIZE)\n","\n","validation_dataset = tf.data.Dataset.from_tensor_slices((val_input_ids, val_attention_mask, y_val))\n","validation_dataset.shuffle(len(validation_dataset))\n","validation_dataset = validation_dataset.batch(BATCH_SIZE)\n","\n","test_dataset = tf.data.Dataset.from_tensor_slices((test_input_ids, test_attention_mask, y_test))\n","test_dataset.shuffle(len(test_dataset))\n","test_dataset = test_dataset.batch(BATCH_SIZE)\n","\n","# train_dataset = create_dataset((train_input_ids, train_attention_mask, y_train), epochs=NR_EPOCHS, batch_size=BATCH_SIZE)\n","# validation_dataset = create_dataset((test_input_ids, test_attention_mask, y_test), epochs=NR_EPOCHS, batch_size=BATCH_SIZE)\n","\n"],"id":"UhHocxD64-E4"},{"cell_type":"code","execution_count":null,"metadata":{"id":"V2EcZqCmZnYP","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651961062613,"user_tz":180,"elapsed":22826177,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"}},"outputId":"34682fe3-84d4-4a07-de38-2de431240783"},"outputs":[{"output_type":"stream","name":"stdout","text":["================================================== EPOCH 1 ==================================================\n","Train Step: 1, Loss: 0.7177930474281311, Accuracy 0.4375, Rows: 16\n","Train Step: 51, Loss: 0.256509929895401, Accuracy 0.8982843160629272, Rows: 816\n","Train Step: 101, Loss: 0.1849445104598999, Accuracy 0.9319307208061218, Rows: 1616\n","Train Step: 151, Loss: 0.17170076072216034, Accuracy 0.9387417435646057, Rows: 2416\n","Train Step: 201, Loss: 0.16438493132591248, Accuracy 0.9430969953536987, Rows: 3216\n","Train Step: 251, Loss: 0.15565107762813568, Accuracy 0.9454681277275085, Rows: 4016\n","Train Step: 301, Loss: 0.1541113555431366, Accuracy 0.947051465511322, Rows: 4816\n","Train Step: 351, Loss: 0.14687561988830566, Accuracy 0.9496082663536072, Rows: 5616\n","Train Step: 401, Loss: 0.143682062625885, Accuracy 0.9505922794342041, Rows: 6416\n","Train Step: 451, Loss: 0.14266610145568848, Accuracy 0.9513580799102783, Rows: 7216\n","Train Step: 501, Loss: 0.13896913826465607, Accuracy 0.9525948166847229, Rows: 8016\n","Train Step: 551, Loss: 0.13810397684574127, Accuracy 0.9530399441719055, Rows: 8816\n","Train Step: 601, Loss: 0.1361691802740097, Accuracy 0.9540349245071411, Rows: 9616\n","Train Step: 651, Loss: 0.13525959849357605, Accuracy 0.9541090726852417, Rows: 10416\n","Train Step: 701, Loss: 0.13456015288829803, Accuracy 0.9547967314720154, Rows: 11216\n","Train Step: 751, Loss: 0.13370665907859802, Accuracy 0.95514315366745, Rows: 12016\n","Train Step: 801, Loss: 0.13312183320522308, Accuracy 0.9549001455307007, Rows: 12816\n","Train Step: 851, Loss: 0.13296771049499512, Accuracy 0.9549059867858887, Rows: 13616\n","Train Step: 901, Loss: 0.13084599375724792, Accuracy 0.9559516906738281, Rows: 14416\n","Train Step: 951, Loss: 0.12952707707881927, Accuracy 0.956756055355072, Rows: 15216\n","Train Step: 1001, Loss: 0.12858806550502777, Accuracy 0.957230269908905, Rows: 16016\n","Train Step: 1051, Loss: 0.12855227291584015, Accuracy 0.9573620557785034, Rows: 16816\n","Train Step: 1101, Loss: 0.12861071527004242, Accuracy 0.9575953483581543, Rows: 17616\n","Train Step: 1151, Loss: 0.1268264651298523, Accuracy 0.9579713344573975, Rows: 18416\n","Train Step: 1201, Loss: 0.12443661689758301, Accuracy 0.958680272102356, Rows: 19216\n","Train Step: 1251, Loss: 0.12463477998971939, Accuracy 0.9581335186958313, Rows: 20016\n","Train Step: 1301, Loss: 0.12491606175899506, Accuracy 0.9580130577087402, Rows: 20816\n","Train Step: 1351, Loss: 0.12460140883922577, Accuracy 0.9580403566360474, Rows: 21616\n","Train Step: 1401, Loss: 0.12376130372285843, Accuracy 0.9582887291908264, Rows: 22416\n","Train Step: 1451, Loss: 0.12399151176214218, Accuracy 0.9581323266029358, Rows: 23216\n","Train Step: 1501, Loss: 0.12359205633401871, Accuracy 0.9583194255828857, Rows: 24016\n","Train Step: 1551, Loss: 0.12273614853620529, Accuracy 0.9585751295089722, Rows: 24816\n","Train Step: 1601, Loss: 0.12256652116775513, Accuracy 0.9587367177009583, Rows: 25616\n","Train Step: 1651, Loss: 0.12221041321754456, Accuracy 0.9590021371841431, Rows: 26416\n","Train Step: 1701, Loss: 0.12224912643432617, Accuracy 0.9590681791305542, Rows: 27216\n","Train Step: 1751, Loss: 0.1218891441822052, Accuracy 0.9590591192245483, Rows: 28016\n","Train Step: 1801, Loss: 0.12141689658164978, Accuracy 0.9590505361557007, Rows: 28816\n","Train Step: 1851, Loss: 0.12129245698451996, Accuracy 0.9592450261116028, Rows: 29616\n","Train Step: 1901, Loss: 0.12083476036787033, Accuracy 0.9593306183815002, Rows: 30416\n","Train Step: 1951, Loss: 0.12074001878499985, Accuracy 0.9592516422271729, Rows: 31216\n","Train Step: 2001, Loss: 0.12033846229314804, Accuracy 0.9593640565872192, Rows: 32016\n","Train Step: 2051, Loss: 0.11974886804819107, Accuracy 0.959592878818512, Rows: 32816\n","Train Step: 2101, Loss: 0.11968045681715012, Accuracy 0.9596025943756104, Rows: 33616\n","Train Step: 2151, Loss: 0.11946862936019897, Accuracy 0.9598152041435242, Rows: 34416\n","Train Step: 2201, Loss: 0.11858375370502472, Accuracy 0.9602169394493103, Rows: 35216\n","Train Step: 2251, Loss: 0.11924463510513306, Accuracy 0.9600732922554016, Rows: 36016\n","Train Step: 2301, Loss: 0.11887095123529434, Accuracy 0.9602347016334534, Rows: 36816\n","Train Step: 2351, Loss: 0.11827775835990906, Accuracy 0.9603360295295715, Rows: 37616\n","Train Step: 2401, Loss: 0.1185668557882309, Accuracy 0.960016667842865, Rows: 38416\n","Train Step: 2451, Loss: 0.11782914400100708, Accuracy 0.9601438045501709, Rows: 39216\n","Train Step: 2501, Loss: 0.11674121767282486, Accuracy 0.9604908227920532, Rows: 40016\n","Train Step: 2551, Loss: 0.1161414235830307, Accuracy 0.9607017040252686, Rows: 40816\n","Train Step: 2601, Loss: 0.11648616939783096, Accuracy 0.9607362747192383, Rows: 41616\n","Train Step: 2651, Loss: 0.1157551109790802, Accuracy 0.961076021194458, Rows: 42416\n","Train Step: 2701, Loss: 0.11552708595991135, Accuracy 0.9612874984741211, Rows: 43216\n","Train Step: 2751, Loss: 0.1154569610953331, Accuracy 0.961309552192688, Rows: 44016\n","Train Step: 2801, Loss: 0.11528503894805908, Accuracy 0.9614199995994568, Rows: 44816\n","Train Step: 2851, Loss: 0.1154441237449646, Accuracy 0.9613732099533081, Rows: 45616\n","Train Step: 2901, Loss: 0.11490818113088608, Accuracy 0.9615865349769592, Rows: 46416\n","Train Step: 2951, Loss: 0.11458145081996918, Accuracy 0.961601972579956, Rows: 47216\n","Train Step: 3001, Loss: 0.11442452669143677, Accuracy 0.9617419242858887, Rows: 48016\n","Train Step: 3051, Loss: 0.11453691869974136, Accuracy 0.9617953300476074, Rows: 48816\n","Train Step: 3101, Loss: 0.11393316090106964, Accuracy 0.9619678854942322, Rows: 49616\n","Train Step: 3151, Loss: 0.11376933008432388, Accuracy 0.9619565010070801, Rows: 50416\n","Train Step: 3201, Loss: 0.11315107345581055, Accuracy 0.9622383713722229, Rows: 51216\n","Train Step: 3251, Loss: 0.11288434267044067, Accuracy 0.962280809879303, Rows: 52016\n","Train Step: 3301, Loss: 0.11238352954387665, Accuracy 0.9624545574188232, Rows: 52816\n","Train Step: 3351, Loss: 0.11192413419485092, Accuracy 0.962567150592804, Rows: 53616\n","Train Step: 3401, Loss: 0.1114058569073677, Accuracy 0.9627131819725037, Rows: 54416\n","Train Step: 3451, Loss: 0.11118941009044647, Accuracy 0.9627825021743774, Rows: 55216\n","Train Step: 3501, Loss: 0.11150968819856644, Accuracy 0.9627249240875244, Rows: 56016\n","Train Step: 3551, Loss: 0.11124108731746674, Accuracy 0.9627217650413513, Rows: 56816\n","Train Step: 3601, Loss: 0.11090646684169769, Accuracy 0.9626839756965637, Rows: 57616\n","Train Step: 3651, Loss: 0.11076661199331284, Accuracy 0.9627156853675842, Rows: 58416\n","Train Step: 3701, Loss: 0.11079836636781693, Accuracy 0.9627634286880493, Rows: 59216\n","Train Step: 3751, Loss: 0.11034388095140457, Accuracy 0.9630098342895508, Rows: 60016\n","Train Step: 3801, Loss: 0.1103367954492569, Accuracy 0.9629209637641907, Rows: 60816\n","Train Step: 3851, Loss: 0.11068305373191833, Accuracy 0.9628018736839294, Rows: 61616\n","Train Step: 3901, Loss: 0.1103435531258583, Accuracy 0.9628620743751526, Rows: 62416\n","Train Step: 3951, Loss: 0.11028444766998291, Accuracy 0.9628891348838806, Rows: 63216\n","Train Step: 4001, Loss: 0.11022119969129562, Accuracy 0.9630248546600342, Rows: 64016\n","Train Step: 4051, Loss: 0.1101316437125206, Accuracy 0.9629875421524048, Rows: 64816\n","Train Step: 4101, Loss: 0.11001267284154892, Accuracy 0.9629663228988647, Rows: 65616\n","Train Step: 4151, Loss: 0.11000007390975952, Accuracy 0.9629908204078674, Rows: 66416\n","\n","Epoch 1, Validation Loss: 0.08714623749256134, Validation Accuracy 0.9707923531532288, Time: 7616.24135518074\n","\n","\n","\n","================================================== EPOCH 2 ==================================================\n","Train Step: 1, Loss: 0.11000267416238785, Accuracy 1.0, Rows: 16\n","Train Step: 51, Loss: 0.11002054810523987, Accuracy 0.9620097875595093, Rows: 816\n","Train Step: 101, Loss: 0.10962049663066864, Accuracy 0.9702970385551453, Rows: 1616\n","Train Step: 151, Loss: 0.10926274210214615, Accuracy 0.9706125855445862, Rows: 2416\n","Train Step: 201, Loss: 0.10901544243097305, Accuracy 0.9701492786407471, Rows: 3216\n","Train Step: 251, Loss: 0.10864375531673431, Accuracy 0.9713645577430725, Rows: 4016\n","Train Step: 301, Loss: 0.1085529550909996, Accuracy 0.9713455438613892, Rows: 4816\n","Train Step: 351, Loss: 0.10808984935283661, Accuracy 0.9724003076553345, Rows: 5616\n","Train Step: 401, Loss: 0.10773657262325287, Accuracy 0.9717893004417419, Rows: 6416\n","Train Step: 451, Loss: 0.10740453749895096, Accuracy 0.9724224209785461, Rows: 7216\n","Train Step: 501, Loss: 0.10682951658964157, Accuracy 0.973802387714386, Rows: 8016\n","Train Step: 551, Loss: 0.10645336657762527, Accuracy 0.9745916724205017, Rows: 8816\n","Train Step: 601, Loss: 0.10599053651094437, Accuracy 0.9750415682792664, Rows: 9616\n","Train Step: 651, Loss: 0.10577880591154099, Accuracy 0.9746543765068054, Rows: 10416\n","Train Step: 701, Loss: 0.10540088266134262, Accuracy 0.9749464988708496, Rows: 11216\n","Train Step: 751, Loss: 0.10506541281938553, Accuracy 0.9750332832336426, Rows: 12016\n","Train Step: 801, Loss: 0.1049041748046875, Accuracy 0.9744070172309875, Rows: 12816\n","Train Step: 851, Loss: 0.10476180911064148, Accuracy 0.9742949604988098, Rows: 13616\n","Train Step: 901, Loss: 0.10439875721931458, Accuracy 0.9744728207588196, Rows: 14416\n","Train Step: 951, Loss: 0.1040961742401123, Accuracy 0.9746977090835571, Rows: 15216\n","Train Step: 1001, Loss: 0.1038249284029007, Accuracy 0.9747751951217651, Rows: 16016\n","Train Step: 1051, Loss: 0.10346753895282745, Accuracy 0.9749643206596375, Rows: 16816\n","Train Step: 1101, Loss: 0.10324729979038239, Accuracy 0.9752497673034668, Rows: 17616\n","Train Step: 1151, Loss: 0.10273127257823944, Accuracy 0.97556471824646, Rows: 18416\n","Train Step: 1201, Loss: 0.10227788239717484, Accuracy 0.9759575128555298, Rows: 19216\n","Train Step: 1251, Loss: 0.10191557556390762, Accuracy 0.9760192036628723, Rows: 20016\n","Train Step: 1301, Loss: 0.10177628695964813, Accuracy 0.976124107837677, Rows: 20816\n","Train Step: 1351, Loss: 0.10148612409830093, Accuracy 0.9762675762176514, Rows: 21616\n","Train Step: 1401, Loss: 0.10097068548202515, Accuracy 0.9765792489051819, Rows: 22416\n","Train Step: 1451, Loss: 0.10081305354833603, Accuracy 0.9763094186782837, Rows: 23216\n","Train Step: 1501, Loss: 0.10056626796722412, Accuracy 0.9763907194137573, Rows: 24016\n","Train Step: 1551, Loss: 0.10021968185901642, Accuracy 0.9766682982444763, Rows: 24816\n","Train Step: 1601, Loss: 0.10013607144355774, Accuracy 0.9766162037849426, Rows: 25616\n","Train Step: 1651, Loss: 0.09997265040874481, Accuracy 0.9766429662704468, Rows: 26416\n","Train Step: 1701, Loss: 0.09986567497253418, Accuracy 0.9767048954963684, Rows: 27216\n","Train Step: 1751, Loss: 0.09962695091962814, Accuracy 0.9768346548080444, Rows: 28016\n","Train Step: 1801, Loss: 0.09933005273342133, Accuracy 0.9768531322479248, Rows: 28816\n","Train Step: 1851, Loss: 0.09904978424310684, Accuracy 0.9769043922424316, Rows: 29616\n","Train Step: 1901, Loss: 0.09860621392726898, Accuracy 0.9771173000335693, Rows: 30416\n","Train Step: 1951, Loss: 0.09845412522554398, Accuracy 0.9770950675010681, Rows: 31216\n","Train Step: 2001, Loss: 0.09813898801803589, Accuracy 0.9772613644599915, Rows: 32016\n","Train Step: 2051, Loss: 0.09769314527511597, Accuracy 0.9774804711341858, Rows: 32816\n","Train Step: 2101, Loss: 0.09762442857027054, Accuracy 0.9773024916648865, Rows: 33616\n","Train Step: 2151, Loss: 0.09737823158502579, Accuracy 0.9772779941558838, Rows: 34416\n","Train Step: 2201, Loss: 0.09708133339881897, Accuracy 0.9774534106254578, Rows: 35216\n","Train Step: 2251, Loss: 0.0970710813999176, Accuracy 0.9773989319801331, Rows: 36016\n","Train Step: 2301, Loss: 0.09684024751186371, Accuracy 0.9774554371833801, Rows: 36816\n","Train Step: 2351, Loss: 0.09656159579753876, Accuracy 0.9775095582008362, Rows: 37616\n","Train Step: 2401, Loss: 0.09651971608400345, Accuracy 0.9773532152175903, Rows: 38416\n","Train Step: 2451, Loss: 0.09616690129041672, Accuracy 0.9775856733322144, Rows: 39216\n","Train Step: 2501, Loss: 0.09572456777095795, Accuracy 0.9778088927268982, Rows: 40016\n","Train Step: 2551, Loss: 0.09546119719743729, Accuracy 0.9779253005981445, Rows: 40816\n","Train Step: 2601, Loss: 0.0954180359840393, Accuracy 0.9779651761054993, Rows: 41616\n","Train Step: 2651, Loss: 0.09500465542078018, Accuracy 0.9780979156494141, Rows: 42416\n","Train Step: 2701, Loss: 0.09483350813388824, Accuracy 0.9781330823898315, Rows: 43216\n","Train Step: 2751, Loss: 0.09470221400260925, Accuracy 0.9782351851463318, Rows: 44016\n","Train Step: 2801, Loss: 0.0944681465625763, Accuracy 0.978289008140564, Rows: 44816\n","Train Step: 2851, Loss: 0.09433338791131973, Accuracy 0.9782532453536987, Rows: 45616\n","Train Step: 2901, Loss: 0.09409647434949875, Accuracy 0.9783264398574829, Rows: 46416\n","Train Step: 2951, Loss: 0.0937575101852417, Accuracy 0.9784606695175171, Rows: 47216\n","Train Step: 3001, Loss: 0.09372904151678085, Accuracy 0.9784238338470459, Rows: 48016\n","Train Step: 3051, Loss: 0.09360064566135406, Accuracy 0.9783472418785095, Rows: 48816\n","Train Step: 3101, Loss: 0.0932246744632721, Accuracy 0.9784746766090393, Rows: 49616\n","Train Step: 3151, Loss: 0.09312308579683304, Accuracy 0.978340208530426, Rows: 50416\n","Train Step: 3201, Loss: 0.09288682043552399, Accuracy 0.9783661365509033, Rows: 51216\n","Train Step: 3251, Loss: 0.09261638671159744, Accuracy 0.9784489274024963, Rows: 52016\n","Train Step: 3301, Loss: 0.09240454435348511, Accuracy 0.978358805179596, Rows: 52816\n","Train Step: 3351, Loss: 0.09200290590524673, Accuracy 0.9785138964653015, Rows: 53616\n","Train Step: 3401, Loss: 0.09184914082288742, Accuracy 0.9784989953041077, Rows: 54416\n","Train Step: 3451, Loss: 0.09164194762706757, Accuracy 0.9784663915634155, Rows: 55216\n","Train Step: 3501, Loss: 0.09156379848718643, Accuracy 0.9784347414970398, Rows: 56016\n","Train Step: 3551, Loss: 0.09136930108070374, Accuracy 0.9784743785858154, Rows: 56816\n","Train Step: 3601, Loss: 0.09102905541658401, Accuracy 0.9785823225975037, Rows: 57616\n","Train Step: 3651, Loss: 0.090847447514534, Accuracy 0.978550374507904, Rows: 58416\n","Train Step: 3701, Loss: 0.09077602624893188, Accuracy 0.9784517884254456, Rows: 59216\n","Train Step: 3751, Loss: 0.09048230201005936, Accuracy 0.9785890579223633, Rows: 60016\n","Train Step: 3801, Loss: 0.0901886373758316, Accuracy 0.9786240458488464, Rows: 60816\n","Train Step: 3851, Loss: 0.09025193750858307, Accuracy 0.9785283207893372, Rows: 61616\n","Train Step: 3901, Loss: 0.09003451466560364, Accuracy 0.9785631895065308, Rows: 62416\n","Train Step: 3951, Loss: 0.08982468396425247, Accuracy 0.9786288142204285, Rows: 63216\n","Train Step: 4001, Loss: 0.08970325440168381, Accuracy 0.9786928296089172, Rows: 64016\n","Train Step: 4051, Loss: 0.08944912999868393, Accuracy 0.9787397980690002, Rows: 64816\n","Train Step: 4101, Loss: 0.08924754709005356, Accuracy 0.9787704348564148, Rows: 65616\n","Train Step: 4151, Loss: 0.08912646025419235, Accuracy 0.9787701964378357, Rows: 66416\n","\n","Epoch 2, Validation Loss: 0.08691684901714325, Validation Accuracy 0.971210241317749, Time: 7605.981234788895\n","\n","\n","\n","================================================== EPOCH 3 ==================================================\n","Train Step: 1, Loss: 0.08911776542663574, Accuracy 1.0, Rows: 16\n","Train Step: 51, Loss: 0.08895767480134964, Accuracy 0.9779411554336548, Rows: 816\n","Train Step: 101, Loss: 0.08869153261184692, Accuracy 0.9826732873916626, Rows: 1616\n","Train Step: 151, Loss: 0.08842296898365021, Accuracy 0.9834437370300293, Rows: 2416\n","Train Step: 201, Loss: 0.08824373781681061, Accuracy 0.9828979969024658, Rows: 3216\n","Train Step: 251, Loss: 0.0880141481757164, Accuracy 0.9830677509307861, Rows: 4016\n","Train Step: 301, Loss: 0.08790348470211029, Accuracy 0.9829733967781067, Rows: 4816\n","Train Step: 351, Loss: 0.08765887469053268, Accuracy 0.9832621216773987, Rows: 5616\n","Train Step: 401, Loss: 0.08739073574542999, Accuracy 0.9834787845611572, Rows: 6416\n","Train Step: 451, Loss: 0.08722665905952454, Accuracy 0.983370304107666, Rows: 7216\n","Train Step: 501, Loss: 0.0868958830833435, Accuracy 0.9842814207077026, Rows: 8016\n","Train Step: 551, Loss: 0.08667576313018799, Accuracy 0.9842332005500793, Rows: 8816\n","Train Step: 601, Loss: 0.08647886663675308, Accuracy 0.9841930270195007, Rows: 9616\n","Train Step: 651, Loss: 0.08625304698944092, Accuracy 0.9838709831237793, Rows: 10416\n","Train Step: 701, Loss: 0.08608204871416092, Accuracy 0.9840406775474548, Rows: 11216\n","Train Step: 751, Loss: 0.08588993549346924, Accuracy 0.9841877222061157, Rows: 12016\n","Train Step: 801, Loss: 0.08573605865240097, Accuracy 0.9840824007987976, Rows: 12816\n","Train Step: 851, Loss: 0.08556018769741058, Accuracy 0.9843566417694092, Rows: 13616\n","Train Step: 901, Loss: 0.08531510084867477, Accuracy 0.9846004247665405, Rows: 14416\n","Train Step: 951, Loss: 0.08503977209329605, Accuracy 0.9850814938545227, Rows: 15216\n","Train Step: 1001, Loss: 0.08493313938379288, Accuracy 0.9848276972770691, Rows: 16016\n","Train Step: 1051, Loss: 0.08477635681629181, Accuracy 0.9846574664115906, Rows: 16816\n","Train Step: 1101, Loss: 0.08464527875185013, Accuracy 0.984673023223877, Rows: 17616\n","Train Step: 1151, Loss: 0.08438512682914734, Accuracy 0.9847958087921143, Rows: 18416\n","Train Step: 1201, Loss: 0.08411247283220291, Accuracy 0.9848043322563171, Rows: 19216\n","Train Step: 1251, Loss: 0.08392414450645447, Accuracy 0.9847621917724609, Rows: 20016\n","Train Step: 1301, Loss: 0.08377397060394287, Accuracy 0.9847232699394226, Rows: 20816\n","Train Step: 1351, Loss: 0.08357477933168411, Accuracy 0.9848260283470154, Rows: 21616\n","Train Step: 1401, Loss: 0.08339674025774002, Accuracy 0.9846984148025513, Rows: 22416\n","Train Step: 1451, Loss: 0.08317019045352936, Accuracy 0.98483806848526, Rows: 23216\n","Train Step: 1501, Loss: 0.08295362442731857, Accuracy 0.9850099682807922, Rows: 24016\n","Train Step: 1551, Loss: 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0.08103535324335098, Accuracy 0.9852748513221741, Rows: 33616\n","Train Step: 2151, Loss: 0.08087511360645294, Accuracy 0.9853556752204895, Rows: 34416\n","Train Step: 2201, Loss: 0.08067762106657028, Accuracy 0.985489547252655, Rows: 35216\n","Train Step: 2251, Loss: 0.08065842092037201, Accuracy 0.9853675961494446, Rows: 36016\n","Train Step: 2301, Loss: 0.08052308112382889, Accuracy 0.9854410886764526, Rows: 36816\n","Train Step: 2351, Loss: 0.08038239926099777, Accuracy 0.9853253960609436, Rows: 37616\n","Train Step: 2401, Loss: 0.08035701513290405, Accuracy 0.9850843548774719, Rows: 38416\n","Train Step: 2451, Loss: 0.08016075938940048, Accuracy 0.985210120677948, Rows: 39216\n","Train Step: 2501, Loss: 0.07992028445005417, Accuracy 0.9853558540344238, Rows: 40016\n","Train Step: 2551, Loss: 0.07979948818683624, Accuracy 0.985275387763977, Rows: 40816\n","Train Step: 2601, Loss: 0.07980412244796753, Accuracy 0.9851980209350586, Rows: 41616\n","Train Step: 2651, Loss: 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tf.debugging.set_log_device_placement(True)\n","\n","train(model, train_dataset, validation_dataset, train_steps_per_epoch=steps_per_epoch, val_steps_per_epoch=validation_steps, epochs=NR_EPOCHS)"],"id":"V2EcZqCmZnYP"},{"cell_type":"code","execution_count":null,"metadata":{"id":"EAcRYCy6d82c"},"outputs":[],"source":["ds = tf.data.Dataset.from_tensor_slices([1,2,3])\n","ds = ds.shuffle(len(ds))"],"id":"EAcRYCy6d82c"},{"cell_type":"code","execution_count":null,"metadata":{"id":"shpi0pL6ZVnV","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651961062615,"user_tz":180,"elapsed":11,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"}},"outputId":"1f358028-ce41-4fe8-bae4-b17332c8d2e9"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<tf.Tensor: shape=(), dtype=int32, numpy=2>,\n"," <tf.Tensor: shape=(), dtype=int32, numpy=1>,\n"," <tf.Tensor: shape=(), dtype=int32, numpy=3>]"]},"metadata":{},"execution_count":14}],"source":["[d for d in ds]"],"id":"shpi0pL6ZVnV"},{"cell_type":"code","execution_count":null,"metadata":{"id":"h1jApq-cZIIK"},"outputs":[],"source":["all_predictions = []\n","for i, (token_ids, masks, labels) in enumerate(test_dataset):\n","  predictions = model(token_ids, attention_mask=masks, training=False)\n","  all_predictions.extend(predictions)"],"id":"h1jApq-cZIIK"},{"cell_type":"code","execution_count":null,"metadata":{"id":"lHyl3e0YlCFN","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651962058661,"user_tz":180,"elapsed":2490,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"}},"outputId":"61abfce9-528d-4877-f9a7-cd8a34fcdd85"},"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.96      0.98      0.97     10727\n","           1       0.99      0.97      0.98     14387\n","\n","    accuracy                           0.97     25114\n","   macro avg       0.97      0.98      0.97     25114\n","weighted avg       0.98      0.97      0.97     25114\n","\n"]}],"source":["from sklearn.metrics import classification_report\n","\n","print(classification_report(y_test, (np.array(all_predictions)>=0.5).astype(int).reshape(-1)))"],"id":"lHyl3e0YlCFN"},{"cell_type":"code","execution_count":null,"metadata":{"id":"rAI5CySllkHo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651962059913,"user_tz":180,"elapsed":1259,"user":{"displayName":"Fernando Sola Pereira","userId":"07411434982593234995"}},"outputId":"ad7ce164-3103-45fe-d4b4-af225c56a471"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0.01512252]], dtype=float32)"]},"metadata":{},"execution_count":17}],"source":["token_data = tokenizer(\"O produto é interessante mas não parece atender as minhas necessidades.\", padding=\"max_length\", truncation=True, max_length=512)\n","\n","t_input_ids = np.array(token_data['input_ids']).reshape(-1, 512)\n","t_attention_mask = np.array(token_data['attention_mask']).reshape(-1, 512)\n","\n","predictions = model(t_input_ids, attention_mask=t_attention_mask, training=False)\n","predictions.numpy()"],"id":"rAI5CySllkHo"},{"cell_type":"code","execution_count":null,"metadata":{"id":"NbU58TCc0Djo"},"outputs":[],"source":[""],"id":"NbU58TCc0Djo"}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","name":"minicurso_pratica_03_80k.ipynb","provenance":[{"file_id":"1B3beiYViBt_23xGjrGJ9-8XWjkIMxsU5","timestamp":1652107186334},{"file_id":"13XQ_TKByJHZaBrr_AYL4MSNrTvHEzag9","timestamp":1638532417984}]},"kernelspec":{"display_name":"Python 3 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